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PROCEEDINGS
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THERMODYNAMIC AND ENVIRONMENTAL ASSESSMENT OF A WIND
TURBINE SYSTEM
Abbas Alpaslan KOCER1, Yunus Emre YUKSEL2, Murat OZTURK3
1
Uluborlu Selahattin Karasoy Vocational School, Suleyman Demirel University,
32260, Isparta Turkey, alpaslankocer@sdu.edu.tr
2
Department of Science and Education, Education Faculty, Afyon Kocatepe University,
03200, Afyon, Turkey, yeyuksel@aku.edu.tr
3
Department of Mechatronics Engineering, Faculty of Technology, Suleyman Demirel
University, 32260, Isparta Turkey, muratozturk@sdu.edu.tr
Abstract
The wind turbine system is one of the most competitive sources in the field of renewable
energy technologies. In many possible applications, a small power plant based on a renewable
energy can be a good solution under both the environmental and economic point of view.
Vertical axis wind turbine types have an important role in small-scale power development.
This wind power plant would allow the reduction of electric energy consumption from the
grid and the increase of the amount of renewable energy use. The large wind turbine market is
mature and it is the product of several extensive researches. Wind turbine market is being
developed to improve the efficiency, performance, and cost effectiveness of the turbines. The
end goal of this development is to gain a position for wind power as a competitive alternative
to fossil fuels. Among all renewable energy technology of different kinds, wind energy
technology has many advantages such as extensive distribution, high efficiency, low cost, low
maintenance, environmental friendliness, economic improvement and environmental
characteristic that it stands for the most popularized and potentially applicable type of green
energy. In many applications, wind is already competitive with conventional options for
generating electricity. In this paper, thermodynamic analysis consisting of energy and exergy
terminology and environmental impact factors for wind turbine systems are investigated, and
parametric studies for efficiency of wind turbine system are given for different ambient
conditions such as wind speed and huge tower high. The relationship between the actual
energy generated from the wind turbine and the wind speed characteristics are investigated for
sustainability of wind turbine system. Also, important outputs for wind turbine system, such
as maximum relative output useful energy and optimal rotational speed corresponding to
different wind speeds, are estimated to improve the system performance. By multiplying
normalized power by maximum relative output power for the wind turbine system, the
relative output power is calculated.
Keywords: Renewable energy, wind energy, thermodynamic analysis, environmental analysis,
efficiency.
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1. Introduction
It is expected that by 2050 the world energy demand will be significantly increased. In
addition, due to global problems caused by greenhouse gas emissions, the world also needs
low emission and low-carbon energy suppliers to eliminate air pollution. Therefore, a lot of
countries in the world have expressed their views on alternative energy resources, such as
sustainable solar energy, safe nuclear energy, clean hydrogen economy, ground source heat
pumps and geothermal energy, bio-energy, wind energy, hydro-power and wave energy.
Suitable renewable energy policies need to be developed for effective use of available
alternative sources. Wind energy is a prime example of a sustainable energy resource.
Because it is a non-polluting source of energy during power generation, it has no emissions or
residues to burden society.
The dominant type of modern wind turbine is the upwind, horizontal, 3-blade turbine variety,
where the rotor axis is parallel to the wind direction and the blades are arranged perpendicular
into the wind direction [Manwell et al., 2011]. The wind turbine system consists of a
foundation, a tower, nacelle and three rotors attached to a hub. The three blades are attached
to the hub, which is attached to the shaft, which is eventually connected to the generator. The
blades are shaped like aircraft propellers, but are considerably larger, and hollow; an absolute
premium is placed on the strength to weight ratio and flexing properties of the blades [Veers
et al, 2003]. The type of wind turbine selected for installation at the sites examined should be
designed for moderate wind speeds, have as tall a tower is as practical, and as large a blade
diameter as is possible.
Entropy production based on design and exergy analysis of the wind turbine system is shown
to identify the maximum theoretical capability of system performance in power production
applications. Exergy analysis is very useful for improving a wide range of energy conversion
systems. Exergy analysis also provides a design tool for increased accuracy and more efficient
performance.
However, there are few examples in past literature [Sahin et al., 2006(a); Sahin et al. 2006(b);
Ozturk, 2011] that pertain to wind exergy. Through an energy and exergy analysis of the
characteristics of wind energy, it was found that differences between energy and exergy
efficiencies are approximately 20-24% at low wind speeds and approximately 10 - 15% at
high wind speeds [Sahin et al., 2006(a)]. Sahin et al. [Sahin et al., 2006(b)] have developed a
useful exergetic analysis technique for determining the exergetic efficiency of a wind turbine.
The technique utilizes the wind chill temperature associated with wind velocity to predict the
entropy generation of the process. Better turbine design and location selection can be
achieved with the aid of such exergy analysis. Ozturk [2011] have estimated wind power
potential for Turkey, and provided suitable data for evaluating potential wind power
production by using the wind data collected at 23 different wind-monitoring stations in
Turkey. The author has used the energy and exergy analyses of wind power for estimating of
the wind power potential in these areas. Exergy analysis of wind power has been investigated
according to air temperature and pressure at inlet and outlet of wind turbine; energy generated
and heat loss from wind turbine. Also, energy and exergy analyses of wind power and
capacity factor, energy and exergy efficiencies at 10, 25 and 50 m have been calculated for
these wind-monitoring stations.
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In this paper, some results of the application methods of the thermodynamic analysis based on
the second law of thermodynamics for reducing the exergy losses in the wind turbine system
are investigated for better system design. In addition that, some parametric studies for
produced energy, exergy destruction, energy efficiency and exergy efficiency of the wind
turbine system are investigated for varying wind speeds. The other outputs of this paper
should be given as follows;
To develop an advanced Engineering Equation Solver (EES) software code and carry
out parametric studies for wind energy system components.
To calculate the exergy content of the proses including the physical or flow exergy for
the system.
To determine the exergy destruction rate and exergy efficiency of each system
component.
To perform a complete parametric study and the performance assessment of the
system.
2. Thermodynamic Analysis
General thermodynamic assessments involving the energy and exergy balance equations, and
energy and exergy efficiencies are given to analyze wind turbine improvement potentials. In
the most general viewpoint, a balance equation for a given quantity in a process should be
written as follows;
(1)
Eq. (1) is supposed to as the quantity balance for the process, and should be given as
quantity accumulated in a process is equal to the difference between the net quantity transfer
through the system boundary plus the quantity generated and the quantity consumed within
the system boundaries.
2.1 Energy Analysis
Wind turbine blades capture a fraction of the kinetic energy from the air passing the turbine
blades and convert this into electric energy [Manwell et al., 2002]. The mass flow of air
moving past the blades should be given as follows;
(2)
where is density of the air (kg/m3), A is the swept rotor area (m2) and Va is the velocity of
the air flowing past the rotor disk (m/s). The air density changes with both ambient
temperature and altitude, and should be calculated by the specific air gravity viewpoint as
follows;
(3)
where s is the standard air density, and taken as 1.225 kg/m3 for ambient temperature and
pressure at 15 °C and 1 atm, respectively, TR and TS are the absolute reference temperature
(15 °C) and space average air temperature, respectively, PR and PS are the absolute reference
pressure (1 atm) and space average air pressure, respectively. The space average air pressure
varies inversely with height above the sea level, and should be calculated as follows for
altitudes less than 5000m [Manwell et al., 2002];
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(4)
The kinetic energy of flowing air can be calculated as;
(5)
The kinetic energy per unit time through the rotor disk is the power of the air flow, and
should be given as;
(6)
Also, it can be calculated as follows using the Eq. (2);
(7)
This is the basic law that applies to extracting power from moving air. Also, Eq. (7)
should be given in terms of the rotor radius (R).
(8)
Air is a compressible fluid. Therefore, all of the kinetic energy cannot be extracted
from the air passing the turbine blades. The maximum quantity of the produced energy which
can be extracted through a wind turbine system from moving air is represented using the Betz
limit, which has a value of 16/27 or nearly 59.26% [Spera, 1994]. But in practical application,
the best that the most blade designs can achieve is nearly 50%, and this performance usually
changes with wind speeds [Walker and Jenkins, 1997]. Also, this performance indicator is
given as the rotor power coefficient (Cp). Eq. (7) can be written as follows by incorporating
Cp ;
(9)
At high wind speeds, the effect of Cp usually is determined by changing the pitch of
the turbine blades. Therefore, the turbine blades become less performance at converting
moving air into rotary motion. When the wind speeds are too high, the turbine rotation is
stopped by adjusting the blade angles to an aerodynamic braking position. Capacity factor
(CF) can also easily be found as the ratio of the annual average power generated and the rated
power of a turbine as follows;
(10)
where Ereal and Erated are total energy generated and maximum annual rated energy by the
wind farm, respectively, and they can be given as follows;
(11)
and
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(12)
where Prated is maximum rated power of a turbine, Nturbine is number of turbines in the farm
and ttotal is number of operational hours in a year.
2.2 Exergy Analysis
Exergy analysis based on the second law of the thermodynamics can support to create the
strategies and managements for more economical and effective use of energy sources, and is
utilized to study wind energy system. The sources of the irreversibility and proposed
approaches to increase the whole efficiency of the given system should be considered by
using exergy analysis. Exergy content of matter is generally divided into four parts which are
physical exergy (exph), chemical exergy (exch), kinetic exergy (exk) and potential exergy (exp).
Therefore, general exergy balance equation per unit mass should be given as follows;
(13)
where exke, expe, exph and exch are the kinetic, potential, physical and chemical exergy,
respectively. In this paper, kinetic, potential and chemical exergies are negligible, as the
elevation differences are low, speeds in the process are small and there is no chemical
reaction. In general, physical exergy is represented the maximum effective work available as a
process interacts with the environment. Any substance of which the temperature, pressure or
composition is different from the thermodynamic equilibrium with the surroundings (thermal,
mechanical and chemical) has the possibility to produce a change. The physical exergy or
general flow exergy of the
flow is given as;
(14)
where subscripts i and o show the ith flow rate and reference condition flow rate, respectively,
h is the specific enthalpy and s is the specific entropy, respectively. Enthalpy difference can
be given as follows;
(15)
where Cp,a is the air specific heat in (kJ/kgK), T1 and T2 are the wind chill temperature at the
input and output to the wind turbine blades, respectively. Wind chill temperature for the
turbine can be given as follows [Nelson et al., 2002];
(16)
where Ti,wind-ct is the wind chill temperature °C and V is the wind speed in km/h at 10 m
elevation from the ground level. Output wind speed (V2) should be calculated as follows
[Abed, 1994];
(17)
The entropy changes for the wind turbine system are consisting of the total entropy of
the system and surround entropy difference [Szargut, et al., 1988].
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(18)
or
(19)
Heat losses per unit mass from the rotor blade of the turbine system should be given as
follows;
(20)
From Eq. (15), the total exergy of wind power can be expressed as;
(21)
Total exergy of the wind turbine system should be written as follows;
(22)
Also, exergy destruction rate for the wind turbine system can be given as;
(23)
2.3 Thermodynamic Efficiencies
The thermodynamic efficiencies of the design system should be considered through the first
law of thermodynamics (energy efficiency) and both the first and second laws of
thermodynamics (exergy efficiency). Energy and exergy efficiencies of the system
components and whole system should be given for detailed thermodynamic analysis.
The energy efficiency ( ) of the system should be given as the ratio of useful energy
produced by the system to the total energy input. The useful produced energy represents the
desired results produced by the system components. The energy efficiency for the single
production should be written as follows:
(24)
The exergy efficiency ( ) of the process should be defined as the divided of exergy
output rate (
) that is created by the considered system to the overall exergy inlet
rate (
) that is cross the boundaries of the system, as follows;
(25)
The exergy efficiency for the process should also be given in terms of exergy
destruction rate as the following;
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(26)
Using the derived equations, a model is generated by using Engineering Equation
Solver (EES). With the help of this model, the system performance and emissions are
investigated by varying a series of input conditions.
3. Results and discussion
The main production characteristics of the wind turbine system are given in Table 1. In this
paper, the ambient temperature and pressure are given as 25 °C and 1 atm, respectively. For
the useful energy production from the chosen wind turbine system, wind speeds from 10 m to
50 m above the ground level are from 5 to 12 m/s. Higher wind speeds create axial forces
which are liable to damage the wheel, the transmission and support of the machine. This is
very important to take special protective measures in the design of the wind wheel sub-system.
Table 1. ENERCON E-40/6.44 product specifications
Model
Rated Power
Hub height
Rotor
Diameter
Type
Direction of rotation
Number of blades
Rotor swept areas
Blade materials
Rotor speed
Generator
Generator
Braking system
Cut-in speed
Rated wind speed
Wind Turbine Product Specifications
ENERCON E-40/6.44
600 kW
46 m on tabular steel tower
44 m
Upwind
Clockwise
3
1662 m2
Fiberglass (reinforced epoxy) with light protection
Variable, 18-34 rpm
Gearless-no oil required
3 independent systems with emergency supply
Rotor brake
Rotor lock for service and maintenance
2.5 m/s
13.0 m/s
Thermodynamic analysis and environmental assessment of the wind turbine system are very
important in terms of the view for sustainable developments. To achieve for these aims,
measured and calculated parameters according to the average values of the wind turbine
system are shown in Table 2. These design parameters are measured wind speed (ms-1),
measured time in a year (hyear-1), wind speed percent (%), capacity factor (Cp), mass flow rate
(kgs-1), available power (kW), useful power (kW) and produced power (kWh/year). For this
paper, using the energy analysis which given in this paper, available power and useful power
for the wind turbine system are calculated as 1364 and 1603 kW respectively for 11.5 ms-1
wind speed. But, produced maximum power from the wind turbine system is calculated as
47739 kWh/year at 6.5 ms-1 wind speed. The maximum and minimum capacity factors of the
wind turbine system are obtained as 0.35 for 7.5 ms-1 and 0.18 for 2.5 ms-1 wind speed,
respectively. This indicates that capacity factor is depended on the measured time in a year
and wind speed. Also, high mass flow rate for the wind system produces high available power
and useful power, respectively. The electricity produced is zero below the 2.5 ms-1 cut-in
wind velocity.
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Table 2. Measured and calculated parameters for the wind turbine system
Measured
wind
speed
Vr
(ms-1)
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
10.5
11.5
Measured
time in a
year ttotal
(hyear-1)
Wind
speed
percent
(%)
Capacity
factor
Cp
Mass
flow
rate
(kgs-1)
Available
power Pa
(kW)
Useful
power
Pu
(kW)
Produced
power
Po
(kWh/year)
4028
1202
1674
1023
637
104
72
11
7
2
45.99
13.73
19.10
11.68
7.28
1.18
0.83
0.12
0.07
0.02
0.18
0.21
0.28
0.30
0.32
0.35
0.33
0.30
0.25
0.21
4.486
6.28
8.074
9.868
11.66
13.46
15.25
17.05
18.84
20.63
14.02
38.46
81.75
149.3
246.4
378.5
550.9
769.2
1039
1364
16.24
45.2
99.08
182.4
303.4
471.3
681
939.8
1243
1603
0
9229
36425
43544
47739
13096
12444
2413
1728
544.7
8000
0.35
7000
0.3
6000
0.25
5000
0.2
4000
0.15
Poutput (kW)
3000
hWT (%)
2000
1000
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
10.5
h (%)
Poutput (kW)
The results of the energy and exergy analysis, including the energy and exergy
efficiency, and exergy destruction rate for the wind turbine system are reported. It is shown
that, the inlet and outlet exergy flows of the system are mainly attributed to the wind speeds.
Produced energy (kW) and energy efficiency of the wind turbine system based on the
measured wind speeds are given in Figure 1. It is seen that, produced energy and energy
efficiency for the system changes between 1364 to 7729 kW and 0 to 26.72%, respectively, at
different measured wind speed, and maximum energy efficiency is obtained for 6.5 ms-1 wind
speed, considering inlet and outlet pressure differences for the system.
0.1
0.05
0
11.5
v r (ms -1)
Figure 1. Variations with wind turbine of the produced energy (kW) and energy efficiency for
the wind turbine
Exergy analysis of the system has an important role in evaluating wind energy technology.
Also, the exergy destruction rate is another important matter to be emphasized because the
study of irreversibility can help to identify where the work or energy lost during the operation.
Various factors identified in the design section can influence both the energy and exergy
efficiencies of the wind turbine system.
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Exergy destruction rate (kW) and exergy efficiency of the wind turbine system based on the
measured wind speeds are given in Figure 2. Increasing of the measured wind velocity
decreases the energy and exergy efficiencies because the net energy inlet to the wind turbine
increases. Furthermore, maximum produced energy and exergy destruction rate are produced
as 272.4 and 1331 kW, respectively, at 11.5 ms-1 wind speed. Higher exergy destruction refers
high inefficiencies or irreversibilities occur in this system. Exergy efficiency of the system is
more meaningful than the energy efficiency since it gives right magnitudes for the losses to be
determined. It is suggested that exergy efficiency should be used for wind energy evaluations
and assessments, so as to allow for more realistic modeling. In the general manner, the exergy
efficiency of the wind turbine system is based on the turbine type, such as horizontal or
vertical, rotor radius hub height and local wind speed.
8000
0.3
0.2
y WT (%)
ExD-WT (kW)
6000
4000
ExD-WT (kW)
2000
0
2.5
y
3.5
4.5
5.5
6.5
7.5
8.5
WT
0.1
(%)
9.5
10.5
0
11.5
v r (ms -1)
Figure 2. Variations with wind turbine of the exergy destruction rate (kW) and exergy
efficiency for the wind turbine
4. Conclusions
Wind turbine system can make significant supports for energy production due to their
potential for high efficiency as well as low operating costs and greenhouse gas and pollution
emissions. Environmental problems such as fossil fuel depletion and climate change upgrade
the advantages and significance of wind turbine system performance. In this study, energy and
exergy analyses are carried out for the wind turbine system to evaluate the system
performance and exergy destruction rates. Exergy analysis of a wind turbine system is given
based on the thermodynamic quantities, such as enthalpy and entropy productions. Therefore,
exergy analysis has a significant role in evaluating wind turbine system. The differences
between the energy and exergy efficiency is very important for analyzing of the energy
conversation. It is suggested that, exergy efficiencies can be used for wind energy evaluation
and assessment, so as to allow for more realistic modeling, evaluation and planning for wind
turbine system. Exergetic assessments of wind power system provide more meaningful and
useful data than energetic assessments for engineers and wind energy companies before
making decisions.
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5. References
Abed, K. A. (1994). Variable Speed Operation of Small Scale Wind Turbines. World Renewable Energy
Congress, Reading University, UK.
Manwell, J., Burton, T., Sharpe, D., Jenkins, N., Bossanyi, E. (2011). Wind Energy Handbook. Second Edition,
John Wiley & Sons.
Manwell, J., McGowan J., & Rogers, A. (2002). Wind Energy Explained. John Wiley & Sons.
Nelson, C. A., Tew, M., Phetteplace, G.E., Schwerdt, R., Maarouf, A., Osczevski, R., Bluestein, M., Shaykewich,
J., Smarsh, D., Derby, J.C, Petty, R.C., Berger, M., Quayle, R.G, Santee, W.R., O’Lenic, E., Lupo, A.R., &
Browne, K. (2002). Review of the Federal Interagency Process Used to Select the New Wind Chill Temperature
(WCT) Index. Preprints, 18th International Conference on Interactive Information and Processing Systems (IIPS)
for Meteorology, Oceanography, and Hydrology, Orlando.
Sahin, A. D., Dincer, I., & Rosen, M. A. (2006a). Development of new spatio-temporal wind exergy maps.
Proceedings of ASME2006 Mechanical Engineering Congress and Exposition. November 5-10, Chicago, Illinois,
USA.
Sahin, A. D., Dincer, I., & Rosen, M. A. (2006b). Thermodynamic analysis of wind energy. International
Journal of Energy Research. 30, 553-566.
Spera, A. D. (2009). Wind Turbine Technology: Fundamental Concepts in Wind Turbine Engineering. Second
Edition, ASME Press.
Szargut, J., Morris, D. R., & Steward, F. R. (1988). Exergy Analysis of Thermal, Chemical and Metallurgical
Process. Hemisphere publishing corporation.
Ozturk, M. (2011). Energy and exergy assessments for potential wind power in Turkey. International Journal of
Exergy. 8(2), 211-226.
Veers, P., Ashwill, T., Sutherland, H., Laird, D., Lobitz, D., Griffin, D., Mandell, J., Musial, W., Jackson, K.,
Zuteck, M., Miravette, A., Tsai, S., & Richmond, J. (2003). Trends in the design, manufacture and evaluation of
wind turbine blades. Wind Energy, 6, 245-259.
Walker, J. F., & Jenkins, N. (1997). Wind Energy Technology. John Wiley & Sons.
228 | P a g e
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Title
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THERMODYNAMIC AND ENVIRONMENTAL ASSESSMENT OF A WIND TURBINE SYSTEM
Author
Author
OZTURK, Murat
YUKSEL, Yunus Emre
KOCER, Abbas Alpaslan
Abstract
A summary of the resource.
The wind turbine system is one of the most competitive sources in the field of renewable energy technologies. In many possible applications, a small power plant based on a renewable energy can be a good solution under both the environmental and economic point of view. Vertical axis wind turbine types have an important role in small-scale power development. This wind power plant would allow the reduction of electric energy consumption from the grid and the increase of the amount of renewable energy use. The large wind turbine market is mature and it is the product of several extensive researches. Wind turbine market is being developed to improve the efficiency, performance, and cost effectiveness of the turbines. The end goal of this development is to gain a position for wind power as a competitive alternative to fossil fuels. Among all renewable energy technology of different kinds, wind energy technology has many advantages such as extensive distribution, high efficiency, low cost, low maintenance, environmental friendliness, economic improvement and environmental characteristic that it stands for the most popularized and potentially applicable type of green energy. In many applications, wind is already competitive with conventional options for generating electricity. In this paper, thermodynamic analysis consisting of energy and exergy terminology and environmental impact factors for wind turbine systems are investigated, and parametric studies for efficiency of wind turbine system are given for different ambient conditions such as wind speed and huge tower high. The relationship between the actual energy generated from the wind turbine and the wind speed characteristics are investigated for sustainability of wind turbine system. Also, important outputs for wind turbine system, such as maximum relative output useful energy and optimal rotational speed corresponding to different wind speeds, are estimated to improve the system performance. By multiplying normalized power by maximum relative output power for the wind turbine system, the relative output power is calculated. Keywords: Renewable energy, wind energy, thermodynamic analysis, environmental analysis, efficiency.
