TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2015
In this paper, epilepsy diagnosis has been investigated by using Electroencephalogram (EEG) recor... more In this paper, epilepsy diagnosis has been investigated by using Electroencephalogram (EEG) records. For this purpose, a technique as the classifier Artificial Neural Networks (ANN), which is frequently used and known as an active classification technique, is used. Particle Swarm Optimization (PSO) method is preferred as training algorithm for ANN. PSO based neural network model (PSONN) is diversified according to PSO variants and seven PSO based neural network models are described. In these models, PSONN3 and PSONN4 are determined as appropriate models for the classification. In addition, different number of neurons, iterations/generations and swarm sizes have been considered and tried. Obtained results of the models have been evaluated.
Indoor air quality is very important for human health, comfort, performance and productivity sinc... more Indoor air quality is very important for human health, comfort, performance and productivity since people spend more than 80% of their time indoor environments such as school, office, home and other building. Some pollutants are mostly at higher concentrations indoors than outdoors and can affect negatively human health and productivity. Therefore, correct prediction of indoor pollutant concentration will guide in making the necessary arrangements indoors in order to spend their time more efficiently.
Indoor air quality (IAQ) monitoring is getting more attention from people because of health, comf... more Indoor air quality (IAQ) monitoring is getting more attention from people because of health, comfort, and energy efficiency considerations. Indoor air pollution sources are gases which are emitted from synthetic materials in the buildings and human breath. These gases can be detected by devices equipped with electronic sensor arrays which are installed rooms in a building, and they convert gas concentrations to digital data. This data must be processed to create information for monitoring system and to control ventilation system each individual room. Collection of digital data from multiple rooms of a building can be achieved by different technologies. In this study, MPI (Message Passing Interface) is preferred due to its simplicity and scalability. Gas concentration measurement devices are simulated by software on virtual hosts. MPI routine libraries are installed to the simulated sensor nodes, i.e. virtual hosts. A graphical user interface (control panel) is implemented to form building components such as rooms and sensor nodes. The sensor data transfer to the central server is achieved by MPI. In the central server some graphical tools are implemented to show air quality status of the rooms by coloring rectangular shapes accordingly.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2015
Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It... more Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.
In the developed world, people spend most part of their days at indoors. Existence and/or high co... more In the developed world, people spend most part of their days at indoors. Existence and/or high concentrations of biological agents (such as mould, fungus), gas (such as CO, SO 2 , O 3 ), particulate pollutants, explosives and combustibles indoor environments can affect the air quality, in turn, human health, comfort, security and performance. Indoor air is so important for human life, hence it must be clean.
Günümüzde insanlar zamanlarının çoğunu iç ortamlarda geçirmektedir. Bu nedenle, insan sağlığını, ... more Günümüzde insanlar zamanlarının çoğunu iç ortamlarda geçirmektedir. Bu nedenle, insan sağlığını, performansını ve konforunu olumsuz yönde etkileyecek iç ortamda bulunan kirleticilerin varlığı ve miktarı hava kalitesini etkilemektedir. İç ortam hava kalitesini belirlemek ve kirleticilerin dağılımını incelemek amacıyla birçok model geliştirilmiştir. Bu çalışmanın amacı, bir odadaki CO, CO 2 , SO 2 gibi iç ortam gaz kirleticilerinin konsantrasyonlarını sıcaklık, nem, basınç ve kirletici kaynaklarının bir fonksiyonu olarak tahmin etmektir. Bu amaçla iç ortam hava kalitesi modellerinden biri olan Nazaroff ve Cass modeli kullanılmıştır. Modelleme yardımıyla filtreleme, havalandırma ve yayılma etkileri açıklanmış ve iç ortamdaki gaz kirleticilerin konsantrasyonları hesaplanmıştır. Çalışmada, ilgili gazların miktarlarını tahmin etmek için MATLAB programlama dili kullanılarak özel bir yazılım geliştirilmiştir. Geliştirilen yazılım yardımıyla havalandırma çeşidine göre gaz kirleticilerin zamana bağlı konsantrasyon değişimleri grafiklerle sunulmuştur.