Publisher
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International Burch University
Date
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2014-05-15
Keywords
Keywords.
Article
PeerReviewed
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ISSN 978-9958-834-36-3
Q Science (General),QH301 Biology,QH426 Genetics
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https://omeka.ibu.edu.ba/files/original/2085a3409cb317a506f7d56022c718fe.pdf
c3a0b2bb1ea871c130e61290ae61db9b
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Text
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REAL-TIME FACE RECOGNITION WITH ARTIFICIAL NEURAL NETWORK
TRAINED BY PARTICLE SWARM OPTIMIZATION
Musa Peker1, Huseyin Guruler2*
1
Department of Information Technologies, Samandira Vocational and Technical High School,
Istanbul, Turkey
2
Department of Information Systems Engineering, Faculty of Technology, Mugla Sitki
Kocaman University, Mugla, Turkey
*Corresponding Author
pekermusa@gmail.com, hguruler@mu.edu.tr
ABSTRACT
Face recognition is one of the widely used biometric method. Verification and recognition of
individuals is possible via the features obtained from desired face image and compared with
the facial image by various methods. Automatic face recognition which is a fundamental
research area in the scope of pattern recognition, is applied in many civil, military and
commercial areas for the purpose of authentication and identification. In this study a real-time
face recognition system was developed. It is aimed that identification of individuals who
entering any field observed with a camera. After detecting the important facial points, they are
presented as input data to feed-forward neural network. Particle swarm optimization was used
as learning algorithm in the network. As a result, a novel real-time face detection method,
which provide high accuracy has been developed.
Keywords: Real time face detection, pattern recognition, neural network, particle swarm
optimization.
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INTRODUCTION
Face detection and face recognition on images is one of the important subjects in computer
vision applications. Previously saved images of each person are used in face recognition of
persons. Faces to be recognized can be obtained from the images obtained in controlled
conditions (images on passport, credit cards, ID, etc.), may also be obtained from real time
video recordings (Peker & Zengin, 2010). Face recognition problem contains finding the faces
in images, determining of limits, finding of attributes and classification of faces using
attributes (Peker & Zengin, 2011).
In recent years, the issue of face recognition systems has been investigated from many aspects.
Variety of techniques for different aspects and details of the subject and efficient algorithms
and methods have been proposed (Turk & Pentland, 1991; Swets & Weng, 1996; Chen et al.,
2000, Yu & Yang, 2001).
Face recognition is a difficult process. Although numerous applications related to facial
recognition has been developed, studies are ongoing. The reason for this, factors such as 3D
exposure differences, different facial expressions, lighting differences, makeup, hair style,
background differences and noise make face recognition quite difficult.
The purpose of this study is to accurately perform face detection and face recognition process
as real time. A new approach is presented in this context. After face recognition, important
attributes of face on images are determined automatically by Gabor wavelet transform. These
attributes are presented as an input to the neural network trained with particle swarm
algorithm. As a result of the operation of neural network, the owner of the face is determined.
The most important feature of the study is the ability to find and recognize multiple faces
simultaneously. The results were obtained are promising.
METHODS
a. Skin color identification algorithm
In this study, skin color based two algorithms were used in order to ideally detect human face
region. These algorithms are
and
code techniques. Because red and green color
tones are more in skin color according to RGB code technique, the following equations are
used.
;
(1)
With using RGB code technique, different skin colors were examined and the most
appropriate value range was attempted to be determined in order algorithm to detect skin
color region ideally. Accordingly, (
) and (
) ranges were determined
as the optimum value ranges to detect skin color regions.
According to
code technique, in
color space
represents the brightness
information,
and
represent color information. Thus, the brightness information is easily
obtained. RGB color space can be converted into
color space by equation (2).
(2)
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and values are used in skin color region finding process. By calculating the maximum
and minimum values of
and components, the pixels between these values are marked as
skin color. Minimum and maximum values of
and components are calculated as shown
in equation (3) (Kurt et al., 2007).
(3)
Minimum and maximum values of
and
components are determined according to the
average value and standard deviation of these components (Kurt et al., 2007).
b. Gabor Wavelet Transform (GWT)
Wavelet transform with Gabor main function is expressed as Gabor Wavelet Transform.
Gabor wavelets show great similarities with the human visual system according to the
frequency and orientation characteristics. These wavelets are used in computer vision
applications, face recognition, fingerprint recognition and classification algorithms (Acar &
Özerdem, 2012). Gabor wavelets constitute an excellent filter for both spatial localization and
orientation. A complex Gabor wavelet (filter) is defined as multiplication of a complex
sinusoid with Gaussian kernel. A two-dimensional Gabor wavelet transform is expressed by
convolution of the image of I(x,y) (Acar & Özerdem, 2012; Buciu & Gacsadi, 2009):
(4)
function represents Gabor filter:
(5)
(6)
(7)
The above-described parameters and , represent the wavelength factor of the cosine (scale)
and the direction of Gabor function (angular orientation), respectively. indicates offset
value of phase, and indicates spatial visual angle.
Parameters calculated from GWT (Acar & Özerdem, 2012):
Assume that the matrix obtained from each GWT matrix regarding gray tone images is an
-dimensional
matrix. Accordingly:
Mean :
(8)
Standard deviation :
(9)
Entropy :
(10)
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c. Artificial Neural Network
Artificial neural networks (ANN) are mathematical systems consisting of many processing
units weighted and connected to each other (Sen & Peker, 2013). This processing unit
receives signals from other neurons; combines them, transforms, and reveals a numerical
result. In general, the processing units roughly correspond to the actual neurons and
interconnected in a network; this structure constitutes neural networks. In this study, feed
forward neural network of the neural network models was used. There are basically three
different layers in feed forward neural networks. These layers, respectively; the input layer
that holds data going into artificial neural network, hidden layer or layers on which processes
are done and train itself according to desired result, and finally output layer which shows
output values.
d. Particle Swarm Optimization
In particle swarm optimization, each solution is called as particle in the search space. All the
particles have relevancy value evaluated by the relevancy function to be optimized and
particle velocity information directing their movements. Particles follow the existing optimum
particles in the problem space (Bakbak & Peker, 2013).
PSO is initialized with random particle swarm and the optimum value is searched with update.
In each iteration, each particle is updated according to the best two values. One of them is the
best relevancy value found by the particle so far called pbest. This value is kept in memory
for later use. Second best value is the best relevancy value found by any particle in swarm so
far called gbest. It is the best global value in the swarm (Yalcin et al., 2013).
Figure 1. The velocity and position updating of a particle at kth generation (Yalcin et al.,
2013).
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Swarm matrix with D swarn dimension and n particle size is described as follows.
x11
x
21
x x31
...
xn1
x12 ... x1D
x22 ... x2 D
x32 ... x3 D
... ... ...
xn 2 ... xnD nxD
(11)
According to the swarm matrix ith particle is described with
xi xi1 , xi 2 , xi3 , , , , xiD
and the
(12)
, best relevancy value found by the particle so far, is
pbesti pi1 , pi 2 , pi3 , , , , piD
global good within the population
gbest p1 , p2 , p3 , , , , p D
(13)
(14)
ith is described as a velocity vector indicating the amount of change in each position of
the particle.
vi vi1 , vi 2 , vi 3 , , , , viD
(15)
Particle’s velocity and position is updated according to the following equations, respectively.
vik 1 vik c1.rand1k .( pbestik xik ) c2 .rand2k .( gbestik xik )
xik 1 xik vik 1
(16)
Where is the number of iterations and is the number of particles. If the particle swarm
matrix consists of rows, it means that . line is being mentioned. and values which are
the learning factors, pull the particle to
and
values.
and usually selected as
equal and in [0,4] range. allows particle to move according to the particle’s own experience,
allows particle to move according to the experience of other particles in the swarm.
APPLICATION AND EVALUATION
e. Face Detection
The biggest problem encountered in face detection is the existence of the areas with a color
close to skin color outside the human face area (Ikizler & Duygulu, 2005). System detects
these areas as a part of the face. After converted to gray level, image is converted to blackand-white picture in order to provide a fast and accurate work on color image. Equation (17)
was used in the conversion of the image to gray level.
(17)
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Thresholding method was used to convert the image to black and white picture. According to
this method; pixels of skin area determined by skin color algorithms are transformed into
white color, while other areas are transformed to black. Thus, the image is transformed into
binary level. In binary system, 0 refers to black while 1 refers to the white color. Binary
format of the image is seen on Figure 2.a. After the image is converted to binary format, the
image is filtered and ensured that image is not affected by unnecessary noises. (Figure 2). As
a filter, median filter, which aims to soften the image, was used as a 3x3 matrix. Median filter
is a nonlinear filter that protects the edges and eliminates random noises (Umbaugh, 1998).
As shown in Figure 2.b, although picture was filtered, noise cleaning cannot be fully ensured.
Therefore, the image was passed through a scanning filter which has 3x3 matrix. This filter
determines the area with maximum skin color by scanning the display screen.
Figure 2. (a) Picture with noise (b) Noise-free picture
As stated earlier, in this study
and
code techniques, which are skin color based
algorithms, were used. In this study, results of algorithms using these code techniques were
compared and it was found that the algorithm in which
code technique is used
provided more successful outcome. The reason for this, the algorithm in which
code
technique is used, is less affected from the factors such as ambient brightness, dust, etc. In
Figure 3, the difference between these two algorithms is seen more clearly.
Figure 3. (a) detection of the areas with skin color using
code technique (b)
detection of the areas with skin color using
code technique.
f. Feature Extraction
Gabor based feature vector related to an image was obtained with statistical values of each
of the 8 wavelet matrix (2 scales and 4 orientations) related to an image. These values are
combined in a vector and feature vector is obtained. From each wavelet transform matrix,
standard deviation, mean and entropy values were calculated respectively. As a result,
statistical values of 8 wavelet transform matrix related to each image were calculated and
feature vector with 8x3 = 24 data length in total was obtained by adding these values
consecutively. The resulting attributes were applied to the input of the classifier.
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g. Face Recognition with Neural Networks Trained by PSO
For the realization of learning in artificial neural networks, weight values between the layers
must be appropriately updated. In this study, unlike classical training algorithms, the PSO, a
powerful optimization algorithm was preferred. In figure 4, a flow chart in which testing and
training of ANN with PSO took place is presented. In the learning phase, primarily, the
weights that hold the numerical value of connections between layers, take random values.
These weight values represent particle values for PSO. Number of connections between the
layers denotes the size of particles (Yalcin et al., 2013).
Network is established according to each particle and training examples are respectively sent
to the network. After the example is presented to the network, the difference (error) between
the actual value should be and the value obtained as output is calculated. After all the samples
submitted to the network, total error (MSE) is calculated and the obtained value is regarded as
the particle's relevancy value. In the first step, this relevancy value is assigned as pbest value
of the particle; the best relevancy value among the particles is assigned as the gbest value
(Yalcin et al., 2013). If relevancy value (error) is not in an acceptable level, particles are
updated with pbest and gbest values. Network is re-established according to the new particle
values, examples are given to the network again and the relevancy value calculation is
performed. These processes continue until the best relevancy value obtained so far (gbest)
reaches to the desired value or the maximum iteration (Yalcin et al., 2013).
If the error in an acceptable level, the testing process begins. This time, network is established
according to the gbest particle values. Test samples are sent respectively to the input layer of
the network and the resulting values is given as output of the example. If any threshold is not
applied to the output of the network, last obtained gbest value gives the classification
performance of the network (Yalcin et al., 2013; Yalcin, 2012).
Figure 4. Flowchart for training and testing of PSONN
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24 attribute values obtained in feature extraction step are presented as an input to the neural
network structure. With PSO algorithm, network parameters are updated and training of
network is carried out. Example of test result is presented in Figure 5. When the figure is
considered, it is seen that the amount of error is decreased depending on the increase in the
value of iteration.
Test
0
MSE
10
-1
10
-2
10
0
2000
4000
6000
8000
10000
Iteration
Figure 5. Iteration-error graph
The face detection performance of the developed software was investigated under various
conditions. Primarily, the success of the application was evaluated as real-time. There is no
change in the success of face recognition in case of an increase in the number of faces in the
image. System is able to find more than one face within the field of view of the camera with
success. Considering the results of the analyses presented in Table 1, the success of the face
recognition process is increase when the image taken frontally and in adequate light
conditions. This is observed that performance is decreased when viewing angle move away
from the front. Face recognition performance of the system decreases in low light conditions.
In order to solve this problem, using of quality and multiple cameras and providing of
sufficient light conditions are proposed.
Table 1. Performance analysis of face detection under different conditions.
Light Condition
Sufficient
Sufficient
Sufficient
Insufficient
Insufficient
Insufficient
Angle
From the front line
From the front line
30 degrees
From the front line
30 degrees
90 degrees
Distance
1,5 m
2m
1,5 m
1,5 m
1,5 m
1,5 m
Success Level
High
High
High
Medium
Medium
Medium
Face recognition experiments were conducted in two stages. In the first stage, experiments
were performed real-timely. In the second stage, experiments were performed on ORL
database (The Database of Faces, 2014).
Experiment 1: Experiments have been tried on 150 people. In experiments conducted by one
person, number of faces recognized as incorrect was two. In this case, success rate was found
as % 98.6. In multi-person applications, depending on the number of different person (2, 3, 4
and 5), number of faces recognized as incorrect was six. In this case, success rate was found
as % 96. The results have shown in Table 2.
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Table 2. Performance analysis for 150 people with the recommended method (real time
application).
Number of people
One person
Multi person (2, 3, 4 and 5)
The number of faces
150
150
Wrong number
2
6
Success rate
98.6 %
96 %
Figure 6 presents an example of the real-time implementation of this application.
(a)
(b)
(c)
(d)
Figure 6. Real-time face recognition and detection
a) Three people b) Four people c) Five people d) Six people
Experiment 2: Table 3 shows the analysis of the success of face recognition carried out in
different conditions. ORL database were used in performance analysis. Analyzes of success
were performed by changing the total number of images, size of the images and the number of
images received from each person. An image set consisting of 300 people was created with 10
different pictures (taken from various fronts) of 30 different people. A database was created
with 30 people by taking one picture of each person in the cluster. Using the remaining 270
pictures, face recognition was performed in different image sizes. Then, performance analyses
were carried out by taking 2, 3 and 5, different image of each person, respectively.
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Table 3. Performance analysis for 300 people with the recommended method.
Number of image
270
270
270
240
240
240
210
210
210
150
150
150
Number of images
per person
1
1
1
2
2
2
3
3
3
5
5
5
Database image
number
30
30
30
60
60
60
90
90
90
150
150
150
Image size
16x14
27x24
40x30
16x14
27x24
40x30
16x14
27x24
40x30
16x14
27x24
40x30
Accuracy rate
(%)
76.5
77.3
78.4
86
86.7
88.5
90
91
94
97
97.5
98.5
In the obtained analyses, it was observed that as the number of samples taken from the same
individual increased, performance of the system also increased. It was also observed that
when the image size is increased, performance slightly increased but, face recognition process
took a long time.
RESULTS
Face detection and analysis are often used in different fields. Face recognition is used in a
wide range such as defense industry, security systems, robotics industry, for commercial
purposes.
Face recognition problem is one of the up-to-date, important and difficult problems. Many
scientists have been working on this issue for a long time. However, due to the difficulty of
the problem, face recognition systems which have the success for solving real-life problems
have not been developed yet.
In this study, software which performs real time face recognition and face detection has been
developed. After face detection was performed in images, important features of the face were
detected. Gabor wavelet transform was preferred for attribute determination. Obtained
attributes were presented as input to the neural network trained with particle swarm algorithm.
In performance tests carried out with face recognition system, it was observed that the system
performance is in the acceptable quality.
REFERENCES
Acar, E., & Özerdem, M.S. (2012). Kızıltepe tarımsal alan imgelerinin gabor dalgacık dönüşümü ile sınıflandırılması,
IEEE 20. Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SİU2012), Fethiye, 2012.
Bakbak P.O., & Peker, M. (2013). Particle swarm optimization design of optical directional coupler based on power
loss analysis. International Journal of Intelligent Systems and Applications in Engineering, 1(2), 29-33.
Buciu, I., & Gacsadi, A. (2009). Gabor Wavelet Based Features for Medical Image Analysis and Classification”.
Applied Sciences in Biomedical and Communication Technologies (ISABEL 2009), (pp. 1-4).
Chen, L.F., Liao, H.Y.M, Ko, M.T., Lin, J.C., & Yu, G.J. (2000). A new LDA-based face recognition system which
can solve the small sample size problem. Pattern Recognition, 33, 1713-1726.
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Ikizler, N., & Duygulu, P. (2005). Haber videolari icin yüz bulma yöntemlerinin.iyilestirilmesi, In Proceedings of
IEEE 13. Sinyal Isleme ve Iletisim Uygulamalari Kurultayi (SIU 2005), Kayseri, (pp. 1-4).
Kurt, B., Nabiyev, V., & Bekiroglu, Y. (2007). Yüz ifadelerinin tanınması. Elektrik-Elektronik-Bilgisayar
Mühendisliği 12. Ulusal Kongresi, (pp. 1-5).
Peker, M., & Zengin, A. (2010). Real-Time Motion-Sensitive Image Recognition System. Scientific Research and
Essays, 5 (15), 2044-2050.
Peker, M, & Zengin, A. (2011). A Real-Time and Motion-Sensitive Security Application with Face Recognition. 6th
International Advanced Technologies Symposium (IATS’11), Elazığ, Turkey, (pp. 92-97).
Sen, B., & Peker, M. (2013). Novel approaches for automated epileptic diagnosis using FCBF feature selection and
classification algorithms. Turkish Journal of Electrical Engineering & Computer Sciences, 21, 2092-2109.
Swets, D. L., & Weng, J. (1996). Using discriminant eigenfeatures for image retrieval. IEEE Transaction on Pattern
Analysis and Machine Intelligence, 18(8), 831-836.
The
Database
of
Faces,
“The
ORL
database
research/dtg/attarchive/facedatabase.html, Access time: 01.01.2014.
of
face”,
http://www.cl.cam.ac.uk/
Turk, M., & Pentland, A.P. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71-86.
Umbaugh, S. (1998). Computer vision and image processing fundamentals. Computer Vision and Image Processing.
Prentice Hall PTR, Bernard Goodwin.
Yalçın, N. (2012). Heuristic algorithm basis artificial neural networks for epilepsy detection. The Graduate School of
Natural and Applied Science of Selçuk University, MS Thesis, Konya.
Yalçın, N., Tezel, G., & Karakuzu, C. (2013). Epilepsy Diagnosis Using Artificial Neural Network Learned by PSO.
Turkish Journal of Electrical Engineering & Computer Sciences, online, DOI: 10.3906/elk-1212-151.
Yu, H., & Yang, J. (2001). A direct LDA algorithm for high-dimensional data with application to face recognition.
Pattern Recognition, 34, 2067-2070.
Musa Peker graduated from Zonguldak Karaelmas University in 2007. Received his master’s
degree in 2009 from Sakarya University. He received his Ph.D. in Computer Engineering
from Karabuk University in 2014. Currently working in Istanbul Samandira Vocational High
School as IT teacher. His interests are biomedical signal processing, image processing and
artificial intelligence applications.
Huseyin Guruler is academic staff in the Department of Information Systems Engineering in
Mugla Sitki Kocman University, Turkey. His bachelor's degree is in the field of electronics
and computer from Marmara University. MSc is in the field of statistics and computer in
Mugla Sitki Kocman University. PhD is in the field of electronics and computer in Sakarya
University. PhD thesis is about diagnosing sleep apnea using ECG signals. His research
interests merge in data mining and knowledge discovery besides dealing with computational
biology and multi-user computers architecture.
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2526
Title
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REAL-TIME FACE RECOGNITION WITH ARTIFICIAL NEURAL NETWORK TRAINED BY PARTICLE SWARM OPTIMIZATION
Author
Author
PEKER, Musa
GURULER, Huseyin
Abstract
A summary of the resource.
Face recognition is one of the widely used biometric method. Verification and recognition of individuals is possible via the features obtained from desired face image and compared with the facial image by various methods. Automatic face recognition which is a fundamental research area in the scope of pattern recognition, is applied in many civil, military and commercial areas for the purpose of authentication and identification. In this study a real-time face recognition system was developed. It is aimed that identification of individuals who entering any field observed with a camera. After detecting the important facial points, they are presented as input data to feed-forward neural network. Particle swarm optimization was used as learning algorithm in the network. As a result, a novel real-time face detection method, which provide high accuracy has been developed. Keywords: Real time face detection, pattern recognition, neural network, particle swarm optimization.
Publisher
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International Burch University
Date
A point or period of time associated with an event in the lifecycle of the resource
2014-05-15
Keywords
Keywords.
Article
PeerReviewed
Identifier
An unambiguous reference to the resource within a given context
ISSN 978-9958-834-36-3
Q Science (General),QH301 Biology,QH426 Genetics
-
https://omeka.ibu.edu.ba/files/original/18074c76058fa6024ba1edde23a0ab8d.pdf
5ed8d687115f098a100ccd10651089d4
PDF Text
Text
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CONNECTION OF NEW GENERATORS IN THE ELECTRICAL POWER SYSTEM
OF KOSOVO
Rexhep Shaqiri1, Bogdanov Dimitar2
1
Technical University - Sofia,
E-mail: rexhep_shaqiri@hotmail.com).
2
Technical University - Sofia, 8 st. Kliment Ohridski Blvd., 1756 Sofia, Bulgaria
E-mail: dbogdanov@tu-sofia.bg
Abstract
The power system of Kosovo is a compact and integrated structure in hierarchical aspect. It
plays an important role in the process of transmission and distribution energy to the
consumers. Based on this importance analysis is necessary in order to estimate the medium
and long term plans of production of electric energy and development of the power generation
plants. Object of study in this article is the project for connection of generators to the
substation in Decani. This article describes the simulations of the power system of Kosovo
(on date 21.01.2014 at 19.00h when the load in the system is 996.978 MW) in order to
emphasize the importance of the connections of generators in Decani substation. Decani
substation is an important node point with specifics of the power flow distribution. The
analysis of the Kosovo electric power system by means of ETAP software and using as a
reference the standards applied in Kosovo, the created models aim to justify if the plans for
improvement of Kosovo grid are appropriate and what kind of changes in the voltage levels
and short circuits values can be expected.
Keywords: Connection, Hydro Power Plant, Voltage profile, Synchronous generator,
Improvement, Power System, Operation
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1. Introduction
The task of the electric power system is to supply the customers with qualitative electric
power, with a high degree of reliability, but economically accepted. The generation,
transmission and distribution are three main components of the electric power system. Kosovo
power system consists of power plants, the main grid, regional networks, distribution
networks, and consumers of electricity. The main grid serves power producers and consumers,
enabling electricity trade throughout Kosovo and also across Kosovo borders. Electrical
power system of Kosovo mainly operates power plants using coal resources for electricity
production, thus has lack of energy of renewable resources. In result of this there is increased
interest of investment in the field of the alternative resource, such as hydro power plants. The
impact of the connection of generators to the distribution system, respectively in the
substation Decani, substation will be studied in details.
In the distribution system different cases may appear when new generators are connected,
because the system in this particular case was not designed as transmission structure, but
aimed for energy distribution to consumers. The connection of the new generators can affect
the stability of the system, quality of the energy and the reliability of the system.
2. Effects of new generators connected to the existing network
The connection of a generation to an electricity network has impact on the operation and
performance of that network. The connection of a generator to the system will result in some
changes to the characteristics of the network. There are some cases when generators can
enhance the performance of the network. New and existing generators connected to the
network have to fulfil the requirements: frequency stability, voltage deviation, voltage
waveform, voltage symmetry, power factor, operational and earthing and insulation level of
detail defined in Kosovo network (www.kostt.com).
The Kosovo companies (Transmission system and market operator) are responsible for the
operational planning and supervision of the main grid, for grid maintenance and grid
development. The main grid in Kosovo includes approximately 188,49 km of 400kV
transmission lines, 231,88 km of 220kV transmission lines, 803 km of 110kV
transmission/distribution lines, 400/220 kV -1 substation, 400/110 kV –2 substations, 220/110
kV – 3 substations, and 110/35 kV, 110/10 kV - 29 substations. The Kosovo system is
connected to the Macedonia, Serbia and Albania transmission systems on 400kV, 220 kV and
110 kV by overhead power lines.
In the electric power system of Kosovo the generation units are: Thermo power plant in
Kosovo A (A3-150 MW, A4-150 MW, and A5- 150 MW) is in total 450 MW and Kosovo B
(B1-300 MW and B2- 300 MW) is in total 600 MW, generator A1 and A2 are not in operation.
While hydro power plants in electric power system are: Ujman (U1, U2) is 32 MW,
Lumbardh (B1 and B2) is 8 MW.
There are also several sources of small hydro power plants with capacities such as: Burim (G1
and G2) 0.47 MW, Dikance (G1 and G2) -1, 32 MW, Radavc (G1 and G2) - 0.28 MW. In
electrical power system of Kosovo, substation Kosovo B 400/220 kV has three transformers
with tap changers, where the tap changers are in middle position and they do not change
position with load, so are static, in other substations, the transformers have tap changers that
working on the load.
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Presented below is a part of electrical power system when load in system is 996.978.MW
(Figure 1).
Figure 1. Voltage profile at some busbars in electrical power system of Kosovo when load is
996.978 MW.
Profile voltage at busbars in electrical power system of Kosovo
1
sh
tr i
2
Vu
tr r
i
sh
Vi
tia
Vu
ep
ça
da
Tr
an
er
Th
de
ra
j
c
ve
Sk
en
1
i
2
ho
ja
Ra
Pe
ja
Pe
an
pj
Ly
Kl
in
a
2
va
ko
ja
G
G
ja
ko
va
ni
ça
i
De
rim
Bu
1
111
110
109
108
107
106
105
104
103
102
Figure 2.Voltage profile at some busbars in electrical power system of Kosovo.
According to code of the electrical equipment in Kosovo system voltage limits are permitted
according to the table shown below (Transmission , system and market of operator of Kosovo)
(www.kostt.com).
400 kV
220 kV
110 kV
Voltage
in
normal
conditions
Min.
Max.
Voltage
voltage
380 kV
420 kV
209 kV
231 kV
99 kV
121 kV
Voltage in extreme
conditions
Min.
Max.
voltage
voltage
360 kV
440 kV
198 kV
242 kV
88 kV
130 kV
Table1. Voltage tolerances in Kosovo system.
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In the electrical power system of Kosovo there are some important substations which do not
fulfil criteria N-1 such as Peja 3-400/110 kV and Ferizaji 2-400/110 kV, these two substations
have a transformer with power 300 MVA. In the development plan for electrical power
system of Kosovo for these two substations shall be provided with additional transformers
with power 300 MVA. Greater impact in Decani substation has any change in Peja 3
substation, because is close to Decani substation.
In the current situation, when the line 164/1 is out of operation, busbar voltage profile of the
system is presented (Figure 3).
Figure 3. Voltage profile in electrical power system of Kosovo when the line 164/1 is out of
operation.
Profile voltage in electrical power system of Kosovo when the line 164/1 is out
of operation
sh
tr i
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1
Vu
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i
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da
ça
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ov
a1
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ni
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Gj
i
De
ri m
Bu
a2
115
110
105
100
95
90
85
Figure 4. Voltage profile in electrical power system of Kosovo when the line 164/1 is out of
operation.
The chart shows that the profile of voltage in every substation is in the permissible range for
the value of 110kV except substation Gjakova 1, which is considered according to the grid
network of Kosovo, as substation that not allowed being in operation, except in extreme
conditions.
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Figure 5. Voltage profile at electrical power system of Kosovo when the transformer is out
of operation.
In the current situation, when the transformer is out of operation in Peja 3 substation, busbar
voltage profile of the system is presented (Figure 5).
Profile voltage in electrical power system of Kosovo when the transformer is out
operation in Peja 3 substation
i1
r iz
aj
tr i
2
Fe
1
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i
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ri m
Bu
1
112
110
108
106
104
102
100
98
96
94
Figure 6. Voltage profile in electrical power system of Kosovo when the transformer is out
of operation.
The chart shows that the profile of voltage in every substation is in the permissible range for
the value of 110kV except substations as: Peja 1, which is considered according to the grid
network of Kosovo, as substations that are not allowed to be in operation.
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2.1. New energy sources
The connection of new generation sources in the power system changes the power flow,
customer’s voltage conditions and the requirements of the utility equipment. In a fault
situation, distributed generators modify the current contribution to fault, and therefore it
influences the behaviour of network protection. The influence will depend on the number,
type, location and size of generators. The distributed generators are mainly designed to be
connected directly to the distribution network near load centers.
Regarding the utilization of alternative renewable energy sources, Kosovo is not in the
appropriate levels. Approximately only 3% of the electricity produced in Kosovo is from
renewable energy sources. As Kosovo has signed the treaty for electricity and according to
Directive 2009/28/EC1 on the promotion of the use of energy from renewable sources (the
"Renewable Energy Directive") established mandatory targets to be achieved by 2020 for a
20% overall share of renewable energy in the EU and a 10% share for renewable energy in the
transport sector.
Based on this, as well as the duty of fulfilment the standards of EU for the renewable energy,
hydro power plants are under construction, such as the Decani river cascade with installed
power about 35 MW. Other small HPP along Decani river are: HPP Lumbardhi (8.3 MW,
22 GWh annual production), HPP Decani (14.3 MW annual productions 41.9 GWh), HPP
Belaja (8.1 MW annual productions 24.8 GWh), HPP Lumbardhi 2 (4.8 MW annual 17 GWh).
Presented below is the configuration of the connection of these generators in the power
system of Kosovo (Decani substation) (Figure 7).
Figure 7. Connection of new generators in electrical power system of Kosovo (Substation
Decani).
It is known that connection of the generators affects the voltage profile, the power flow and
the losses in power system, the stability of the system and the short circuit currents.
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2.2. Voltage profile of electrical power system of Kosovo
Profile of the load in 24 h profile in Decani substation is present the following chart (Figure 8).
Load in Decani substation (MW )
25
Load in Decani substation (MW)
20
15
10
5
21:00
22:00:
23:00
23:30:
20:00
19:00
18:00
15:00
16:00
17:00:
14:00
13 :00
12:00
11:00
10:00
08:00
09:00
07:00
06:00
04:00
05:00
03:00
02:00
00:00:
01:00
0
Figure 8. Depending on the time, loads in Decani substation.
Graphically are presented the bus bar voltage changes that occur when generators are
connected to the power system. In this case, when the generators are connected, the voltage
level is increased at the nodes which are part of the analysis. The expected values of voltage
approach the nominal value.
Figure 9. Voltage profile at some busbars when are connected generators (35 MW) in Decani
substation.
115
114
113
112
111
110
109
108
107
106
105
104
103
102
101
100
Profile voltage at some busbars when are connected generators (35 MW ) in Decani substation.
tr i
2
i1
sh
tr r
Vu
sh
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Vu
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Tr
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en
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ja 1
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a2
ov
ak
Gj
Gj
ak
ov
ni
De
ça
i
rim
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a1
Profile voltage at busbars in electrical power system of Kosovo
Figure 10. Variation of the voltage at busbars in the substations in Kosovo
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Therefore, when the generators are connected the voltage level is increased at all bus bar
system in Kosovo. In case, when transformer in Peja 3 substation is out of operation, the
voltage profile at the substation buses of the electrical power system of Kosovo is presented
(Figure 11).
Figure 11. Transformer while out of operation in Peja 3 substation, but generators are
connected (35 MW) in Decani substation.
Profile voltage in some busbars when transformer is out of operation in Peja 3
substation, but generators are connected (35 MW) in Decani substation.
tr i
2
1
sh
Vu
tr r
i
tia
sh
Vi
Vu
da
ça
Tr
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Th
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an
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ko
Kl
1
i
va
ça
n
G
ja
rim
Bu
De
i
112
110
108
106
104
102
100
98
Figure 12. Voltage profile in some busbars when transformer is out of operation in Peja 3
substation, but generators are connected (35 MW) in Decani substation.
The chart shows that the profile of voltage in every substation is in the permissible range for
the value of 110kV according to the grid network of Kosovo.
In case when line 164/1 is out of operation, profile voltage at the substation buses of the
electrical power system of Kosovo is presented (Figure 13).
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Figure 13. Voltage profile at some busbars when line 110 kV - 164/1 is out of operation.
Profile voltage at some busbars when line 110 kV - 164/1 is out operation
V
itia
Vu
sh
tr r
i1
Vu
sh
tr i
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ho
ve
c
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en
de
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an
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an
i
in
a
Kl
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rim
i
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ça
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ko
va
1
G
ja
ko
va
2
112
110
108
106
104
102
100
98
Figure 14. Profile of voltage at electrical power system of Kosovo when the line 164/1 is out
of operation.
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Presented below is the case when line 126/5 is out of operation (Figure 15).
Figure 15. Profile of voltage at some busbars when line 110 kV- 126/5 is out of operation.
Profile voltage at some busbars when line 110 kV - 126/5 is out operation
Vi
tia
Vu
sh
trr
i1
Vu
sh
tr i
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Ra
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c
Sk
en
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j
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i
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i
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G
ja
ko
va
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111
110
109
108
107
106
105
104
103
Figure 16. Profile of voltage at electrical power system of Kosovo when the line 126/5 is out
of
operation.
The worst case is when lines 126/5 and 164/1 are out of operation (Figure 17).
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Figure 17. Profile of voltage at some busbars when lines 126/5 and 164/1 are out of operation.
Profile voltage at some busbars when lines 110 kV - 164/1 and 126-5 are out operation
1
tr r
i
sh
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110
108
106
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100
98
96
94
92
90
Figure 18. Profile of voltage at electrical power system of Kosovo when the line 164/1 and
125/5 are out of operation
In this case substation Decani, Peja 2 and Gjakova 1 are not allowed to be in operation. The
chart shows that the profile of voltage in every substation is in the permissible range for the
value of 110kV except substations as: Peja 1, Peja 2, Decani and Gjakova 1, which are
considered according to the grid network of Kosovo, as substations that are allowed to be in
operation just for short time.
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3. Conclusions
In this paper the impact of the distributed generators connected to the electrical network
system of Kosovo was analysed.
From the presented simulation results a conclusion can be made that the distributed generators
can greatly influence the node voltages. The results show that new generators to be connected
to substation Decani will have a great impact on network voltages. Voltage profile problem in
the presence of distributed generation is much prominent than in the case without them.
Based on the current energy situation in Kosovo, the installation of hydro generators plays a
significant role in increased security besides the supply of customers, as well as to meet the
criteria for the share of alternative (renewable) energy in the energy mix of Kosovo.
Connection of new generators in Decani substation has impact on the voltage profile of the
electrical power system of Kosovo, especially in substations Decani, Gjakova 1, Peja 1 and
Peja 2.
Also, if any of the elements of the electrical power system such as lines 126/5,164/1, 1805,
164/2 or transformer in Peja 3 substation are out of operation, connection of new generators in
Decani substation has impact that the profile of voltage in every substation is in the
permissible range for the value of 110kV according to the grid network of Kosovo.
The construction of the particular new plants with hydro generators will play a significant role,
including security of the power system, the quality of voltage, increase generating capacity, as
well as more secure in the supplying of customers.
Also, the connection of generators is important as an option as grid reserve and grid
restoration generation in cases of commutations, outage of grid components and as well as in
case of planned outages of other generation facilities in the power system.
4. References
Government of Kosovo. (2014). Transmission system and market operator of Kosovo Power Network Analysis
for Wind. Power Integration Publication, April 2014, (page 17).
Li, Kam W.& A. Paul Priddey. 1985. Power Plant System Design. New York.
Lausterer, G. K., H. Weber, and E. Welfonder. 1993. Control of Power Plants and Power Systems. New York
and London.
Wood, Allen J.& Bruce Wollenberg. 1996. Power Generation, Operation and Control. New York.
P.M. Anderson & A.A. Fouad, 2003 Power System Control and Stability.
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Rexhep Shaqiri was born in Gjilan, Kosovo. He received the engineer electrical from the
Faculty of Electrical Engineering and Computer, Prishtina, and received Master degree from
the same university in 2009. His field of interest includes electrical networks, planning,
renewable energy sources of energy to grid, evaluation of grid short circuit levels. He has
excellent experience on coordination engineering feasibility studies, developing alternatives
for proposed capital projects, developing and updating facilities and system master plans.
Rexhep Shaqiri is PhD Student in the Technical University - Sofia, also assistant in the
Faculty of Electrical Engineering and Computer, Prishtina (rexhep_shaqiri@hotmail.com).
Dimitar Bogdanov was born in Sofia, Bulgaria. He graduated from the Technical University
- Sofia, and received Master degree from the same university in 1998. He has received a PhD
degree from Technical University - Sofia in 2009 in Electrical Power Engineering. His field
of interest includes electrical relay protections and automation, electrical networks, nuclear
power plants (electrical systems, control and safety aspects), renewable energy sources.
Currently he is associate professor in the Faculty of Electrical Engineering of the Technical
University – Sofia, dpt. head of chair “Electrical power engineering”. He works on studies
related to improvement of the protection schemes for connection of renewable sources of
energy to the grid, evaluation of grid short circuit levels and grid control. Dimitar Bogdanov
is with the Faculty of Electrical Engineering, Technical University of Sofia, 8, st. Kliment
Ohridski Blvd., 1756 Sofia, Bulgaria (e-mail: dbogdanov@tu-sofia.bg).
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2532
Title
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CONNECTION OF NEW GENERATORS IN THE ELECTRICAL POWER SYSTEM OF KOSOVO
Author
Author
SHAQIRI, Rexhep
BOGDANOV, Dimitar
Abstract
A summary of the resource.
The power system of Kosovo is a compact and integrated structure in hierarchical aspect. It plays an important role in the process of transmission and distribution energy to the consumers. Based on this importance analysis is necessary in order to estimate the medium and long term plans of production of electric energy and development of the power generation plants. Object of study in this article is the project for connection of generators to the substation in Decani. This article describes the simulations of the power system of Kosovo (on date 21.01.2014 at 19.00h when the load in the system is 996.978 MW) in order to emphasize the importance of the connections of generators in Decani substation. Decani substation is an important node point with specifics of the power flow distribution. The analysis of the Kosovo electric power system by means of ETAP software and using as a reference the standards applied in Kosovo, the created models aim to justify if the plans for improvement of Kosovo grid are appropriate and what kind of changes in the voltage levels and short circuits values can be expected. Keywords: Connection, Hydro Power Plant, Voltage profile, Synchronous generator, Improvement, Power System, Operation
Publisher
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International Burch University
Date
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2014-05-15
Keywords
Keywords.
Article
PeerReviewed
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ISSN 978-9958-834-36-3
Q Science (General),QH301 Biology,QH426 Genetics
-
https://omeka.ibu.edu.ba/files/original/984bb7f06b65d0fe958a68cc3dcebebf.pdf
28b6b9b3aa9b3ffa5309598c63e025aa
PDF Text
Text
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GENOFUND OF NATIVE FRUIT IN THE UNA - SANA CANTON
Azra Skender1, Dinko Bećirspahić1, Aida Mujagić-Pašić1, Senad Joldić1, Berin Kulelija2,
1
2
Biotechnical faculty, University of Bihać
Faculty of Agriculture and Food Science, University of Sarajevo
E-mail: skenderharun@yahoo.com
ABSTRACT
A high-quality genofund of fruit species and varieties, which are the result of centuries-long
adaptation and selection, characterises the Una – Sana Canton. Therefore, this work has
included research on the thirty-one sites in eight municipalities (Bihać, Bosanska Krupa,
Bosanski Petrovac, Bužim, Cazin, Sanski Most, Velika Kladuša) of the Una- Sana Canton.
The aim is primarily to protect and preserve endangered fruit trees and genofund, whereby the
first step in meeting these goals is the inventory of native species and varieties. The total of
275 native fruit accessions has been found at these sites, apples and pears being dominant.
Most native fruit accessions are not commercially significant varieties. However, local
inhabitants mainly use them in the processing and preparation of traditional products. Also, a
high degree of tolerance of these accessions for particular diseases and pests was recorded,
whereby these accessions represent an exceptional source of starting material for future
breeding steps.
Key words: Native fruit, genofund, Una-Sana Canton, inventory, accession
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INTRODUCTION
Preserving biodiversity is one of the key concerns of the world community, one of the most
important issues for the protection of the environment. The conservation of plant genofund is
essential for each country. While the genofund of agricultural species plays an important role
in agricultural production of a country, genofund of wild species plays an important role in
preserving the environment and preserving biodiversity. Domestic varieties are all those
varieties which originate from our country or which are grown for a long time here and are of
unknown origin, but according to their economic characteristics are very important, and have
a national significance. Domestic varieties are often similar in some basic features, because
they originate from the same environmental conditions. They usually have more important
agricultural and biological, as well as pomological characteristics, which make them suitable
as starting material for selection (Šoškić, 1994). According to Kowarik & Seitz (2003),
indigenous varieties are those natural populations spontaneously formed in a certain area, and
varieties that have directly or indirectly come to a particular area thanks to a man, and it is not
known from which area.
The history of our indigenous fruits varieties is very long. Balkan Peninsula is one of the most
important and richest centres of genetic diversity of fruit species in Europe. Many fruit
species during domestication came in contact with their wild relatives, and the crossing of
genetic material and adaptation to environmental conditions resulted in enriching the
biodiversity of the area (Vukojević et al., 2012).
As it is pointed out by Milenković and Lukić (2008), indigenous varieties are donors of genes
that are responsible for specific traits: resistance to the causes of diseases and pests, colouring,
flavour, resistance to abiotic factors of the environment, storage properties. Specialisation in
terms of choice of breeding of certain species and varieties towards which a modern fruit
production strives, the choice of intensive breeding systems, market demands, and striving for
the realization of greater financial gain, are the reasons of genetic uniformity, i. e. permanent
reduction of genetic variability of cultivated fruit trees, which may have disastrous
consequences. These effects generally manifest as outbreaks of plant diseases, a significant
reduction in yield or total absence of production of certain species or varieties (Jarebica &
Kurtović, 1997). As stated by Fischer (2002), an intensive production of new highly
productive varieties makes agrobiodiversity susceptible to diseases, pests and weeds, which in
turn demands the use of pesticides. The solution to this problem in the fruit growing can be
found in the genetic variability of indigenous varieties, therefore the conservation and use of
genetic resources of indigenous species and varieties.
Indigenous varieties of fruit, although very present and widespread in the Balkans, were rarely
studied. However , today more and more authors conduct research of indigenous fruit species
and varieties, which indicates ever increasing importance of conservation of fruit trees genetic
resources (Milenković & Lukić, 2008; Skender, Jahić, Hadžiabulić & Kurtović, 2008;
Ognjanov 2005; Begić-Akagić et al., 2011; Gaši et al., 2010, 2011, 2013 etc.). According to
Begić - Akagić et al. (2011), indigenous varieties in Bosnia and Herzegovina are valuable
sources of desirable genetic material for important pomological, nutritional and technological
properties of fruit. As Klještanović stated (2012), clonal selection of native apple varieties and
their application in hybrid combinations yielded visible results in Serbia.