The mathematical modeling of the indoor air pollutants is an issue which draws interest today and... more The mathematical modeling of the indoor air pollutants is an issue which draws interest today and also is promoted to work on. The objective of this study is to predict the indoor air pollutants' (such as CO, SO 2 , O 3 ) concentration rates as a function of temperature, humidity, pressure and certain objects (odor sources) in a classroom using Nazaroff and Cass model. The effects of direct emission, filtration and ventilation are explained and the changes in the concentration of the pollutants in indoor air are determined by the modeling. In the study, specific software is also developed via MATLAB programming language to produce numerical values of the interested gas amount. The graphical output of this software shows emitted gas amount with respect to time.
Bu çalışma, üç moleküler yapının (DNA (DeoksiriboNükleik Asit), nanoteknoloji ve yonga) tıp alanı... more Bu çalışma, üç moleküler yapının (DNA (DeoksiriboNükleik Asit), nanoteknoloji ve yonga) tıp alanındaki geleceğine ilişkin bir bakış sunmaktadır.İlk olarak bu üç moleküler yapı kavramları açıklanmış, daha sonra tıp alanında yapılmış çalışmalarına örnekler verilmiş ve gelecekteki gelişimleri hakkında öngörüde bulunulmuştur. ABSTRACT This paper is an overview of the future of three molecular structures (DNA (Deoxyribonucleic Acid), nanotechnology and chip) in the medical.First, the concepts of this molecular structures are explained, then their recent past studies in the medical field are given and their improvements in the future are predicted.
Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It... more Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in classification of epileptic attacks and epilepsy diagnosis. In this paper, epilepsy diagnosis by evaluation of EEG records is presented. Artificial Neural Networks (ANN) is used as a classification technique. Particle Swarm Optimization (PSO) method, which doesn't require gradient calculation, derivative information and any solution of differential equations is preferred for ANN training. This training method is compared with back propagation algorithm, which is one of the traditional methods, and the results are interpreted. In case of using the PSO algorithm, the training and test classification accuracies are %99.67 and %100, respectively. PSO based neural network model (PSONN) has a better classification accuracy than backpropagation neural network model (BPNN) for epilepsy diagnosis.
Sezgisel optimizasyon algoritmaları, doğrusal olmayan sistemlerin optimizasyon problemlerinde old... more Sezgisel optimizasyon algoritmaları, doğrusal olmayan sistemlerin optimizasyon problemlerinde oldukça sık kullanılmaktadır. Bu çalışmada, Doğru Akım (DA) motor denetimi için Oransal-İntegral-Türevsel (Proportional-Integral-Derivative, PID) denetleç parametrelerinin tespitinde sezgisel optimizasyon algoritmalarından Yerçekimsel Arama Algoritması (YAA) kullanılmıştır. Algoritma 100 defa koşturulmuş, elde edilen ortalama PID denetleç parametre değerleri [1]' de verilen diğer optimizasyon algoritmalarının (Tur Atan Karınca Kolonisi Algoritması (TACO), Genetik Algoritma (GA), Parçacık Sürü Optimizasyonu (PSO) ve Diferansiyel Gelişim (DE)) 100 defa koşturulması sonucu elde edilen değerleri ile karşılaştırılmıştır, denetim başarımları ile kıyaslanmıştır. Sonuç olarak, YAA' nın birçok kategoride TACO ve GA' dan daha iyi, PSO ve DE algoritmalarından daha düşük başarım sergilediği görülmüştür.