The north-western part of Bosnia and Herzegovina, i. e. the Una-Sana Canton, is
characterized by high quality resources fruit species and varieties, which are the result of
centuries-long adaptation and selection. Given that these are very old high trees (indigenous
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varieties) in the system of extensive planting, the survival of these resources is questionable.
As in other parts of Bosnia and Herzegovina, commercial fruit varieties were dominant in the
20th century. This resulted in neglecting of old genotypes which, apart from representing our
natural and cultural heritage, are species and varieties highly resistant to diseases and pests. In
north-western Bosnia and Herzegovina, the presence of a large number of very old trees of
different varieties of apple, pear, plum, cherry, walnut and chestnut is evident. These
genotypes represent a reservoir of genes which should serve as the basis for the breeding of
these fruit species in the future. Therefore, the survival and preservation of these varieties is
the issue of the overall biodiversity of Bosnia and Herzegovina. The existing genetic diversity
can be preserved by establishing in situ, on farm and ex situ collection plantations.
METHOD
Research, i. e. inventory of native species and varieties of fruit was carried out over the period
July – December 2012, in north-western Bosnia and Herzegovina, i. e. the Una - Sana Canton.
The study included thirty–one sites in eight municipalities (Bihać, Bosanska Krupa, Bosanski
Petrovac, Bužim, Cazin, Sanski Most and Velika Kladuša). During the inventory, standard
research methodology and data collection of a large number of data from the field was used.
A survey was conducted on the field, regarding the data on the variety and age of a tree, basic
pomological characteristics, use value, resistance to disease and frost. The exact address has
been recorded for each tree. Every tree has been photographed, and very rare varieties have
been especially noted. Collection of fruits was done at the time of their full maturity, whereby
basic characterization and description of fruit have been conducted, and photographs taken.
Some of the identified varieties have not yielded fruit in the studied vegetation season. It is
assumed that the reason for this is low temperatures in the spring months, which damaged
flowers and led to their sterility and lack of birth. It was also observed that some of the
varieties have a characteristic of alternative birth (bear fruit every other year), so this is one of
the reasons why the fruits of all varieties found have not been collected. All collected data are
summarized in a working manual.
RESULTS
It is known that the inventory represents the first step in determining and rescuing rare plant
species and genotypes. Based on the inventarisation of indigenous fruit species and varieties
in the Una - Sana Canton, we established the actual condition and position which they take on
these sites. During the war period, but in the post-war years as well, many individual trees
have been cut down in order to build residential buildings, and old trees were dying out. The
specialization in terms of choice of individual species and cultivated varieties, then intensive
cultivation system, as well as market requirements and striving for greater financial gain led
to the gradual disappearance of autochthonous assortment. The results showed that the studied
area of the Una-Sana Canton today has an enviable genofond of native species and varieties,
but we also noted the disconcerting regression in breeding them. It was noted that almost
every municipality has a large number of varieties of apple trees Petrovka and Tuskača
(which possesses various synonyms in different areas, and is referred to as Čupa and Rapava).
Also, many varieties of apple trees Gavranuša (Garvanuša), Senabija, Zvečac (Zvečarka),
varieties of pear Kolatuša, Barliman, and Black Pear were found. In some locales, there are
many fruits of Požegača plum, which is still trying to resist its greatest disease, Plum pox
virus, in some parts of the Una-Sana Canton.
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DISCUSSION
The greatest attention should be focused on the assortment that has the smallest number of
trees on our sites, which is the most vulnerable and which we might lose in the near future.
Those are apple varieties Đulabija, Delbašinka, Muškinja, Šarenika, Stambolka, Zukva,
Prutulja, the varieties of pear Karupnjača, Medika, Vodenjača (Jeribasma), and plum
Zambelija. Currently, this assortment listed has the smallest number of individual trees and
represents the most endangered varieties in the area of the Una-Sana Canton, and especially
those with trees older than 100 years. The age of catalogued trees usually ranges between 50
and 70 years, although there are trees older than 100 years. The encouraging fact is that there
are trees between the ages of 15 and 30, which means certain indigenous varieties will be kept
in this area for some time.
Inventoried trees are mainly grown in extensive conditions, without the use of any
agrotechnics, and no significant attack of pests and diseases was noticed, which suggests that
these varieties are resistant to pests and diseases in poor growing conditions. Because of that,
the preserved indigenous genofund of these endangered varieties can be used in breeding
purposes, because they have characteristics which new varieties mainly lack. Those are
resistance to frost, disease and pests, late blossoming, and some have good organoleptic
properties.
Skender, Jahić, Hadžiabulić, and Kurtović (2008) have conducted tests of pomological
characteristics of some of these indigenous varieties of apples from the area of the Una-Sana
Canton, and the results showed that certain indigenous varieties in this region have highquality varieties regarding their pomological traits, where the variety of Kisela apple is
particularly prominent and still enough represented. Drkenda et al. (2007) have conducted
similar research when it comes to technological properties of some indigenous apple
genotypes in the area of Goražde, and came to the conclusion that the tested genotypes have
satisfactory values of important technological parameters, and the fruits of these genotypes
can be recommended for direct consumption and for processing.
In the municipalities of Velika Kladuša, Cazin and Bužim, there are large natural populations
of chestnut (Castanea sativa Mill.), which are the largest natural habitats of European edible
chestnut in our country. These populations can be used to select suitable material for genetic
selection and conservation of natural populations of chestnut in Bosnia and Herzegovina.
During this study, a chestnut tree estimated to be about 300 years old was found in Rošići
near Pećigrad.
In the end, it can be concluded that 275 indigenous varieties of fruit trees have been found and
photographed in 31 investigated sites in the eight municipalities of the Una - Sana Canton.
Many of the varieties found are similar in phenotype, but they may have different synonyms
at different locations. Indigenous varieties are similar in some of its properties, as they
occurred in the same environmental conditions. To draw attention to preserving our native
varieties, both through counselling of small farmers and raising awareness of all our country's
citizens to protect and preserve our agrobiodiversity is of utmost importance.
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CONCLUSION
Based on the inventory of indigenous varieties of fruit in the Una-Sana Canton, the following
conclusions can be drawn:
Despite large regression of indigenous varieties in the last twenty years, the area of the
Una - Sana Canton still has a considerable number of indigenous varieties of fruit.
The most common indigenous varieties of apple in this area are Petrovka, Tuskača,
Čupa, Gavranuša, Senabija , Zvečac, pears Kolatuša, Barliman, and Black Pear, while
the most common plum variety is Požegača.
The most vulnerable indigenous varieties which could be extinct soon are varieties of
apple Đulabija, Delbašinka, Muškinja, Šarenika, Stambolka, Zukva, Prutulja, and
pears Karupnjača, Medika, and Jeribasma, while the most vulnerable plum variety is
Zambelija.
When it comes to the age of trees of the indigenous varieties, it usually ranges between
50 and 70 years, although there are trees that are older than 100 years, and those
younger than 30 years.
Since inventory is the first, but very important step in preservation of indigenous fruit
varieties, it is necessary to intensify research in the field of indigenous varieties of fruits and
preserving them in the near future, because they represent a valuable natural and cultural
heritage of the area, as well as the genetic material for breeding purposes. One of the ways to
preserve and protect indigenous species and varieties in the Una - Sana Canton is the raising
of the first ex situ collection planting of those indigenous varieties which have been found to
be most vulnerable. This planting would, apart from conservation of these species and
varieties, provide an opportunity for detailed studies of their properties, and would serve to
obtain starting material for further propagation and breeding. Also, raising the ex situ
plantations would enable us to find native species and varieties of this region in one place,
which would raise the awareness of local people about the importance of preserving
indigenous genofund and values that these varieties have.
REFERENCES
Begić - Akagić, A., Spaho, N., Oručević, S., Drkenda, P., Kurtović, M., Gaši, F., Kopjar, M., & Piližota, V.
(2011). Influence of cultivar, storage time, and processing on the phenol content of cloudy apple juice. Croatian
Journal of Food Science and Technology 3 (2), 1 – 8.
Drkenda, P., Kurtović, M., Čaušević, E., Begić-Akagić, A., Gaši, F., & Kanlić, K. (2007). Tehnološka evaluacija
autohtonih genotipova jabuke na području Goražda. Zbornik sažetaka, 42nd Croatian & 2nd International
Symposium on Agriculture, 271.
Fischer, M. (2002). Bilanz 10-jahriger Arbeit. Genbank Obst Dresden-Pillnitz.
Jarebica, Dž., & Kurtović, M. (1997). Oplemenjivanje voćaka i vinove loze. Edis, Sarajevo.
Klještanović, L. (2012). Izbor sorti jabuke u funkciji zaštite agroekosistema. Aktuelni savetnik, Očuvanje i
unapređenje biološke i genetičke raznovrsnosti 10, 12 – 15.
Kowarik, I., & Seitz, B. (2003). Perspektiven fur die Verwendung gebietseigener „autochtoner“ Geloze. Institut
fur Okologie der TU Berlin.
Milenković, S., & Lukić, M. (2008). Autohtone i novostvorene sorte jabuke u organskoj proizvodnji. Zdravo –
Organic, II. Simpozijum.
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Skender, A., Jahić, S., Hadžiabulić, S., & Kurtović, M. (2008). Evaluacija pomoloških karakteristika autohtonih
sorti jabuke Cazinske krajine. Savremena poljoprivreda 1 – 2, 124 – 129.
Šoškić, M. (1994). Oplemenjivanje voćaka i vinove loze. Papirus, Beograd.
Vukojević, D., Simić, J., Dragišić, N., Sevo, D., Misimović, M., Zavišić, N., Bolić, E., & Radanović, B. (2012).
Evaluation of the qualiti of autochthonous plum cultivars in the area of Bosanski Petrovac. Agrosym Jahorina,
161 – 166.
Azra Skender PhD., assistant proffesor. Theme of doctoral dissertation: Genetic and
Pomological Variability of Chestnut Population in Bosnia and Herzegovina. Research inerests:
plant breeding an genetics in agriculture.
Dinko Bećirspahić MSc., assistant. Research interests: Modern technology of growing fruit,
Pomotechnics in fruit growing, Fruit breeding.
Aida Mujagić-Pašić MSc, assistant. Research interests: Biosystematics of plants,
Biodiversity and variability of plants.
Bsc. Senad Joldić. Young researcher in the field of Modern technology of growing fruit.
Msc. Berin Kulelija. Research interests: agro-food trading sector with emphasis on
agroeconomy.
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2483
Title
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GENOFUND OF NATIVE FRUIT IN THE UNA - SANA CANTON
Author
Author
SKENDER, Azra
BEĆIRSPAHIĆ, Dinko
MUJAGIĆ-PAŠIĆ, Aida
JOLDIĆ, Senad
KULELIJA, Berin
Abstract
A summary of the resource.
A high-quality genofund of fruit species and varieties, which are the result of centuries-long adaptation and selection, characterises the Una – Sana Canton. Therefore, this work has included research on the thirty-one sites in eight municipalities (Bihać, Bosanska Krupa, Bosanski Petrovac, Bužim, Cazin, Sanski Most, Velika Kladuša) of the Una- Sana Canton. The aim is primarily to protect and preserve endangered fruit trees and genofund, whereby the first step in meeting these goals is the inventory of native species and varieties. The total of 275 native fruit accessions has been found at these sites, apples and pears being dominant. Most native fruit accessions are not commercially significant varieties. However, local inhabitants mainly use them in the processing and preparation of traditional products. Also, a high degree of tolerance of these accessions for particular diseases and pests was recorded, whereby these accessions represent an exceptional source of starting material for future breeding steps. Key words: Native fruit, genofund, Una-Sana Canton, inventory, accession
Publisher
An entity responsible for making the resource available
International Burch University
Date
A point or period of time associated with an event in the lifecycle of the resource
2014-05-15
Keywords
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Article
PeerReviewed
Identifier
An unambiguous reference to the resource within a given context
ISSN 978-9958-834-36-3
Q Science (General),QH301 Biology,QH426 Genetics
-
https://omeka.ibu.edu.ba/files/original/bb38d4ecda5b3ea7ddb095d1483eb868.pdf
155c4e70e75424a49de0e94154cadd04
PDF Text
Text
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DETERMINATION OF FIBROSIS SCORE IN THE VENTRICULAR ARTER BY
USING IMAGE PROCESSING TECHNIQUES ON HISTOPATHOLOGICAL
IMAGES
Dilek Sönmezer1, Yasemin Benderli Cihan2, Fatma Latifoğlu3
1
Çukurova University, Faculty of Engineering and Architecture
Department of Biomedical Engineering
Adana, Turkey
dsonmezer@cu.edu.tr
2
Department of Radiation Oncology
Kayseri Education and Research Hospital
Kayseri, Turkey
cihany@erciyes.edu.tr
3
Erciyes University, Faculty of Engineering
Department of Biomedical Engineering
Kayseri, Turkey
flatifoglu@erciyes.edu.tr
ABSTRACT
Histopathological image analysis is an important area for pathological image analysis and
diagnosis in medicine. Among cancer patients, radiotherapy is widely used for treatment
modality. The aim of the radiotherapy is giving maximum dose to tumor tissue as well as
maintaining normal tissue unaffected as possible. The increase of the radiation dose is parallel
with local tumor control. However, risk of complication of normal tissue is also increased.
Thus, controlling of the tumor depends on the normal tissue tolerant. In this study, we aim to
support radiologists to detect and control radiation dose and its effects for the radiotherapy.
Determining of the ventricular artery thickness by using image processing techniques can give
information about radiation effects and dose. Using by image processing techniques,
adventitia layer which of cardiac vessel layer thickness is measured. For this measurement,
whole cardiac histopathological RGB image is cropped and studies are applied on this image.
Then RGB image is converted to grayscale image and after converted the binary image.
Adventitia layer is detected with edge detecting method. After segmentation of the adventitia
layer, this layer thickness is measured to show effects of the radiation dose. Thus, with this
study an optimal radiation dose can be adjusted according to the increase of the adventitia
thickness.
Keywords: Histopathological images; cardiac tissue; radiotheraphy; fibrosis
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INTRODUCTION
Histopathological image analysis is important for getting good results on therapy.
In this study, we aim to support radio therapist to detect radiological effects in cancer
treatment. Treatment of radiation is used to kill cancer cells or to stop of cancer cells
proliferation. At that time, determination of radiation dose limits depending on the layer of
vessel wall can be a new approach in cancer treatment. Radiation damages the vessel wall,
especially causes vessel wall fibrosis. Thus, radiation dose and its application are important
for human health.
Image processing techniques for diagnosis of diseases are widely applied in medicine [1, 2].
Fibrotic Myocardial Tissue Mechanics, classification of cervical cancer, diagnosis of prostate
cancer, morphological analysis of carotid artery plaque and a study related with coroner artery
and many more studies about histopathological image analysis are present in literature [3-7].
Image processing techniques also used in enhancement of vessels on angiography images. [8].
Response of arterial injury is determined based on adventitia of artery wall layer [9].
Thickness of the adventitia layer increases with the increment of the arteriosclerosis [12].
Radiotherapy is usually used for treatment of cancer disease, but also late side effects which
depends on radiation therapy become crucially important [10]. Toxicological effects on the
cardiovascular system are resulted from radiotherapy used for treatment breast cancer. [Mc
Chesney SI, Rad Res, 1991; 125].
The aim of this study is to investigate whether the use of radiotherapy (RT) has a contribution
to the development of radiation fibrosis in the heart.
MATERIAL AND METHODS
In this study, experimental studies were conducted in Erciyes University Faculty of Medicine
Hakan Çetinsaya Experimental and Clinical Research Center (DEKAM) after ethical aproval
from the Animal Experiments Local Ethics Committee of Erciyes University Faculty of
Medicine. Twenty healthy female Wistar Albino rats aged 8 weeks and weigthing 213±27
grams were used in the study. The rats were kept under standard laboratory conditions (12:12hour light/dark cycle at 25±3 oC) and fed with standard commercial pelleted feed. The rats
were divided into 2 groups with 10 animals in each group. The groups were designed as
follows:
Group C: The control group. No treatment was administered. The animals were followed
under similar conditions as the other animals.
Group RT: The radiotherapy-only group. The thoracic region was irradiated while the
animals were under anesthesia.
Histopathological examination: Samples were taken from various regions of heart that were
fixed in formalin. Following paraffin blocking procedure, serial cross-sections of 5 microns
were obtained. They were stained with hematoxylin-eosin. Mean of the fibrosis scoring was
obtained for the heart vessel of each rat. The fibrosis score was numerically assessed based on
values from 0 to 4. The scoring system for the intensity of fibrosis is as follows:
Score 0: No fibrosis or minimal fibrosis in the vessel wall.
Score 1: Moderate fibrosis that does not cause marked structural damage on the heart.
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Score 2: Increased fibrosis with definite damage to heart vessel and formation of fibrous
bands or small fibrous masses.
Score 3: Fibrosis that causes severe distortion in the heart vessel and that has large fibrous
areas.
Score 4: Total fibrosis.
The rats were divided into two equal groups as follows: Group C: control and Group RT: RT
only. RT was administered heart region in a single fraction at a dose of 12 Gy using a Co–60
device. At the end of 24 weeks, the rats were sacrificed after sedation. The heart was
removed and blocked in paraffin. After H&E staining, the level of fibrosis in each crosssection was assessed with the help of a scale. Histopathological images were obtained from
vessels, which showed late toxic effect on the heart. Histopathological tissue preparats of
cardiac vessel were used for imaging. These cardiac tissues were locally applied radiotherapy
on healthy rats. Leica microscope together with Olympus 3.2 Megapixel, C-3020 200m
camera was used for routine inspections.
Histological image dataset was cinsists of 10 image slides. Our approach proposes measuring
the adventitia thickness in order to understand effects of the radiation therapy. Methods for
measuring adventitia thickness were combined with image processing techniques. Firstly,
RGB image of the histopathological image was converted to gray-level image. After that,
optimal thresholding was carried out by Otsu method to create a binary image. Then,
mathematical morphology operations were used to obtain adventitia layer. The proposed
approach is based on measuring of the adventitia layer by segmenting of this layer on
histopathological images. Obtaining an average of the adventitia thickness of two images was
used to show effects of the radiotherapy.
Figure 1. shows the microscopic image of the vessel of the rat at 600x magnification. In this
image low radiation dose was applied to rat cardiac. Segmentation of the outer layer is seen as
blue color (Fig. 1).
In Fig. 1.A, histopathological preparats were imaged via light microscopy at 600x
magnification. RGB image was cropped in order to show the related part of the vessel area in
figure 1.B. Then, RGB image was converted to grayscale image and after that obtained
grayscale image is converted to binary image as seen in figure 1.D. Vessel adventitia layer
was detected with Sobel edge detection method (Fig. 1.E). Taking complementation of the
edge was detected image and showed it on grayscale vessel image and RGB image (Fig. 1.G).
Finally, adventitia layer was measured with using five points on the layer of the distance
between outer and inner surfaces (Fig. 1.I).
RESULTS AND DISCUSSIONS
Radiotherapy is an important treatment for breast cancer and any other kinds of cancers
diseases. Because of the side effects of the radiation can generate problems on cardiac tissue.
Due to the success of mammography in early diagnosis and the advances in chemotherapy and
radiotherapy, breast cancer is a type of cancer whose treatment techniques is changing rapidly
and can be combined also. A multimodal treatment that includes surgery, radiotherapy,
chemotherapy, and hormonotherapy is used in the treatment of breast cancer. After surgery,
patients are initially treated with chemotherapy and then undergo RT. Although the optimal
sequence of chemotherapy and RT is still controversial, the generally accepted approach is RT
after chemotherapy is completed. It is still unclear whether hormonotherapy should be used
sequentially or concurrently with RT.
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For detection of the radiation toxicological effects on the cardiac vessels are examined with
proposed method in healthy but applied radiation to rats. Therefore, an optimal dose of the
radiation can be applied according to vessel damaged score. In this study, we aim to measure
just adventitia layer using by histopathological vessel images.
Fig. 1 Adventitia layer segmentation and measuring flowchart.
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REFERENCES
[1]
KCA Sneeuw, NK Aaronson, JR Yarnold, M Broderick: Cosmeic and functional outcomes of breast
conserving treatment for early stage breast cancer. 1. Comparison of patients ratings, observers’ ratings and
objective assasments. Radiat and Oncology, 1992; 25:153-159.
[2]
Al-Ghazal SK, Blamey RW, Stewart J, Morgan AA: The cosmetic outcome in early breast cancer treated
with breast conservation. Eur. J. Surg. Oncol; 1999;25:6:566-70.
[3]
L. Cordero-Grande, T. Sevilla, A. Revilla, M. Martín-Fernández and C. Alberola-López. "Assessment of the
Fibrotic Myocardial Tissue Mechanics by Image Processing". Proceedings of the IEEE Computing in
Cardiology Conference (accepted for publication), Zaragoza (Spain), Sep. 2013.
[4]
Rahmadwati, G. Naghdy, M. Ros, C. Todd & E. Norachmawati, "Classification cervical cancer using
histology images," in International Conference on Computer Engineering and Applications, 2010, pp. 515519.
[5]
E.C. Kyriacou, C.S. Pattichis, M.S. Pattichis, C.P. Loizou, C.I. Christodoulou, S.K. Kakkos, and A.N.
Nicolaides, "A Review of Noninvasive Ultrasound Image Processing Methods in the Analysis of Carotid
Plaque Morphology for the Assessment of Stroke Risk", IEEE Transactions on Information Technology in
Biomedicine, vol. 14, issue 4, pp. 1027-1038, July 2010
[6]
S. Balocco, C. Gatta, M. Alberti, X. Carrillo, J. Rigla, P. Radeva, "Relation between plaque type, plaque
thickness, blood shear stress and plaque stress in coronary arteries assessed by X-ray Angiography and
Intravascular Ultrasound", Med Phys 39(12):7430-45 (2012), PMID 23231293
[7]
S. Verma, A. Rajesh, AJR Am J. Roentgenol, A clinically relevant approach to imaging prostate cancer,
2011 Mar;196(3 Suppl):S1-10 Quiz S11-4. Review.
[8]
P. Tran Ho Truc, Md. A. U. Khan, Y. Lee, S. Lee, T. Kim: Vessel enhancement filter using directional filter
bank.Computer Vision and Image Understanding 113(1): 101-112 (2009)
[9]
M. Jean-Baptiste, T. Olivier, H. Xavier, M. Olivier, C., Giuseppina, N. Antonino, 2000: Topological
determinants and consequences of adventitial responses to arterial wall injury. Arteriosclerosis Thrombosis
and Vascular Biology 27(6): 1259-1268
[10]
Perez CA, IJROBP,1998,44:855 Lanciano RM, Cancer,1992,69:2124 Pollack A, IJROBP, 2002, 53:1097
[11]
G. Rioufol, M. Elbaz, O. Dubreuil, A. Tabib, G. Finet, Adventitia measurement in coronary artery: an in
vivo intravascular ultrasound study, Heart. 2006 July; 92(7): 985–986.
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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Extent
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2504
Title
A name given to the resource
DETERMINATION OF FIBROSIS SCORE IN THE VENTRICULAR ARTER BY USING IMAGE PROCESSING TECHNIQUES ON HISTOPATHOLOGICAL IMAGES
Author
Author
SONMEZER, Dilek
CIHAN, Yasemin B.
LATIFOGLU, Fatma
Abstract
A summary of the resource.
Histopathological image analysis is an important area for pathological image analysis and diagnosis in medicine. Among cancer patients, radiotherapy is widely used for treatment modality. The aim of the radiotherapy is giving maximum dose to tumor tissue as well as maintaining normal tissue unaffected as possible. The increase of the radiation dose is parallel with local tumor control. However, risk of complication of normal tissue is also increased. Thus, controlling of the tumor depends on the normal tissue tolerant. In this study, we aim to support radiologists to detect and control radiation dose and its effects for the radiotherapy. Determining of the ventricular artery thickness by using image processing techniques can give information about radiation effects and dose. Using by image processing techniques, adventitia layer which of cardiac vessel layer thickness is measured. For this measurement, whole cardiac histopathological RGB image is cropped and studies are applied on this image. Then RGB image is converted to grayscale image and after converted the binary image. Adventitia layer is detected with edge detecting method. After segmentation of the adventitia layer, this layer thickness is measured to show effects of the radiation dose. Thus, with this study an optimal radiation dose can be adjusted according to the increase of the adventitia thickness. Keywords: Histopathological images; cardiac tissue; radiotheraphy; fibrosis
Publisher
An entity responsible for making the resource available
International Burch University
Date
A point or period of time associated with an event in the lifecycle of the resource
2014-05-15
Keywords
Keywords.
Article
PeerReviewed
Identifier
An unambiguous reference to the resource within a given context
ISSN 978-9958-834-36-3
Q Science (General),QH301 Biology,QH426 Genetics
-
https://omeka.ibu.edu.ba/files/original/6e497079419e94af475422ae03fbe5a5.pdf
82214a6cde3b58d3d0b590b710b9411e
PDF Text
Text
PROCEEDINGS
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THE CHEMICAL COMPOSITION OF THE CRAYFISH
(Astacus leptodactylus) IN POND YENICE
Selçuk Berber, Selçuk Türel*, Sevdan Yılmaz
Marine Sciences and Technology Faculty,
Çanakkale Onsekiz Mart University, Çanakkale, Turkey
*
Correspondonce: selcukturel@comu.edu.tr
Abstract
The changes in the compositions of crude protein, fat and fatty acid in the muscle tissues of
male and female samples of Astacus leptodactylus acquired by hunting in the dates between
November 2007 and June 2008 which is the breeding season for the crayfish from Pond
Yenice which is used for irrigation in Çanakkale province, Turkey, are examined in the study.
Whereas the amount of crude protein in male samples is around 11.78-15.68%, it is identified
that the amount is around 13.09-17.59% with the female samples. Fat changes from 3.294.95% for the male samples, 3.67-5.82% with the female samples. It is observed that there is a
continuous increase in the compositions of crude protein and fat generally through the
beginning and the end of the sampling period. The amounts of EPA, DHA and AA show
change according to the breeding season and season.
Keywords: Astacus leptodactylus, Fatty acids, Crude protein, Lipid, Seasonal change.
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Introduction
The crayfish (A. leptodactylus), a member of the Astacidae family, is widely seen in our
inland waters. Generally, they live in various habitats such as streams, rivers, ponds and lakes
(Lowery Köksal, 1988). Having more than 500 species over the world, the crayfish is
represented only by two subspecies (A. leptodactylus leptodactylus and A. leptodactylus
salinus) of A. leptodactylus species (Geldiay & Kocataş, 1970). They exist naturally in many
lakes, reservoirs and rivers. Crayfish is one of the species of the crustacean species which has
a high economic value.
The A. leptodactylus species is reported in the inland waters of Turkey in Kayseri, Bursa and
İstanbul for the first time (Bott, 1950). While the crayfish were represented only by A.
leptodactylus until recent time in our country, the existence of Austropotamobius torrentium
(Shrank, 1803), a new species in the northern Thrace, is proven (Harlıoğlu Güner, 2006).
There are about 500 lakes in various sizes in the Marmara region and these lakes and ponds
are unrestrainedly filled with crayfish in different times. One of the water resources that are
filled with crayfish is Pond Yenice (Berber, Yıldız, Ateş, Bulut, Mendeş, (2010)) (Çanakkale,
Yenice Central Irrigation Pond).
Its most significant natural habitat is lakes and ponds and they began to be seen widely in the
lakes and ponds of the Marmara region after the 2000’s (Harlıoğlu, 2004; Berber et al. 2010).
Reaching 5000 tons in the 1980’s nationwide in Turkey, the crayfish production decreased
substantially due to a mycosis, the crayfish plague (Aphanomyces astaci) after the date (Bolat,
2001).
Like in other aquaculture products productions, the breeding season and breeding physiology
are quite significant in crayfish production as well. Healthy and genetically durable offspring
are needed to increase the amount of production in the lakes and ponds. The quality of the
brood stock should stand out in order to acquire individuals with high life forces. The
availability of quality offspring is based on body biochemical compositions of the brood stock.
The composition of fatty acid in the muscle and egg tissues of the brood stock individuals is
an important parameter for quality as well. Fatty acid levels of EPA, DHA and AA in the
muscle and egg content are especially important for the offspring quality (Bulut, 2003). The
fatty acids; DYA, MUFA and PUFA are individually important for the offspring and brood
stock quality and should be evaluated one by one (Bulut, 2003).
The effect of the fatty acid content on the offspring quality is studied in the research. The
seasonal fatty acid change in the muscle tissue of the brood stock is identified.
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Material and Methods
Research Field and Material
This research is performed in the Center Irrigation Pond in Yenice District, Çanakkale
Province between the dates July 2007-June 2008. 1842 A. leptodactylus salinus crayfish
caught from the pond are used as material in the test. Literature is used in determining the
type of crayfish (Bott, 1950; Holthius, 1961; Geldiay Kocataş, 1970; Köksal, 1988)
The Hunting Gears Used
A single-entry fyke net with two venters is used to catch the crayfish samples from the pond.
The fyke net used have 5 frames and a stretch net is put between two fyke nets. The cell width
of the fyke net is 34 mm.
Identification of Physical and Chemical Features of the Water Samples
The warmth, oxygen, pH, salinity and conductivity features of the pond water is evaluated
using YSI Probe (556 MPS) and its calcium and magnesium contents are analyzed in
inductively-matched plasma-atomic emission spectrometry, in Çanakkale Onsekiz Mart
University, Science and Technology Application and Research Center.
Chemical Analysis
In this study, 255 male (mean weight=29.96 g) and 291 female (mean weight=31.04 g) are
used for the chemical analysis. All analyses were performed in triplicate. Analyses of crude
protein, moisture and ash in crayfish were performed according to standard procedures
(AOAC, 2000). Dry matter content of samples was determined by drying at 105 oC until a
constant weight was obtained. Ash content was measured by incineration in a muffle furnace
at 525 oC for 12 h. Crude protein (N*6.25) was analyzed by the Kjedahl method after acid
digestion using the Gerhardt system. Total lipids in the crayfish were extracted according to
the procedure of Floch, Lee Sloane-Stanley (1957) with chloroform/methanol (2:1 v/v).
The fatty acids in the total lipid were esterified into methyl esters by saponification with 0.5 N
methanolic NaOH and Trans esterified with 14% boron trifluoride-methanol (AOAC, 2000).
Fatty Acid Methyl Esters (FAME) were analyzed using a flame ionization gas chromatograph
(Shimadzu GC-2014) equipped with an Omega wax 250 capillary column (30 mg/l X 0.25
mm internal diameter), a Flame Ionization Detector (FID) and a split injection system with
nitrogen carrier gas. Injector port and detector temperatures were maintained at 250 oC and
260 oC, respectively. The column temperature program was held at 140 oC for 5 min and then
elevated at a rate of 3 oC/ min to 200 oC. Total run time was 60 min per sample. Fatty acids
were identified by comparing their retention times of the standard fatty acid standards (SigmaAldrich Co, USA).
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Statistical analysis
All data were subjected to one way analysis of variance (ANOVA) and Duncan multiple
range test using Stat graphics 7.0 version for Windows (Manugistics Incorporated, Rockville,
MD, USA). The results were treated statistically significant at the P<0.05 level.
Results
The physico-chemical water parameter findings acquired from Pond Yenice are displayed in
Table 1. The highest temperature detected in the pond during the field work is 26.8°C in
August; the lowest is 5.7°C in December 2007. The dissolved oxygen values are determined
to be under 8mg/1 in the dates July, September and October 2007; determined to be above
10mg/1 in the dates December 2007, February, March and June 2008. Apart from these, the
pH is 5.5 and the following values displayed a change between these ranges: conductivity
229.1-387.1 μS, Ca 21.06-38.27 mg/l, Mg 6.91-10.47 mg/l. The saltiness (S) levels are
determined to be 0.2 ppt except for the dates July-August 2007 (0.1 ppt).
Table 1.The physico-chemical water parameter findings acquired from Pond Yenice
Tarih
T (°C)
ÇO (mg/l)
pH
EC (μS)
Ca (mg/l)
Mg (mg/l)
S (ppt)
July,2007
28.9
7.65
9.03
279
36.16
9.85
0.1
August,2007
26.8
8.25
8.95
301.2
37.46
10.47
0.1
September,2007
22.1
7.9
8.8
330.2
31.33
9.21
0.2
October,2007
18.2
7.6
8.75
387.1
24
8.41
0.2
November,2007
8.3
8.72
7.14
229.1
23.08
8.11
0.2
December2007
5.7
11.8
8.68
269.5
36.43
9.77
0.2
January,2008
8.2
8.5
8.34
320.1
21.06
8.91
0.2
February,2008
9.1
10.2
7.86
344.6
24.43
8.02
0.2
March,2008
9.6
12.85
9.03
382.3
38.27
7.88
0.2
April,2008
13.9
9.53
6.11
311.6
23.85
6.91
0.2
May,2008
15.9
9.02
5.5
265.8
34.31
7.44
0.2
June,2008
22.4
10.69
6.85
234.5
36.5
8.11
0.2
The changes in the crude protein and fat compositions in the muscle tissues of the male and
female individuals of A. leptodactylus species acquired from Pond Yenice are displayed in
Table 2. While the lowest amount of crude protein (respectively 11.78 – 13.09 units) of both
male and female individuals is observed in the samples taken in November, the highest
amount (respectively 15.68 – 17.59 units) in both individuals is determined in the samples
taken in June. While the monthly difference in the samples taken from male individuals in
May and June are seen significant, it is determined that this difference is significant for the
females in the months April, May and June (P<0.05). While the monthly change in the fat
compositions of male individuals are seen significant only in June, the fat changes are
significant for the female individuals in November, April and June (P<0.05). In the study, the
interaction between male and female individuals is evaluated.
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Table 2. Biochemical composition of muscle tissues of Freshwater crayfish (A. leptodactylus)
November
January
March
April
May
June
Crude Protein
11.78a
12.06a
13.45ab
13.86ab
14.06b
15.68c
Male
Fat
3.29a
4.63ab
4.08ab
3.78ab
4.66ab
4.95b
Ash
0.91a
1.45ab
1.13ab
1.33ab
1.59ab
1.32b
Crude Protein
13.09a
14.23a
15.35ab
16.47b
17.12b
17.59b
Female
Fat
3.67a
4.28ab
4.00ab
4.72b
4.50ab
5.82c
Ash
1.03
1.58
1.74
1.57
1.36
1.25
Lower-case letters indicate differences in the same column
The fatty acid changes in the muscle tissues of the male individuals of the crayfish that were
acquired in the breeding season are shown in Table 3, the fatty acid changes in the muscle
tissues of the female individuals are shown in Table 4. While the C14:0 fatty acid is found
significantly high in November (1.16%) and in April (1.32%) in male individuals, this
difference is found significant for the female individuals only in November (1.13%) (P<0.05).
The amount of C15:1 fatty acid is determined to have a remarkable increase in June (2.28%)
for the male individuals and in April (2.87%) for the female individuals. While the difference
in the C16:0 fatty acid composition in April (16.22%) is observed to be significant only for
the male individuals, this difference is not significant for the females (P<0.05). While the
changes in the C16:1 fatty acid is found significantly high in January (4.28%) for the male
individuals and in April (7.34%) for the female individuals (P<0.05).
Table 3. Variation in fatty acid compositions of muscle tissues of male Freshwater crayfish (A.
leptodactylus) (%)
Fatty Acids
C14:0 (Myristic)
C14:1 (Myritoleic)
C15:0 (Pentadecanoic)
C15:1 (cis-10-Pentadecenoic)
C16:0 (Palmitic)
C16:1 (Palmitoleic)
C17:0 (Heptadecanoic)
C17:1 (cis-10-Heptadecenoic)
C18:0 (Stearic)
C18:1n9c (Oleic)
C18:1n7
C18:2n6
C18:3n6 (g-Linoleic)
C18:3n3 (a-Linoleic)
C18:4n-3
C20:0 (Arachidic)
C20:1n9 (cis-11-Eicosenoic)
C20:2 (cis-11,14-Eicosadienoic)
C20:3n3 (cis-11,14,17-Eicosatrienoic)
C20:4n6 (Arachidonic)
C20:5n3 (cis-5,8,11,14,17-Eicosapentaenoic)
C22:0 (Behenic)
C22:1n9 (Erucic)
C22:2 (cis-13,16-Docosadienoic)
C23:0 (Tricosanoic)
C22:5n3
C22:6n3 (cis-4,7,10,13,1619-Docosahexaenoic)
Male
November
1.16b
0.16
0.53
1.23a
15.97ab
2.62a
0.87
1.08
7.93
18.30ab
4.29
7.03
0.30
0.96
0.19
0.41
1.34
1.45
7.09ab
0.30
18.40
0.27
0.25
1.78ab
0.19
0.75ab
4.70a
January
0.78ab
0.21
0.77
1.73a
14.57ab
4.28b
0.79
0.91
6.14
18.22ab
5.04
5.63
0.31
0.82
0.26
0.51
1.13
1.98
8.22b
0.35
17.28
0.50
0.22
1.13a
0.31
0.88ab
7.01b
March
0.52a
0.18
0.81
1.72ab
14.22ab
3.57ab
0.75
1.29
5.81
17.45ab
4.27
6.35
0.26
0.84
0.36
0.23
0.47
2.15
9.20b
0.56
18.10
0.44
0.24
0.77a
0.33
1.18b
7.86b
April
1.32b
0.19
0.83
1.50ab
16.22b
3.95ab
0.87
1.01
7.02
19.43ab
4.45
5.08
0.30
0.70
0.19
0.58
1.81
2.07
9.50b
0.28
13.52
0.32
0.20
1.50ab
0.35
0.75ab
5.85ab
May
0.54a
0.19
0.36
1.64ab
13.65a
3.81ab
0.83
0.85
8.23
26.30b
5.16
6.25
0.41
0.60
0.23
0.55
0.42
1.23
5.87a
0.41
12.26
0.68
0.23
4.16b
0.48
0.53a
4.43a
June
0.63a
0.19
0.63
2.28b
15.61ab
3.47ab
0.83
1.03
6.83
15.85a
4.00
6.44
0.26
0.70
0.25
0.26
1.06
2.01
9.13b
0.48
19.42
0.35
0.19
1.25a
0.55
0.79ab
5.44ab
Lower-case letters indicate differences in the same column.
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Table 4. Variation in fatty acid compositions of muscle tissues of female Freshwater crayfish
(A. leptodactylus) (%)
Female
Fatty Acids
C14:0 (Myristic)
C14:1 (Myritoleic)
C15:0 (Pentadecanoic)
C15:1 (cis-10-Pentadecenoic)
C16:0 (Palmitic)
C16:1 (Palmitoleic)
C17:0 (Heptadecanoic)
C17:1 (cis-10-Heptadecenoic)
C18:0 (Stearic)
C18:1n9c (Oleic)
C18:1n7
C18:2n6
C18:3n6 (g-Linoleic)
C18:3n3 (a-Linoleic)
C18:4n-3
C20:0 (Arachidic)
C20:1n9 (cis-11-Eicosenoic)
C20:2 (cis-11,14-Eicosadienoic)
C20:3n3 (cis-11,14,17-Eicosatrienoic)
C20:4n6 (Arachidonic)
C20:5n3 (cis-5,8,11,14,17Eicosapentaenoic)
C22:0 (Behenic)
C22:1n9 (Erucic)
C22:2 (cis-13,16-Docosadienoic)
C23:0 (Tricosanoic)
C22:5n3
C22:6n3 (cis-4,7,10,13,16,19Docosahexaenoic)
November
b
January
a
March
a
April
May
a
a
June
1.13
0.19
0.58
1.59ab
15.46
2.40a
0.81
1.00
7.85
18.18ab
4.25
7.00
0.30
0.91
0.19
0.51
1.30ab
1.45
7.15ab
0.30
0.84
0.15
0.74
1.55ab
13.31
3.28a
0.73
1.12
7.04
20.39ab
4.42
5.41
0.30
0.77
0.22
0.55
0.93ab
2.22
11.11b
0.36
0.88
0.21
0.75
1.72ab
14.44
3.63a
0.89
1.02
7.80
19.10ab
4.39
5.80
0.39
0.72
0.25
0.26
0.52a
2.16
10.50b
0.51
0.84
0.22
0.89
2.87b
17.92
7.34b
0.68
1.05
6.21
17.13a
4.40
7.00
0.30
0.71
0.27
0.48
0.96ab
1.65
5.91a
0.41
0.53
0.20
0.43
1.33a
13.53
3.49a
0.90
0.99
8.42
24.03b
4.94
6.02
0.43
0.90
0.22
0.59
1.74b
1.76
6.57a
0.31
0.68a
0.12
0.59
1.80ab
15.01
3.46a
1.01
0.97
8.16
21.60b
4.25
5.25
0.30
0.86
0.26
0.57
2.08b
1.88
7.28ab
0.37
18.31b
17.20ab
16.34ab
16.82ab
15.21a
15.24a
0.39
0.23
1.69ab
0.29
0.71
0.29
0.22
0.81a
0.49
0.69
0.38
0.28
0.81a
0.49
0.65
0.51
0.23
0.85a
0.28
0.77
0.49
0.31
1.18ab
0.58
0.67
0.52
0.23
2.12b
0.36
0.60
4.59
4.36
5.09
3.15
4.26
4.51
Lower-case letters indicate differences in the same column.
The difference in the C18:1n9c fatty acid is significant in May (24.03%) and June (21.60%)
for the female individuals, while it is significant only in May (26.30%) for the male
individuals (P<0.05). While the difference in the C20:1n9 fatty acid compositions are
significant in May (1.74%) and in June (2.08%) only for the female individuals, this
difference is not significant for the males (P<0.05). The difference in the C20:3n3 fatty acid
compositions is significant in January (8.22%), March (9.20%) and June (9.13%), the
difference is determined significant in January (11.11%) and in March (10.50%) for the
females compared to other months (P<0.05). The difference in the C20:5n3 fatty acid amount
is significant in November (18.31%) only for the females, this difference is not significant for
the males (P<0.05). The difference in the C22:2 fatty acid is found significantly high in May
(4.16%) for the males and in June (2.12%) for the females (P<0.05). The difference in the
C22:5n3 and C22:6n3 fatty acid compositions is significant in respectively March (1.18%),
January (7.01%), March (7.86%) for the male individuals, this difference is not found
significant in the female individuals. It is seen that the monthly difference between the other
fatty acids researched is insignificant for both male and female individuals (P<0.05).
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Figure 1. The values of DYA%, MUFA% and PUFA% for the male individuals.
Figure 2. The values of EPA, DHA and AA for the male individuals.
For the male individuals, the values of DYA%, MUFA% and PUFA% in Figure 1, the values
of EPA, DHA and AA are shown in Figure 2. For the female individuals, the values of
DYA%, MUFA% and PUFA% in Figure 3, the values of EPA, DHA and AA are shown in
Figure 4.
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Figure 3. The values of DYA%, MUFA% and PUFA% for the female individuals.
Figure 4. The values of EPA, DHA and AA for the female individuals.
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Discussion
The levels of fat and protein determine the adaptation character and strategy of a living being
(Vonk, 1960). The levels of fat and protein are effected by many biotic (maturation, breeding
and bait suitability) and abiotic (photoperiod, warmth, pH and dissolved oxygen) factors
(Vonk, 1960).
The crude protein and fat contents in the muscle tissue have constantly increased in the test
during the time of research. The cause for this increase that appeared over time could be the
seasonal changes seen in the bait diversity in pond Yenice. While filament green algae
(Cladophora) and plant wastes become dominant in fall and winter, maggots and fishes are
also seen alongside with algae and plant wastes in spring and summer (Türel Berber, 2013).
Therefore, it might be assumed that plant wastes and Cladophora in fall and winter and
maggots and fishes in the following period might have been consumed. The nutrition acquired
through these resources is sent to the muscle that works as storage for the fats and protein
through blood and to hepatopancreas after being digested (Vonk, 1960).
Every living being require an optimum level of temperature in order to survive. When the
temperature drops lower than this optimum level and/or rises above it, the metabolic activities
of these living beings slow down and thus their energy consumption decreases (BOFC). Such
effects of the temperature might possibly be another reason for the increase in the crude
protein and fat levels over time. Because the value of optimum temperature for the A.
leptodactylus species is 4-22 ºC. The average water temperature in fall and winter in Pond
Yenice, on the other hand, are determined to be respectively 16.2ºC and 7.6 ºC in the studies
by Berber, S. As the water temperatures of both periods are below the optimum value for the
survival of the crayfish, the metabolic activities would slow down and accordingly, there will
be a decrease in the energy consumption. As a result of accumulation of surplus of protein and
fat in the muscle tissue due to such effects of the temperature, such an increase might have
happened over time. The water temperatures in spring and summer in the pond are evaluated
as respectively 15.3 ºC and 25.3 ºC. Along with the increase in the temperatures, there will be
acceleration in the metabolic activities and an increase in the energy consumption. The reason
for the increase of protein and fat components might be that the bait diversity improves as the
temperature rises and thus the living beings that carry relatively more protein and fat than the
algae and plant wastes, such as maggots and fishes, are consumed (Lowery, 1988). There is an
increase in the fat content in the seasonal muscle of the crayfish from November to June. This
is all related to the nutrition and temperature in the pond and it shows a routine increase and
decrease except for the gonad development period.
The period between November and June is the breeding season for the A. leptodactylus
species. Therefore, vitellogenesis, gonad development period and gamete generation, seen in
this period depending on the time, might have caused the biochemical structure of the being to
change. Because proteins and fats are the structural compounds and energy resources of the
embryonic tissues (Vonk, 1960). Hence, the proteins and fats in the storage organs
(hepatopancreas and adiposis) are constantly transferred to the gonad (Güner and
Mazlum,( 2010); Harlıoğlu, Cakmak, Köprücü, Aksu, Harlıoğlu,Yonar, Çakmak, Özcan,
Gündoğdu,(2013)). The Gonadosomatic and Hepatosomatic indexes gain importance since
there is a constant transfer of protein, fat and energy from the hepatopancreas to the gonad.
When the relation between these two is in inverse ratio, it means that the individual reaches
maturity and protein, fat and energy are transferred to the gonad (Güner and Mazlum, 2010).
In a research performed on the C. quadricarinatus species, it is determined that there is an
increase in the protein composition in the gonad as a result of transfer of energy from
hepatopancreas to the gonad and the oocytes through endocytosis in the gonad (Abdu, Davis,
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Khalaila & Sagi, 2002). It is obviously seen that vitellogenesis that happens during the
hepatopancreas breeding season is the primary source of energy, protein and fat which are
essential for the gamete generation and gonad development. If one considers that
hepatopancreas fulfills the needs in this period and the increase in the protein and fat amounts
in the muscle tissues, one might think that breeding has no effect whatsoever on this increase
over time. In this study, the amounts of ash, fat and crude protein decrease in the cold periods
and increase in accordance with the increase of nutrition variety with the temperature.
However, there is a decrease in the breeding season and during the gonad generation. To
increase the survival rate of the offspring acquired at this period, brood stocks should be fed
with nutritionally rich food. In a comparison between the male and female brood stocks, the
fat and protein levels of the females are determined to be the higher. The reason for this is that
the living being always feels the need to store the energy since the gonad generation continues
throughout the year for the females. All vertebrates need polyunsaturated fatty acid (PUFA).
If this need is not fulfilled, there might be some deficiencies in reproduction, development
and growth. Almost all of the vertebrates need linolenic and linolenic fatty acids. The
effective form of the PUFA is generally C20 and C22. Metabolic forms are in the form of;
linolenic acid, linoleic acid, arachidonic acid and docosahexaenoic acid. The various
deviations (linolenic, stearidonic, docosapentanoic acids) of n-3 series fatty acids in the
vertebrates occur as a result of the biological activities of docosahexaenoic and
eicosapentaenoic fatty acids. This situation is more apparent with the fresh-water living
beings (Sargent et al. 1989). In this study, it is determined that the PUFA amount in the
muscle is higher compared to HUFA and DYA. This could be a sign that the crayfish species
used in this study might have a high performance of growth, development and reproduction.
Like in the biochemical composition, the amount of PUFA of female individuals is
determined to be higher than the male individuals. This is considered to occur because the
females need more PUFA due to the gonad generation. EPA rate for the male individuals
from November to June, decreased in April and May and then it stayed around the same level.
On the other hand, a decrease from November to June is observed with the females. This
could be explained with the breeding season and that the gonad generation period has
advanced.
Investigating the DHA rates, it is seen that the male individuals had an increase in January
and March, a decrease in May and an increase starting from June. This is considered to be
about the temperature and the nutrition regime of the male brood stock. DHA is seen less with
the females compared to the males and it decreased to its lowest level in April and then stayed
the same. This makes us think that DHA has no direct effect on reproduction. It is seen that
the AA amount is at the highest level in March, the minimum level in April and that it rises
again. Unlike the males, females have their maximum in March and April and then a decrease
and an increase afterwards. It could clearly be seen that the arachidonic acid has no direct
effects on reproduction. Bulut, (2003) suggested that EPA, DHA and AA are much more
significant in the researches he made about the egg quality and survival rates of some
saltwater fish. The fatty acids stated in our study also show seasonal changes according to the
breeding season. It is determined that this change is about reproduction. MUFA is at its
maximum values in April and May and it stayed as normal in the other months for both males
and females. DYA, however, is identified less in the muscle compared to MUFA and PUFA
and there is not much seasonal difference between the male and female individuals. Bulut,
(2004) in the research he made on the egg quality and biochemical features of sea bass and
bream, stated that compared to MUFA and DYA, PUFA is more significant. The results show
that PUFA, compared to the other fatty acids, is more significant in terms of reproduction and
development for the crayfish as well.
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As a result, the biochemical composition of the living beings and contents are great signs of
continuity and quality of life. The quality of offspring and brood stocks might be improved
using the amounts and rates. This study is quite significant as it forms the structure of future
researches.
Acknowledgements
This study is partially supported by the project titled as "The comparison of Bio-Ecological
and morphometric features of the crayfish (Astacus leptodactylus Eschscholtz, 1823)
population of Yenice Central Irrigation Pond (Çanakkale)" numbered 2007/47.
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D.M. Holdich and R.S. Lowery), Croom Helm Press, pp. 365-400.
Lowery R.S. (1988). Growth, moulting and reproduction In: Holdich DM and Lowery RS (eds). Freshwater
crayfish: biology, management and exploitation. Croom Helm, London, 83–113.
Sargent, J., R.J. & Henderson & Tocher D. R. (1989). The lipids, p. 153-218. In J.E. Halver (Ed.), Fish nutrition
and Academic Press, New York.
Türel, S., & Berber, S. (2013). Kalsiyum İçerikli Yemlerin Tatlısu İstakozları (Astacus leptodactylus
Eschscholtz, 1823) 'nin Büyüme Performansına Etkisi, 3. Ulusal Alabalık Sempozyumu, Kastamonu
Üniversitesi, Su Ürünleri Fakültesi, 24-26 Mayıs.
Vonk, H.J. (1960). Digestion and metabolism. In: The Physiology of Crustacea Vol. I, pp. 291-316. Ed.
Waterman T H. Academic Press, New York.
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THE CHEMICAL COMPOSITION OF THE CRAYFISH (Astacus leptodactylus) IN POND YENICE
Author
Author
TUREL, Selcuk
BERBER, Selcuk
YILMAZ, Sevdan
Abstract
A summary of the resource.
The changes in the compositions of crude protein, fat and fatty acid in the muscle tissues of male and female samples of Astacus leptodactylus acquired by hunting in the dates between November 2007 and June 2008 which is the breeding season for the crayfish from Pond Yenice which is used for irrigation in Çanakkale province, Turkey, are examined in the study. Whereas the amount of crude protein in male samples is around 11.78-15.68%, it is identified that the amount is around 13.09-17.59% with the female samples. Fat changes from 3.29- 4.95% for the male samples, 3.67-5.82% with the female samples. It is observed that there is a continuous increase in the compositions of crude protein and fat generally through the beginning and the end of the sampling period. The amounts of EPA, DHA and AA show change according to the breeding season and season. Keywords: Astacus leptodactylus, Fatty acids, Crude protein, Lipid, Seasonal change.
Publisher
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International Burch University
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A point or period of time associated with an event in the lifecycle of the resource
2014-05-15
Keywords
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Article
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ISSN 978-9958-834-36-3
Q Science (General),QH301 Biology,QH426 Genetics
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https://omeka.ibu.edu.ba/files/original/29f5a1e151b75178cf86ebb170fa5a40.pdf
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PROCEEDINGS
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ISSD 2014
PERFORMANCE ANALYSIS OF FEATURE RANKING ALGORITHMS ON
MICROARRAY DATASETS
Uğur Turhal1, Murat Gök2, Suat Onur3, Sebahattin Babur4
1,2,4
1
Department of Computer Engineering
3
Department of Informatics,
1,3
BalıkesirUniversity
2,4
Yalova University
ugurturhal@balikesir.edu.tr
3
suatonur@balikesir.edu.tr
2
murat.gok@yalova.edu.tr
4
sebahattin_babur@hotmail.com
ABSTRACT
The microarray datasets host a lot of information which influence the problems with different
the degree. Choosing the minimum number of features (attributes) which are representing of
these data structures as an optimization problem. Nowadays, the microarray datasets are
utilized in the diagnose of cancer diseases. However, their size may cause the curse of
dimensionality for machine learning methods during classification(Loris, N. et al., 2012).
Therefore, they need more computing power and long processing times. Hence, reducing the
number of attributes will be fundamental step to solve this problem. In this study, "Colon" and
"Ovarian" datasets which are used frequently in literature were processed with various feature
ranking algorithms. The best “k” number features, which chosen after ranking were classified
with "Naive Bayes” and "SVM(Linear) classifiers. The evaluation of the system was realized
on "Kappa", "MCC" and "Accuracy" scores and "ROC" graphs. This study aims to provide
helpful information to the researchers who work on the same datasets.
Keywords: Microarray datasets, Feature ranking, Naive Bayes, SVM
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INTRODUCTION
DNA microarray technology has proven to be an important breakthrough in molecular
biology. This rapidly maturing technology is providing scientists with a means of monitoring
the expression of genes on a genomic scale(Chee, M.et al. 1996).
Cancer is a broad group of diseases involving unregulated cell growth. In cancer, cells divide
and grow uncontrollably, forming malignant tumors, which may invade nearby parts of the
body. Not all tumors are cancerous; benign tumors do not invade neighboring tissues and do
not spread throughout the body. There are over 200 different known cancers that affect
humans (Cancer Research UK, 2012).
In 2007, cancer caused about 13% of all human deaths worldwide (7.9 million). Rates are
rising as more people live to an old age and as mass lifestyle changes occur in the developing
world (Jemal A, et al. 2011). According to American Cancer Society, about 1,665,540 new
cancer cases are expected to be diagnosed and about 585,720 of them are expected to die in
America, 2014(American Cancer Society, 2014).
The American men-women who died owing to different cancer diseases between 1930 and
2010 are shown in the following figures I-II.
Figure I: Age-adjusted Cancer Death Rates, Males by Site, US, 1930-2010(American Cancer
Society, 2014).
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Figure II: Age-adjusted Cancer Death Rates, Females by Site, US, 1930-2010(American
Cancer Society, 2014).
The microarray data sets host a lot of information which influence the problems with different
the degree. One of important application area is disease prognostication(Golub, T.R. et al.
1999).Hence, choosing the minimum number of features (attributes) which are representing of
these data structures as an optimization problem.
In our former studies, we have improved the performance of classification with using
ensemble classification methods on "Colon" and "Thyroid" microarray datasets(Akbaş, A. et
al. 2013;Babur, S. et al. 2012;Turhal, U. et al. 2013). In this study, “Ovarian” and
"Colon"datasets which are used frequently in literature were processed with various feature
ranking algorithms. The best “k” (150 and 300) number features, which chosen after ranking
were classified with "Naive Bayes" and "SVM(Linear)" classifiers. The evaluation of the
system was realized on "Kappa", "MCC" and "Accuracy" scores and "ROC" graphs.
Finally all results have been compared and best ranking methods and classifiers for each
datasets are shown in the tables.
II.
MATERIAL AND METHODS
In this study, several experiments have been conducted on 2 publicly available datasets.
Below were provided a brief description for each dataset. (the salient features of each dataset
are summarized in Table I):
Table I: Characteristics of the datasets used in the experiments: the first column presents the
number of features (#F), and the second column reports the number of samples (#S)(Loris, N.
et al.2012).
Dataset
Ovarian (O)
Colon (C)
#F
15154
2000
#S
253
62
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Ovarian dataset (O): the ovarian dataset contains 253 samples and two class are considered:
91 samplesare normal and 162 samplesare ovarian cancers(Petricoin,E.F. et al. 2002);
Colon (C): the colon dataset contains 62 samples and two class are considered: 22 samples
are normal and 40samples are tumor cancers(Alon,U. et al.1999);
A. Feature Ranking
Many feature ranking methods are using frequently in literature. However all methods have
advantages and disadvantages while comparing each others. All feature ranking methods that
used in this study are described below;
1. Bhattacharyya
The Bhattacharyya coefficient is an approximate measurement of the amount of overlap
between two statistical samples. The coefficient can be used to determine the relative
closeness of the two samples being considered. It is calculated by following
equation(Djouadi, A. et al. 1990);
(1)
Where,
samples
number of partitions
,
numbers of members of samples
and
in the
partition.
2. T-Test
T-test is one method for testing the degree of difference between two means in small sample.
It uses T distribution theory to deduce the probability when difference happens, then judge
whether the difference between two means is significant (Jiaxi, L. 2010). It is calculated by
following equation;
(2)
Where,
= Average of first set of values
S1 = Standard deviation of first set of values
n2 = Total number of values in first set
= Average of second set of values
S2 = Standard deviation of second set of
values
n2 = Total number of values in second set
3. Wilcoxon
Absolute value of the standardized u-statistic of a two-sample unpaired Wilcoxon test, also
known as Mann-Whitney U test, is a non-parametric test of the null hypothesis that two
populations are the same against an alternative hypothesis, especially that a particular
population tends to have larger values than the other (Wilcoxon, F. 1945).It is calculatedwith
two formulas below (Mann, H.B. and Whitney, D.R. 1947);
(3)
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(4)
Where,
: the sample size for sample 1
: the sample size for sample 2
: the sum of the ranks in sample 1
: the sum of the ranks in sample 2
: observation and the total ranking number
: observation and the total ranking number
for sample 1
for sample 2
B. Feature Selection
In this section, the features of microarray datasets that used in the work are ranked according
to significance level. After that, first k number features are selected and created a new dataset.
Feature selection process is repeated for k=150 and k=300.
C. Classifiers
The classifiers used in this study are described below;
1.
Naïve Bayes
Naive Bayes is the simplest form of Bayes Net. All features are independent from given class
variables. This method is called conditional independency (Zhang, H. 2005).
(5)
2. Support Vector Machines (with Linear Kernel)
The support vector machine or SVM, first described by Vapnik and collaborators in
1992(Boser, B.E. et al. 1992), has rapidly established itself as a powerful algorithmic
approach to the problem of classification within the larger context known as supervised
learning (William H. 2007).
D. Performance Measurement
In order to increase reliability of results, some evaluation methods have been used that found
acceptance in literature. These methods;
1.
Accuracy (Acc)
The accuracy of a measurement system is the degree of closeness of measurements of a
quantity to that quantity's actual (true) value (Taylor, R. 1999). It is calculated by following
equality;
(6)
Where,
Number of real positives
Number of real negatives
Number of unreal positives
Number of unreal negatives
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PROCEEDINGS
Kappa
Cohen's kappa coefficient is a statistical measure of inter-rater agreement or inter-annotator
agreement for qualitative items (Cohen, J. 1960). Bigger difference means better result. It is
calculated by following equality;
(7)
Adding proportion of observed compatibilities for two data,
Probability of emergence by coincidence for this compatibility
Kappa result
3.
Matthews Correlation Coefficient (MCC)
The measure was introduced in 1975 by Matthews (Matthews, B.W. 1975).The Matthews
correlation coefficient (MCC) is using as a measure of the quality of binary (two-class)
classifications.Bigger difference means better result. It is calculated by following equation;
(8)
are explained under the Accuracy header.
4. ROC
It is a method used for showing performance of binary classifier with graphic (Swets, A.
1996). It is calculated by following equation;
(9)
Where,
(10)
(11)
are explained under the Accuracy header.
E. Classification and Results
The datasets that obtained in section B are classified with classifiers which described in
section C. Ten-fold cross-validation method was used during the classification. The obtained
outcomes are shown in the tables.
The accuracy results that obtained by the raw datasets are shown in the Table II.
Table II: The accuracy results of full datasets.(%)
Naive Bayes
SVM (Linear)
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Ovarian
k = 15154
92,4901
100,0000
Colon
k = 2000
53,2258
82,2581
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This results show that Linear SVM is better than the Naive Bayes for each dataset. This is
because the Linear SVM is appropriate to the large size datasets (McCue, R. 2009).
Classification performance results of the best 150 features for each datasets are shown the
tables below. The most effective values are shown bold in a yellow cell.
Table III: Ovarian dataset results (feature count “k” = 150)
Ovarian
NaiveBayes
SVM - Linear
k = 150
Acc (%) MCC Kappa
Acc (%)
MCC
Kappa
bhattacharyya
98,4190
0,966 0,9655
100,000
1,000
1,0000
ttest
97,6285
0,949 0,9480
100,000
1,000
1,0000
wilcoxon
88,5375
0,761 0,7576
99,2095
0,983
0,9829
Table IV: Colon dataset results (feature count “k” = 150)
Colon
NaiveBayes
SVM - Linear
k = 150
Acc (%) MCC Kappa
Acc (%)
MCC
Kappa
bhattacharyya
82,2581
0,656 0,6384
79,0323
0,547
0,5467
ttest
75,8065
0,560 0,5250
80,6452
0,587
0,5857
wilcoxon
72,5806
0,453 0,4411
69,3548
0,352
0,3506
May be reached the following outcomes by referencing the above values;
In all datasets, the highest results for Naive Bayes classifier were obtained by using
bhattacharyya method.
In Ovarian dataset, the highest results of best 150 features were obtained by using Linear
SVM classifier.
The ROC graphs of the above classification results are given below;
Figure III: Ovarian dataset ROC graph (feature count “k” = 150)
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Figure IV: Colon dataset ROC graph (feature count “k” = 150)
The classification results and ROC graphs of first 150 feature are given above. The results of
the best 300 features are given below.
Table V: Ovarian dataset results (feature count “k” = 300)
Ovarian
NaiveBayes
k = 300
Acc (%) MCC Kappa
Acc (%)
MCC
Kappa
bhattacharyya
96,4427
0,923 0,9226
100,0000
1,000
1,0000
ttest
96,8379
0,931 0,9310
100,0000
1,000
1,0000
wilcoxon
83,3992
0,656 0,6514
97,2332
0,941
0,9404
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Table VI: Colon dataset results (feature count “k” = 300)
Colon
NaiveBayes
SVM - Linear
k = 300
Acc (%) MCC Kappa
Acc (%)
MCC
Kappa
bhattacharyya
79,0323
0,628 0,5884
79,0323
0,538
0,5373
ttest
77,4194
0,605 0,5607
82,2581
0,617
0,6164
wilcoxon
62,9032
0,311 0,2849
74,1935
0,436
0,4364
May be reached the following outcomes by referencing the above values;
In both of datasets,the highest results of best 300 features were obtained by using Linear
SVM classifier.
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The ROC graphs of the above classification results are given below;
Figure V: Ovarian dataset ROC graph (feature count “k” = 300)
Figure VI: Colon dataset ROC graph (feature count “k” = 300)
III. CONCLUSION
"Average Accuracy Results Table" is formed with the average of the results which given in
the above tables.The averagedtableis given below;
Table XII: Average Accuracy Results Table (“k” is the number of features)
Average Accuracy Results
Datasets
k
Bhattacharyya
T-Test
Wilcoxon
150
99,2095
98,8145
93,8735
300
98,2214
98,4190
90,3162
150
80,6452
78,2259
70,9677
300
79,0323
79,8388
68,5484
Ovarian
Colon
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Where,
The greencells show the highest average accuracy resultsof the Ovarian dataset.
The bluecells show the highest average accuracy results of theColon dataset.
Above table was created with the averaged results of all classifiers for each method.
Table XIII: Average Accuracy Results Table (“k” is the number of features)
k = 150 Accuracy Results (%)
Wilcoxon (Ovarian)
Naive Bayes
Linear SVM
88,5375
99,2095
(12)
Following conclusions are reached when considering the obtained average accuracy results
Ranked Colon dataset results has been increased in comparison with raw dataset results.
Hence, ranking-selection algorithms are quite useful for this dataset.
Ranked Ovarian dataset results has been decreased a little in comparison with raw dataset
results.Hence, ranking-selection algorithms is useful for the purpose of shorten the
classification duration.
Also, the effect of the Wilcoxon method was observed. This method is quite ineffective for
all used datasets. Hence, it is not useful for these datasets.
At the next works; performance improvement can be realized with using same feature ranking
algorithms and datasets. Also, new feature ranking methods can be used in the work.All
processes can be repeated with less number of features. Roc and Accuracy values can be
increased with using ensemble classifiers. Thus, the advantages and disadvantages of used
each methods can be determined clearly.
IV. REFERENCES
Akbaş, A. et al. (2013) Performance Improvement with Combining Multiple Approaches to Diagnosis of
Thyroid Cancer. The 7th International Conference on Bioinformatics and Biomedical Engineering (iCBBE
2013), Beijing, China.
Alon, U. et al. (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal
colon tissues probed by oligonucleotide arrays. Proc. Natl Acad. Sci. USA,96, 6745–6750.
American
Cancer
Society
(2014).
Cancer
Facts
&
Figures.
http://www.cancer.org/research/cancerfactsstatistics/cancerfactsfigures2014/index
Retrieved
from
Babur, S. et al. (2012) Dvm Tabanlı Kalın Bağırsak Kanseri Tanısı İçin Performans Geliştirme. Eleco 2012
Elektrik-Elektronik ve Bilgisayar Mühendisliği Sempozyumu, 425-428.
Boser, B. E. et al. (1992) A training algorithm for optimal margin classifiers. Proceedings of the fifth annual
workshop on Computational learning theory(COLT), 144.
Cancer Research UK (2011, May 11) How many different types of cancer are there? CancerHelp UK.
Chee, M. et al. (1996) Assessing genetic information with high-density dna arrays. Science,274, 610–614.
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Cohen, J. (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement,
20 (1), 37-46.
Djouadi, A. et al. (1990) The quality of Training-Sample estimates of the Bhattacharyya coefficient. IEEE
Transactions on Pattern Analysis and Machine Intelligence.12 (1), 92–97.
Golub, T.R. et al. (1999) Molecular classification of cancer: class discovery and class predition by gene
expression monitoring. Science. 286, 531–537.
Jemal, A. et al. (2011) Global cancer statistics. CA: a cancer journal for clinicians,61 (2), 69–90.
doi:10.3322/caac.20107. PMID 21296855.
Jiaxi, L. (2010) The Application and Research of T-test in Medicine. Networking and Distributed Computing
(ICNDC).
Loris, N. et al. (2012) Combining multiple approaches for gene microarray classification. Oxford University
Press, 28 (8), 1151-1157.
Mann, H.B. and Whitney, D.R. (1947) On a test of whether one of two random variables is stochastically larger
than the other. Annals of Mathematical Statistics, 18, 50-60.
Matthews, B. W. (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme.
Biochimica et Biophysica Acta (BBA) - Protein Structure 405 (2), 442–451.
McCue, R. (2009) A Comparison of the Accuracy of Support Vector Machine and Naive Bayes Algorithms In
Spam Classification. University of California at Santa Cruz, Nov 29.
Petricoin, E.F. et al. (2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 359, 572–577.
Swets, A. (1996) Signal detection theory and ROC analysis in psychology and diagnostics. Lawrence Erlbaum
Associates, Mahwah, NJ.
Taylor, R. (1999) An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements.
128–129.
Turhal, U. et al. (2013) Performance Improvement for Diagnosis of Colon Cancer by Using Ensemble
Classification Methods. The International Conference on Technological Advances in Electrical, Electronics and
Computer Engineering (TAEECE 2013), Konya, Turkey.
Wilcoxon, F. (1945) Individual comparisons by ranking methods. Biometrics Bulletin,1, 80-83.
William, H. et al. (2007) Support Vector Machines Numerical Recipes: The Art of Scientific Computing (3rd
ed.). Cambridge University Press, New York.
Zhang, H. (2005) Exlporing Conditions for the Optimality of Naive Bayes. International Journal of Pattern
Recognition and Artificial Intelligence,19 (2), 183-192.
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Uğur TURHAL was born in Trabzon, Turkey in 1988. He was graduated with Bachelor’s degree
from Marmara University in 2011. He is a graduate student in the Computer Engineering Department
of Yalova University, Turkey. Also, He is working as a computer specialist at Balikesir University,
Turkey. Interested areas are; Bioinformatics, Signal Processing, Microarray Datasets, Cancer
Dieseases
Murat GÖK performed a Master in Computer Sciences at Mugla University (Turkey). After his
Master thesis on the decision support systems, he began in 2006 a PhD in Computer Sciences at
Sakarya University (Turkey). In June 2011, he defended his PhD thesis entitled “Prediction of HIV-1
Protese Cleavage Sites with New Techniques”. Having completed his PhD, he became an assistant
professor at the department of computer engineering on Yalova University(Turkey).His research
interests are bioinformatics, machine learning algorithms and theories, computer programming. He has
several papers on bioinformatics. He currently has several master students.
Suat ONUR was born in Kütahya, Turkey in 1972. He was graduated with Bachelor’s degree from
Gazi University in 1995. He is a graduate student in the Electric-Electronic Engineering Department
of Balikesir University, Turkey. Also, He is working as a lecturer at Balikesir University, Turkey.
Interested areas are; Bioinformatics, Internet Programming, Embedded Systems
Sebahattin BABUR was born in Bursa/Turkey in 1988. He was graduated with Bachelor degree in
2011 from Marmara University. He is a graduate student in the Computer Engineering Department of
Yalova University, Turkey. Also, he has been working as Technical Sales Engineer for 1 year at
Beckhoff Automation Company, Turkey. His areas of interest: Solution of Bioinformatics Problems,
Image Processing, The Design of Electronic Circuits, Industrial Automation Technology
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Dublin Core
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2529
Title
A name given to the resource
PERFORMANCE ANALYSIS OF FEATURE RANKING ALGORITHMS ON MICROARRAY DATASETS
Author
Author
TURHAL, Ugur
GOK, Murat
ONUR, Suat
BABUR, Sebahattin
Abstract
A summary of the resource.
205 | P a g e PERFORMANCE ANALYSIS OF FEATURE RANKING ALGORITHMS ON MICROARRAY DATASETS Uğur Turhal1, Murat Gök2, Suat Onur3, Sebahattin Babur4 1,2,4Department of Computer Engineering 3Department of Informatics, 1,3 BalıkesirUniversity 2,4 Yalova University 1 ugurturhal@balikesir.edu.tr 2 murat.gok@yalova.edu.tr 3 suatonur@balikesir.edu.tr 4 sebahattin_babur@hotmail.com ABSTRACT
Publisher
An entity responsible for making the resource available
International Burch University
Date
A point or period of time associated with an event in the lifecycle of the resource
2014-05-15
Keywords
Keywords.
Article
PeerReviewed
Identifier
An unambiguous reference to the resource within a given context
ISSN 978-9958-834-36-3
Q Science (General),QH301 Biology,QH426 Genetics
-
https://omeka.ibu.edu.ba/files/original/46cf36473f341152b9c8b879629bbbea.pdf
261564c39f9a8c08497e404894e72193
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PROCEEDINGS
th
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COMPARASION OF WIND MEASUREMENTS BY LIDAR AND MEASUREMENT
MAST FOR BORA WIND IN BOSNIA AND HERZEGOVINA
Elvir Zlomušica, Suad Zalihić, Jasmin Bejdić
University „Džemal Bijedić“ of Mostar
elvir.zlomusica@unmo.ba, suad.zalihic@unmo.ba, jabc@cowi.dk
ABSTRACT
Research of wind energy potential with the aim of installing wind turbines was performed on
location Hrgud in the southeastern part of Bosnia and Herzegovina. These are complex
terrains characterized by specific wind Bora. Measurements were performed by standard
procedures using the classical standard instruments, anemometers and wind vanes, mounted at
different heights on the measurement mast height of 77.5 m (agl) combined with remote
sensing technique such as the LIDAR (LIght Detection And Ranging) during period 23.8.
2013. – 19.12. 2013. The aim of this study was to perform an analysis and comparison of
collected measurement data from the measurement mast and the LIDAR (Windcube v2 FCR),
as well as the behavior of the equipment itself in the complex terrain and wind Bora
conditions in Bosnia. During the comparison the 10-min averaging time for the wind speed
and direction is used. It can be concluded from this analysis that the behavior of the LIDAR
under harsh local weather conditions was relatively well, except the problems with the power
supply. The LIDAR uncorrected wind speed was in general lower than the wind speed
measured by the cup anemometer at the same height. The comparison of the data of
measurement methods provides reliable information on the wind speed within the considered
altitude range.
Keywords: wind energy, LIDAR, wind Bora, Bosnia
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1. INTRODUCTION
The first measurements with adequate equipment and technology aimed at determining of the
wind energy potential in Bosnia started in 2002 at the location of Podveležje (Mostar). Thanks
to analysis of data from different measurements campaigns the area of south Bosnia and
Herzegovina has been recognized as an interesting region for wind power production. At the
moment, it is impossible to discuss precisely about the real potential for the wind farm
construction. Research is still incomplete and limited by the complexity of terrain and by the
wind Bora. The Bora is a strong cold katabatic wind which mostly blows from north to north
– east, starts suddenly and decelerate slow. There are anti - cyclonal (dry) and cyclonal (with
clouds) Bora. Furthermore, there are several conditions needed for Bora (mountain massifs,
different values of temperature and pressure in the heights and lowlands, etc.). Also, there has
not been enough studies or research conducted on Bora in Bosnia and Herzegovina.
According to the rough estimation, the economically feasible potential should be around 1
000 – 1 200 MW (Zlomušica & Behmen, 2003; Ćatović, Behmen, & Zlomušica, 2004;
Zlomušica, 2010). In any case, it would be a success to install 50 MW before the year of 2015
(Zlomušica, 2013).
Detailed knowledge of the wind resource is necessary in the developmental and operational
stages of a wind farm site (International Standard, IEC 61400-12-1 Ed. 1., 2005). As wind
turbines continue to grow in size, measurement masts for mounting cup anemometers (the
standard procedure for wind resource assessment) have become much taller, and much more
expensive.
The LIDAR is ground-based and can work over one hundred of meters, sufficient for the tall
wind turbines. The use of LIDAR in complex terrain is very attractive for wind site
assessments since a grinding installation of a high mast can be avoided. The measurement
campaigns in some projects showed very promising results (Albers, Janssen, & Mander, 2008;
Bingöl, Mann, & Foussekis, 2008; Bourgeois, Cattin, Locker, & Winkelmeier, 2008;
Bourgeois, Cattin, Winkelmeier, & Locker, 2009; Krishnamurthy, Boquet, & Machta, 2014).
Some strengths of the LIDAR are: relatively easy to deploy, still some fingering with cables
and tubes, installed by one or two persons in half a day, withstanding harsh climatic
conditions, low power consumption and no noise, while some weaknesses of the LIDAR are:
uncertainty of accuracy of wind speed data in complex terrain, very expensive high-tech
instrument, affected by rain and low clouds.
However, to the present day it is not recommended to use a LIDAR as a stand alone
instrument for accurate wind measurements. More validation studies and comparisons are
needed and data retrieval algorithms (vertical wind speed, turbulence) have to be improved.
Furthermore, the assumption of a homogeneous flow field used by the LIDAR technology has
to be considered in the data analyses, especially in complex terrain. In the next period the
standard (IEC 61400-12-1 Ed. 1., 2005) is expected to be changed, and a new standard will
include remote sensing techniques like the LIDAR.
The aim of the work is to compare the wind measurements from commercial LIDAR
instrument against an instrumented mast, in complex terrain, where many wind farms are now
being installed worldwide, as well as equipment behavior under harsh meteorological
conditions at the locality of Hrgud (southeast of Bosnia and Herzegovina). This equipment
has been used first time in Bosnia and Herzegovina. Measurements performed during the
summer-fall period of 2013 in the duration of four months.
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2. METHOD AND MATERIALS
A four-months measurement campaign with an LIDAR and 78.5 m mast, which provided also
has long term data, for evaluating the remote sensing instruments, was performed. The
LIDAR was connected directly to the electrical grid via the local power line. However this
power line has been hit by lightings several times during the measurement campaign, which
has destroyed the 220V to 24V LIDAR convertor.
2.1 Site description
The measurement site called Hrgud in Bosnia and Hercegovina is situated approximately 35
km southeast of the city of Mostar or 5 km east from the town of Stolac, 10 - 11 km west from
the town of Berkovići, 50 km east from the Adriatic sea coast, Figure 1. The area can be
categorized as complex, with altitudes varying between 960 to 1110 m asl (above sea level)
and is approximately 5 km2. A southeast – northwest fault delimiting the plateau is
characterised with a very steep slope, which have a significant influence at the wind flow at
the site. The terrain is characterized by karsts with small meadows, bushes and low forest
vegetation.
Figure 1. Location of the Hrgud site in Bosnia and Herzegovina
Figure 2 shows the wind rose (frequency) for the Hrgud site of filtered data at 77.5 m agl for
the met mast at Hrgud site. The met mast was installed according to the standard IEC 6140012-1 (IEC 61400-12-1 Ed. 1., 2005) and MEASNET (Measuring Network of Wind Energy
Institutes) (Measnet, 2009). It can be seen that the prevailing wind directions are NNE and
SSE and consequently the most of the wind energy comes from these directions.
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Figure 2. Frequency and energy roses at 77.5 m for the 12 months of wind data
at the met mast Hrgud
Site conditions parameters for the met mast height 77.5 m are: max 10-min. measured wind
speed is 31.4 m/s, max 3-sec. measured wind speed is 38.7 m/s, annual mean temperature is
10.2 °C, annual min. temperature is –10 °C and annual mean air density is 1.083 kg/m3
(Impro-Impex & COWI, 2013).
2.2 Measurement configuration
The instruments were installed at a height 1098 m asl. The instruments site coordinates are X:
258 392, Y: 4 776 046 of UTM WGS84 projection, according to the resolution of the GPS
device. The instruments were located on 100 m high hill about 1.5 km North of a 1000 m
deep and 2 km wide canyon. The hill is about 1 km long and 100 m wide, oriented E-W.
The LIDAR was positioned approximately 1.5 m from measuring mast, Figure 3. The sensor
height in the LIDAR is 1 meter above mast ground level. Therefore 1 meter shall be added to
the entered heights to get the actual measuring height. The LIDAR measurement started on 23
August 2013. The system consists of a Windcube v2 LIDAR, set up to measure the windspeed
at 10 different heights. The LIDAR is powered by 220 V supplied from the commercial grid.
Data from the LIDAR shall be used as supplement to data from Hrgud mast (as long term
data), in order to give a better assessment of the wind conditions on the site.
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Figure 3. The measurement site Hrgud, LIDAR (left) mast (right)
Windcube v2 LIDAR equipped with FCR (Flow Complexity Recognition) for direct wind
measurements in complex terrain was used in this campaign. The height range of this
instrument is from 40-200 m, data sampling rate is 1 sec.
In Table 1 the measurement configuration and measurement periods of the met mast and the
LIDAR are shown.
Table 1. Measurement configuration
Measurement height of
wind speed (m)
Measurement height of
wind direction (m)
Measurement period
Mast, cup
anemometers, Thies
Classic, wind vanes
Thies Compact
30; 55; 55; 75; 77.5
53 and 75
30 July 2012 – up to date
LIDAR
Windcube v2
44; 54; 64; 74; 77; 79; 89;
119; 129; 159
44; 54; 64; 74; 77; 79; 89;
119; 129; 159
23 August 2013 – 19
December 2013
3. RESULTS
3.1 Analyse of the data availability
The analyzed period for this study started 23 August 2013 and ended 19 Decebmer 2013. The
time series of the wind speed measured with the top cup anemometer at 77.5 m and the
LIDAR with and without FCR at 78 m are displayed in Figure 4.
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Figure 4. Time series of the wind speed measured with the top cup anemometer (black), the
LIDAR without correction (red) and the LIDAR with FCR (green)
The LIDAR measurements were interrupted several times during measuring campaing. The
main events causing interruption in the measurements are summarized in Table 2.
Table 2. Periods improper functioning of the LIDAR
Beginning
29.8.2013
9.10.2013
20.10.2013
25.10.2013
5.11.2013
23.11.2013
End
3.10.2013
20.10.2013
23.10.2013
30.10.2013
17.11.2013
23.11.2013
Explanation
FCR accidentally turned off
Power supply damaged by lighting
Complete std data files, but incomplete FCR files (only 1 value/day)
Complete std data files, but incomplete FCR files (only 1 value/day)
Power supply damaged by lighting
End of reliable measurements; LIDAR system damaged by lighting
Much more LIDAR uncorrected wind speed data (non-corrected for the terrain effect) were
collected than FCR corrected data.
3.2 Comparison of the measured wind speeds
The comparison of the measured wind speeds between the cup anemometer at 77.5 m and the
uncorrected LIDAR measurements and the FCR corrected LIDAR data at 78 m, respectively,
for the same dataset is shown in Figure 5 and Figure 6 with scatterplots and the evaluated
regression and correlation coefficients.
Dataset including uncorrected LIDAR wind speed data with an availability above 80% (Red:
two parametric linear regression; Blue: one parametric linear regression forced through 0).The
uncorrected LIDAR wind speed measurements underestimate the cup anemometer by 4.1%
on average, Figure 5.
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Figure 5. Uncorrected LIDAR wind speed at 78 m vs cup anemometer wind speed at 77.5 m
The FCR corrected LIDAR wind speed measurements overestimate the cup anemometer by
1.5% on average. The correlation coefficient is higher for the FCR corrected data than for the
uncorrected measurement. Dataset including FCR LIDAR wind speed data with an
availability above 80% (Red: two parametric linear regression; Blue: one parametric linear
regression forced through 0), Figure 6.
Figure 6. FCR LIDAR wind speed at 78 m vs cup anemometer wind speed at 77.5 m
Similar comparisons between LIDAR wind speeds (uncorrected and FCR corrected) were
done at 75 m and 55 m. The comparison results are similar to those at 77.5 m (Wagner &
Bejdić, 2014).
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3.3 Comparison of the measured wind direction
Direction measurements were taken at 53 m and 75 m with Thies Compact wind vanes, but
the wind vane at 75 m got broken during the measurement campaign, therefore the wind vane
at 53 m was used in the analysis.
The direction measured by the LIDAR at 55 m was compared to the direction from the wind
vane at 53 m. The comparisons of the LIDAR uncorrected direction and the FCR corrected
direction are not identical but very similar (Figure 7 and Figure 8). In both cases, the linear
regression results in an offset of about 110.
Figure 7. Uncorrected LIDAR wind direction at 55 m vs wind vane at 53 m (Red: two
parametric linear regression) – Wind speed below 3 m/s for this comparison
Figure 8. FCR LIDAR wind direction at 55 m vs wind vane at 53 m. (Red: two parametric
linear regression) - Wind speed below 3 m/s for this comparison
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3.4 Wind Shear
The wind shear or vertical wind speed profile is an important parameter in the choice of the
optimal hub height of wind turbine. The wind shear expresses the ratio between the wind
speeds at different heights. The wind shear, of course, depends on the topography of the site
and is not identical for all directions. The power law wind shear is defined by (International
Standard. IEC 61400-1 Ed. 3., 2005):
v2 v1 h2 h1
(1)
where the shear exponent α is calculated between the respective heights h1 and h2 and their
corresponding wind speeds v1 and v2.
The average wind speed profiles for the four prevailing wind sector measured with the mast
between 30 and 77.5 m and with the LIDAR between 45 and 160 m are displayed in Figure 9.
The wind sector is indicated at the top of each plot and with the number of data within that
sector in parenthesis. The vertical wind speed profiles of LIDAR and mast agree very well for
the NE sectors 0°-30°, 30°-60° and SE sectors 120°-150°, 150°-180°. However it was noticed
in Figure 9 that the averaged profiles do not typically follow a power law.
Figure 9. Average wind speed profile for the prevailing wind directions of mast data (black),
uncorrected LIDAR (red) and FCR corrected LIDAR (green)
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4. CONCLUSIONS
The LIDAR functioned “relatively well” in complex conditions of the terrain and wind
characteristics of Bora. Unfortunate events demonstrate the importance of protecting the
power supply of the LIDAR from an exposed power line. The LIDAR software also happened
to be unstable, since several periods of data were missing although the system had power.
The LIDAR uncorrected wind speed was in general lower than the wind speed measured by
the cup anemometer at the same height by about 4%. The FCR corrected wind speeds were
higher than the cup anemometer wind speed by about 1.5%.
Based on a rough analysis of the surrounding topography, it seems that the LIDAR deviation
is mainly affected by the topography around the LIDAR within a radius of 500 m but the
major features of the topography within a larger radius has also some influence. This would
need to be further investigated with numerical tools (CFD).
The LIDAR uncorrected wind directions compared well to the wind vane and the FCR
correction had no significance influence on this comparison.
The shear exponent derived from the LIDAR wind speeds, both without and with FCR,
compared relatively well with the shear exponent form the mast cup anemometers.
For more relevant observance of Bora characteristics and behavior of the equipment in a
complex location like this one, it is necessary to carry out measurements in a longer period of
time and in different seasons.
ACKNOWLEDGEMENT
This work was performed under project Wind Measurement Program in RS, Bosnia & Herzegovina.
The authors are grateful for the considerable technical support from the staff of the COWI A/S and
Impro Impex doo.
REFERENCES
Zlomušica, E., & Behmen, M. (2003). Methodological approach to the selection of wind farm location.
Proceeding of 12th International Symposium on Power Electronics, Ee, Novi Sad, Serbia.
Ćatović, F., Behmen, M., & Zlomušica, E. (2004). Trends in the Development of the Electric Power Systems
Based on Wind energy in World and in Bosnia and Herzegovina. Journal of Environmental Protection and
Ecology-Official Journal of the Balkan Environmental Association (B.EN.A), 5(4), 836-840.
Zlomušica, E. (2010). Wind Energy Resources in Bosnia and Herzegovina. Thermal Science, 14(1), 255-260.
Zlomušica, E. (2013). Particular Review on SODAR and LIDAR Measurements of Bora Wind in Mostar, Bosnia
and Herzegovina, International Journal of Engineering & Technology IJET-IJENS, 13(6), 53-61.
International Standard. IEC 61400-12-1 Ed. 1. (12/2005). Power performance measurements of electricity
producing wind turbines.
Albers, A., Janssen, W., & Mander, J. (2008). Comparison of LIDARs, German test station for remote wind
sensing devices. German Wind Energy Conference, DEWEK, Bremen, Germany.
Bingöl, F., Mann, J., & Foussekis, D. (2009). Lidar Performance in Complex Terrain Modeled by WASP
Engineering. European Wind Energy Conference & Exhibition, EWEC, Marseille, France.
Bourgeois, S., Cattin, R., Locker, I., & Winkelmeier, H. (2008). Analysis of the vertical wind profile at a BORA
− dominated site in Bosnia based on SODAR and ZephIR LIDAR measurements. European Wind Energy
Conference & Exhibition, EWEC, Brussels, Belgium.
Bourgeois, S., Cattin, R., Winkelmeier, H., & Locker, I. (2009). CFD Modeling of the vertical wind profile and
the turbulence structure above complex terrain and validation with SODAR and LIDAR measurements.
European Wind Energy Conference & Exhibition, EWEC, Marseille, France.
Krishnamurthy, R., Boquet, M., & Machta, M. (2014). Turbulence Intensity Measurements from a Varity of
Doppler LIDAR. European Wind Energy Conference & Exhibition, EWEA, Barcelona, Spain.
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Measnet. (2009). Evaluation of site-specific wind conditions – Version 1.
Impro-Impex, COWI. (2013). Hrgud – 12 Months Wind Study and Site Conditions Report.
Wagner, R., & Bejdić, J. (2014). Windcube + FCR test in Hrgud, Bosnia & Herzegovina - Final report. DTU
Wind Energy.
International Standard. IEC 61400-1 Ed. 3. (08/2005). Wind Turbines - Part 1: Design Requirements.
Elvir Zlomušica was born in 1971 in Mostar (Bosnia). Since 2000 he has been employed at the
University. In 2006 he got a PhD degree in technical sciences from University „Džemal Bijedić“ of
Mostar. He is the co-author of a book and author or co-author of more than 40 articles and scientific
papers presented in conferences and seminars, home and abroad. He was involved in realization of
over 20 research projects related to production process. Since October 2011 he has been employed as
an associate professor. Since April 2012 he has performed the function of a vice-rector of the
University „Džemal Bijedić“ of Mostar. His research interests include renewable energy sources, wind
energy.
Suad Zalihić was born in 1960 in Mostar (Bosnia). Since 2010 he has been employed at the
University. In 2013 he got a MSc degree in technical sciences from University „Džemal Bijedić“ of
Mostar. He participated in the implementation of many projects related to wind potential research at
various locations in Bosnia and Herzegovina. His research interests include wind energy and wind
loads on various structures.
Jasmin Bejdić was born in 1985 in Banja Luka (Bosnia). He holds a MSc degree in Wind Energy
from Technical University of Denmark. He has been working with wind energy since 2006, first for an
Danish wind energy developer/investor European Energy A/S and since 2011 as a consultant in COWI
A/S. Bejdić has great world wide experience wind measurements and application of wind data for
wind farm development.
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2531
Title
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COMPARASION OF WIND MEASUREMENTS BY LIDAR AND MEASUREMENT MAST FOR BORA WIND IN BOSNIA AND HERZEGOVINA
Author
Author
ZLOMUŠICA, Elvir
ZALIHIĆ, Suad
BEJDIĆ, Jasmin
Abstract
A summary of the resource.
Research of wind energy potential with the aim of installing wind turbines was performed on location Hrgud in the southeastern part of Bosnia and Herzegovina. These are complex terrains characterized by specific wind Bora. Measurements were performed by standard procedures using the classical standard instruments, anemometers and wind vanes, mounted at different heights on the measurement mast height of 77.5 m (agl) combined with remote sensing technique such as the LIDAR (LIght Detection And Ranging) during period 23.8. 2013. – 19.12. 2013. The aim of this study was to perform an analysis and comparison of collected measurement data from the measurement mast and the LIDAR (Windcube v2 FCR), as well as the behavior of the equipment itself in the complex terrain and wind Bora conditions in Bosnia. During the comparison the 10-min averaging time for the wind speed and direction is used. It can be concluded from this analysis that the behavior of the LIDAR under harsh local weather conditions was relatively well, except the problems with the power supply. The LIDAR uncorrected wind speed was in general lower than the wind speed measured by the cup anemometer at the same height. The comparison of the data of measurement methods provides reliable information on the wind speed within the considered altitude range. Keywords: wind energy, LIDAR, wind Bora, Bosnia
Publisher
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International Burch University
Date
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2014-05-15
Keywords
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PeerReviewed
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ISSN 978-9958-834-36-3
Q Science (General),QH301 Biology,QH426 Genetics
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https://omeka.ibu.edu.ba/files/original/cc17ded3e35c7eb6b59854acfcd88c10.pdf
e0e811f9d5c8301839c7b54f647f6f48
PDF Text
Text
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DIGITAL FORENSIC INVESTIGATION, COLLECTION AND PRESERVATION OF
DIGITAL EVIDENCE
Vahidin Đaltur , Kemal Hajdarević,
Internacional Burch University, Faculty of Information Technlogy
71000 Sarajevo, Bosnia and Herzegovina
Vahidin.dzaltur@gmail.com
ABSTRACT
With computers, and other electronic devices being involved in an increasing number, and
type, of crimes the electronic trace left on electronic media can be a vital part of the legal
process. To ensure acceptance by courts, accepted processes and procedures need to be
acquired and demonstrated which are not dissimilar to the issues surrounding traditional
forensic investigations. Forensic technology makes it possible to: identify privacy issues;
establish a chain of custody for provenance; employ write protection for capture and transfer;
and detect forgery or manipulation. It can extract and mine relevant metadata and content;
enable efficient indexing and searching by curators; and facilitate audit control and granular
access privileges. In recent years, digital forensics has emerged as an essential source of tools
and approaches for facilitating digital preservation and curation, specifically for protecting
and investigating evidence from the past. Institutional repositories and professionals with
responsibilities for personal archives can benefit from forensics in addressing digital
authenticity, accountability and accessibility. Digital personal information must be handled
with due sensitivity and security respecting available standards while demonstrably protecting
its evidential value. A digital forensic investigation is a special case of a digital investigation
where the procedures and techniques that are used will allow the results to be entered into a
court of law. Computer forensics is a new and fast growing field that involves carefully
collecting and examining electronic evidence that not only assesses the damage to a computer
as a result of an electronic attack, but also to recover lost information from such systems to
prosecute criminals. With the growing importance of computer security today and the
seriousness of cyber-crime, it is important for computer professionals to understand the
technology used in computer forensics.
Keywords: Computer forensics, image acquisition, digital preservation, data recovery
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1. What Is Computer Forensics?
Computer forensics is the practice of collecting, analyzing and reporting on digital
information in a way that is legally admissible. It can be used in the detection and prevention
of crime and in any dispute where evidence is stored digitally. Computer forensics follows a
similar process to other forensic disciplines, and faces similar issues. Purpose is to give
answer to questions of a legal system related to computers. Any sort of legal issue, trial, some
sort of civil court cases or any other legal processing that has computer involved.
“Computer forensics usually refers to the forensic examination of computer components and
their contents such as hard drives, compact disks, and printers.” (Eoghan Casey, 2011).
2. Preparing for an investigation
Before we start with forensic investigation, we want to be sure that we understand the scope
of investigation. In order to understand what pieces of evidence we are looking for, what
elements are in play, what will move case forward in order to understand the truth what
happened? In understanding the scope of investigation we will get what evidence do we need
to acquire and what evidence do we have authority to acquire. There are cases where we may
find information that we don’t actually have authority to acquire and obtain. After we
determined scope of investigation, we must understand the type of investigation we are going
to conduct.
Live acquisition
o Do we need what’s in memory?
o Do we need network state?
Static acquisition
o Files
o Programs
The type of investigation is important so it has to be determined do we need that system up
and running in order to do live acquisition, or we just need hard drive or other storage device
in order to do static acquisition. Next step is to provide evidence storage in places, to store
disk drives, USB stick, and any type of memory card or a PC. We must have a place where
we can store them securely, with limited access or no access to other person at all. Also, we
need place to store digital artifacts where we can store image files of evidence that can’t be
tampered with. Along with those lines we need to be sure how documentation will be look
like. We must have a chain of evidence and evidence verification data (hash values).
Ultimately we need to be able to control and document everything that was happening with
evidence from the point that we required to the point that we handled off or presented
testimony for the evidence.
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3. Forensic Workstation
If we are doing a lot of a forensic investigation or forensic examination, we definitely want to
have dedicated forensic work station. First we have to build a hardware configuration, chose
different types of interfaces, USB, FireWire, SCSI and so on. Other decision we have to make
is what operating system we are going to run on that working station. One of the choices is to
run a “LIVE CD”, because in this way we actually storing anything in primary hard drive,
nothing is writable at that regard and we are not making any changes.
“Primary these live CD-s are mostly Linux based and there are several available for forensic
workstation usage” (Christopher L.T. Brown, 2009).
One of the advantage for using Linux or UNIX like operating systems are number of tools
that are built in. Also we have a lot of forensic programs that run only on a Windows. Some
of the best free digital forensic investigation tools are:
ProDiscover Basic is, indeed, a professional tool for consultants, system
administrators and investigators, giving them the information required to build strong
legal cases.
The Sleuth Kit (+Autopsy), are open source digital investigation tools (a.k.a. digital
forensic tools) that run on Windows, Linux, OS X, and other Unix systems. They can
be used to analyze disk images and perform in-depth analysis of file systems (such as
NTFS, FAT, HFS+, Ext3, and UFS) and several volume system types.
FTK Imager, is a simple but concise tool. It saves an image of a hard disk in one file
or in segments that may be later on reconstructed. It calculates MD5 hash values and
confirms the integrity of the data before closing the files. The result is an image file(s)
that can be saved in several formats including, DD raw.
DEFT (Linux LIVE CD), (acronym for Digital Evidence & Forensics Toolkit) is a
distribution made for Computer Forensics, with the purpose of running live on
systems without tampering or corrupting devices (hard disks, pen drives, etc…)
connected to the PC where the boot process takes place.
CAIN, Is a password recovery tool for Microsoft Operating Systems. It allows easy
recovery of various kinds of passwords by sniffing network, cracking encrypted
passwords using dictionary, brute-force and cryptanalysis attack, recording VoIP
conversation, decoding scrambled password, recovering wireless network keys.
4. Image Acquisition
Image format is the way that data from a hard drive or hard drive partitions is stored in the
way they can be analyzed later on. There is a several way to acquire a disk image. Also, they
are a couple a different ways to store that disk image once we are acquired a data so that they
can be used without that actually having to use a hard drive. One of really important ways of
storing that is advanced forensic format (AFF). It is able not only to store data from a hard
drive, but also it can store some forensic Meta data along with them. AFF format is supported
from most of the primary forensic tools like Sleuth Kit and FTK. Another image format used
in Linux based OS is a RAW image. It’s a bit for bit copy whether it is a hard drive or a
particular partition, exactly the way it was on that physical media, but it’s stored in the file.
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“When collecting the bit-stream image to file, the investigator will essentially access the data
through this method; stream the data sector by sector from the evidence media into a file or
group of files residing elsewhere.” (Christopher L.T. Brown, 2009).
4.1 Image Acquisition Under Linux
Under Linux, we have advantage of built in tools that will allows as to do image capture.
Name of the tool which is most often in use is dd, and it’s comes with majority of Linux
distributions available today. It can be used for various digital forensic tasks such as:
Creating a raw image file (a bit for bit) from drive or partition
the basic syntax is:
dd if=/dev/sdb1 of=/home/vahidin/newimage.dd bs=512 conv=noerror,
sync
where if = input file ( in our case drive)
of = output files
bs = block size
conv = conversion options
Forensically wiping a drive or partition ( zero out a drive)
the basic syntax is:
dd if=/dev/zero of=/dev/sdb1 bs=1024tem
where if = input file
of = output files
bs = block size
We can find a modified version of dd such as dcfldd or dc3dd, with additional features that
were added specifically for digital forensic acquisition tasks. The dd is a very powerful tool
that can have devastating effects if not used with care. It is recommended that you experiment
in a safe environment before using this tool in the real world.
4.2 Image Acquisition Under Windows
One of the most popular Windows imaging tools is “FTK Imager (Forensic Tool Kits)”. FTK
Imager is a data preview and imaging tool that allows as to examine files and folders on local
hard drives, USB sticks, network drives, CDs/DVDs, or any other media card and review the
content of forensic images or memory dumps. Using FTK Imager we can also create SHA1 or
MD5 hashes of files, export files and folders from forensic images to disk, review and recover
files that were deleted from the Recycle Bin (providing that their data blocks haven’t been
overwritten), and mount a forensic image to view its contents in Windows Explorer.
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4.3 Volatile Information
As we are doing an investigation, sometimes we have use different systems which have to be
up and running and is actively in use. Volatile system information’s is capturing particular
information from this system before its shutdown, because when its shutdown the system’s all
information will vanish or disappear. One type of volatile information is logon session where
we can find information about user and services used at any given point of time. One of the
commonly used software is named ProDiscover.
“Using ProDiscover’s expanded live memory imaging and processed volatile data extraction,
investigators can learn more about the target system’s interaction within the running
environment and find passwords and memory-only resident malware” (Harlan Carvey, 2012).
Another interesting thing is processes that are running at any given time on a system. This
information is usually retained in memory while the system is operating and tends to
disappear when the system is shut down. Volatile information generally consists of:
System time, Logged on user(s), Process information, Network connections, Network status,
Clipboard contents, Command history, Service/driver information.
5. Data Recovery
Data recovery is the process of restoring data that has been lost, corrupted or made
inaccessible for any reason or accidentally deleted.
“In general, when a file is deleted, the data it contained actually remain on a disk for a time
and can be recovered” (Fred Cohen, 2009).
There are several reasons for data recovery; it’s possible that has been a deliberate attempt’s
to destroy a hard drive or partitions, or at least a data on them. We can find very handful tools
available for different platforms in order to recover the data. Depending on the file system, we
know that each operating system treats differently deleted files. For example:
Windows FAT, marks file directory as unused and destroy allocation information.
Windows NTFS, marks file entry as unused, then it deletes record from directory and
mark a disk space as unused.
Linux file system destroys a file descriptor and sets a disk as free. (File location info,
file size, type of the file etc.)
This mean that data will remain there until the operating system reuses the space for new data.
5.1 Tools for Data Recovery
Whether we want to recover a deleted files and folders or to recover data from damaged
media our chances to save those data at safe location are depending at circumstances in which
way they are missing. In order to achieve this, we will use one of available data recovery
software, but we must pay attention from which file system, are we trying to recover the data.
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PROCEEDINGS
Let as introduce some of them:
VirtualLab Data Recovery, besides supporting the NTFS, FAT and FAT-32, file
systems, it supports Mac HFS / HFS+ and even NFS. In addition, it supports data
recovery on devices such as memory card or USB drives. It has ability to make
sector-by-sector copy of a failing drive.
EaseUS Data Recovery Wizard, have a three recovery modules
o Complete Recovery, used to recover data from formatted hard drive, corrupted
or displayed as a RAW
o Deleted File Recovery, used when your data are deleted and emptied from
Recycle Bin.
o Partition Recovery, used to recover data from hard drive when the partition is
deleted, invisible or lost.
Stellar Pheonix, recover data from Windows PC hard drive, memory card and USB
sticks. Hi can restore archive, databases, documents and different type of multimedia
files.
Beside these, we can find a several other programs with better or almost the same functions,
depends on whether they are licensed or free of charge.
6. Conclusion
As is the case with all evidence, it's very important to maintain a chain of custody for
computer evidence. Each person who handled evidence may be required to testify that the
evidence presented in court is the same as when it was processed during the investigation.
Although it may not be necessary to produce at trial every individual who handled the
evidence, it is best to keep the number to a minimum and maintain documentation to
demonstrate that digital evidence has not been altered since it was collected. Forensic
investigators must do everything possible to preserve the integrity of the digital evidence.
Any mistakes in the process call the evidence into question and rendering it worthless. The
way we handle integrity issues are numerous and include the way we seize, label, transport
copy, analyze and finally present the results at court trials.
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ISSD 2014
REFERENCES
[1] Christopher L.T. Brown, (2009). Computer Evidence, Second Edition: Collection & Preservation.
[2] Eoghan Casey, (2011). Digital Evidence and Computer Crime, Third Edition: Forensic Science, Computer
and Internet.
[3] Harlan Carvey, (2012). Windows Forensic Analysis Toolkit, Third Edition: Advanced Analysis Techniques
for Windows 7.
[4] Fred Cohen, ( 2009). Digital Forensic Evidence Examination.
[5] John Sammons, (2012). The Basic of Digital Forensics, The primer For Getting Started in Digital Forensics.
[6] Michael G. Solomon, K Rudolph, Ed Tittel and Neil Broom, (2011). Computer Forensics JumpStart , Second
Edition.
[7] LIVE CD - BackTrack Linux - Penetration Testing Distribution. (2012). Retrieved Jan 27, 2014, from
http://www.backtrack-linux.org/
[8] Sleuth Kit – Open Source Digital Investigation Tools. (2014). Retrived Feb 15, 2014 from
http://www.sleuthkit.org/
[9] FTK – Forensic Toolkit 5. (2014). Retrived Feb 23, 2014 from http://www.accessdata.com/products/digitalforensics/ftk
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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Extent
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2534
Title
A name given to the resource
DIGITAL FORENSIC INVESTIGATION, COLLECTION AND PRESERVATION OF DIGITAL EVIDENCE
Author
Author
ĐALTUR, Vahidin
HAJDAREVIC, Kemal
Abstract
A summary of the resource.
With computers, and other electronic devices being involved in an increasing number, and type, of crimes the electronic trace left on electronic media can be a vital part of the legal process. To ensure acceptance by courts, accepted processes and procedures need to be acquired and demonstrated which are not dissimilar to the issues surrounding traditional forensic investigations. Forensic technology makes it possible to: identify privacy issues; establish a chain of custody for provenance; employ write protection for capture and transfer; and detect forgery or manipulation. It can extract and mine relevant metadata and content; enable efficient indexing and searching by curators; and facilitate audit control and granular access privileges. In recent years, digital forensics has emerged as an essential source of tools and approaches for facilitating digital preservation and curation, specifically for protecting and investigating evidence from the past. Institutional repositories and professionals with responsibilities for personal archives can benefit from forensics in addressing digital authenticity, accountability and accessibility. Digital personal information must be handled with due sensitivity and security respecting available standards while demonstrably protecting its evidential value. A digital forensic investigation is a special case of a digital investigation where the procedures and techniques that are used will allow the results to be entered into a court of law. Computer forensics is a new and fast growing field that involves carefully collecting and examining electronic evidence that not only assesses the damage to a computer as a result of an electronic attack, but also to recover lost information from such systems to prosecute criminals. With the growing importance of computer security today and the seriousness of cyber-crime, it is important for computer professionals to understand the technology used in computer forensics. Keywords: Computer forensics, image acquisition, digital preservation, data recovery
Publisher
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International Burch University
Date
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2014-05-15
Keywords
Keywords.
Article
PeerReviewed
Identifier
An unambiguous reference to the resource within a given context
ISSN 978-9958-834-36-3
Q Science (General),QH301 Biology,QH426 Genetics
-
https://omeka.ibu.edu.ba/files/original/4c489ebacd90c30e8011e4cf0b940004.docx
0ce3267c6578df251390b7e8f324b683
https://omeka.ibu.edu.ba/files/original/10a7067339be6caaec27497acab9506e.pdf
58a687905d8e2187c393923e3c43fcf1
PDF Text
Text
Influence of Environment on Business Performance
SeadAhmić
Business Association PRO-IN
Bosnia and Herzegovina
sead.ahmic@pro-in.ba
Abstract: Entrepreneurial spirit of the people in Bosnia and Herzegovina has been on the rise
over the last nineteen years, since we witnessed significant emergence of many new business
start-ups. During Yugoslavian period most businesses were state-owned enterprises. Different
branches were deployed at different business areas. In Bosnia and Herzegovina, there were
certain parts of the country where certain industry sectors were highly developed such as
textile industry, food industry, metal, wood and other industries.
Purpose of this study is to explore influence of business environment on business
performance. Both qualitative and quantitative research will be conducted. For the
qualitative part of study data will be collected through in depth interviews with several
entrepreneurs in Federation of Bosnia and Herzegovina. And for the quantitative part, data
will be collected through questionnaires that will be delivered to entrepreneurs in four
municipalities in Federation of Bosnia and Herzegovina. The results of the study indicated
that people in the regions where state business were not present are more likely to open and
run their own private business than the people in the regions were state businesses operate.
Keywords: Entrepreneurs, Business Performance, Business Environment, Industry.
98
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Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Extent
The size or duration of the resource.
2552
Title
A name given to the resource
Influence of Environment on Business Performance
Author
Author
AHMIĆ, Sead
Abstract
A summary of the resource.
Entrepreneurial spirit of the people in Bosnia and Herzegovina has been on the rise over the last nineteen years, since we witnessed significant emergence of many new business start-ups. During Yugoslavian period most businesses were state-owned enterprises. Different branches were deployed at different business areas. In Bosnia and Herzegovina, there were certain parts of the country where certain industry sectors were highly developed such as textile industry, food industry, metal, wood and other industries. Purpose of this study is to explore influence of business environment on business performance. Both qualitative and quantitative research will be conducted. For the qualitative part of study data will be collected through in depth interviews with several entrepreneurs in Federation of Bosnia and Herzegovina. And for the quantitative part, data will be collected through questionnaires that will be delivered to entrepreneurs in four municipalities in Federation of Bosnia and Herzegovina. The results of the study indicated that people in the regions where state business were not present are more likely to open and run their own private business than the people in the regions were state businesses operate. Keywords: Entrepreneurs, Business Performance, Business Environment, Industry.
Publisher
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International Burch University
Date
A point or period of time associated with an event in the lifecycle of the resource
2014-04-24
Keywords
Keywords.
Article
PeerReviewed
Identifier
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ISSN 2303-4564
H Social Sciences (General)