In this paper, epilepsy diagnosis has been investigated by using Electroencephalogram (EEG) recor... more In this paper, epilepsy diagnosis has been investigated by using Electroencephalogram (EEG) records. For this purpose, a technique as the classifier Artificial Neural Networks (ANN), which is frequently used and known as an active classification technique, is used. Particle Swarm Optimization (PSO) method is preferred as training algorithm for ANN. PSO based neural network model (PSONN) is diversified according to PSO variants and seven PSO based neural network models are described. In these models, PSONN3 and PSONN4 are determined as appropriate models for the classification. In addition, different number of neurons, iterations/generations and swarm sizes have been considered and tried. Obtained results of the models have been evaluated.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2015
In this paper, epilepsy diagnosis has been investigated by using Electroencephalogram (EEG) recor... more In this paper, epilepsy diagnosis has been investigated by using Electroencephalogram (EEG) records. For this purpose, a technique as the classifier Artificial Neural Networks (ANN), which is frequently used and known as an active classification technique, is used. Particle Swarm Optimization (PSO) method is preferred as training algorithm for ANN. PSO based neural network model (PSONN) is diversified according to PSO variants and seven PSO based neural network models are described. In these models, PSONN3 and PSONN4 are determined as appropriate models for the classification. In addition, different number of neurons, iterations/generations and swarm sizes have been considered and tried. Obtained results of the models have been evaluated.
Indoor air quality is very important for human health, comfort, performance and productivity sinc... more Indoor air quality is very important for human health, comfort, performance and productivity since people spend more than 80% of their time indoor environments such as school, office, home and other building. Some pollutants are mostly at higher concentrations indoors than outdoors and can affect negatively human health and productivity. Therefore, correct prediction of indoor pollutant concentration will guide in making the necessary arrangements indoors in order to spend their time more efficiently.
Indoor air quality (IAQ) monitoring is getting more attention from people because of health, comf... more Indoor air quality (IAQ) monitoring is getting more attention from people because of health, comfort, and energy efficiency considerations. Indoor air pollution sources are gases which are emitted from synthetic materials in the buildings and human breath. These gases can be detected by devices equipped with electronic sensor arrays which are installed rooms in a building, and they convert gas concentrations to digital data. This data must be processed to create information for monitoring system and to control ventilation system each individual room. Collection of digital data from multiple rooms of a building can be achieved by different technologies. In this study, MPI (Message Passing Interface) is preferred due to its simplicity and scalability. Gas concentration measurement devices are simulated by software on virtual hosts. MPI routine libraries are installed to the simulated sensor nodes, i.e. virtual hosts. A graphical user interface (control panel) is implemented to form building components such as rooms and sensor nodes. The sensor data transfer to the central server is achieved by MPI. In the central server some graphical tools are implemented to show air quality status of the rooms by coloring rectangular shapes accordingly.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2015
Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It... more Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.
In the developed world, people spend most part of their days at indoors. Existence and/or high co... more In the developed world, people spend most part of their days at indoors. Existence and/or high concentrations of biological agents (such as mould, fungus), gas (such as CO, SO 2 , O 3 ), particulate pollutants, explosives and combustibles indoor environments can affect the air quality, in turn, human health, comfort, security and performance. Indoor air is so important for human life, hence it must be clean.
Günümüzde insanlar zamanlarının çoğunu iç ortamlarda geçirmektedir. Bu nedenle, insan sağlığını, ... more Günümüzde insanlar zamanlarının çoğunu iç ortamlarda geçirmektedir. Bu nedenle, insan sağlığını, performansını ve konforunu olumsuz yönde etkileyecek iç ortamda bulunan kirleticilerin varlığı ve miktarı hava kalitesini etkilemektedir. İç ortam hava kalitesini belirlemek ve kirleticilerin dağılımını incelemek amacıyla birçok model geliştirilmiştir. Bu çalışmanın amacı, bir odadaki CO, CO 2 , SO 2 gibi iç ortam gaz kirleticilerinin konsantrasyonlarını sıcaklık, nem, basınç ve kirletici kaynaklarının bir fonksiyonu olarak tahmin etmektir. Bu amaçla iç ortam hava kalitesi modellerinden biri olan Nazaroff ve Cass modeli kullanılmıştır. Modelleme yardımıyla filtreleme, havalandırma ve yayılma etkileri açıklanmış ve iç ortamdaki gaz kirleticilerin konsantrasyonları hesaplanmıştır. Çalışmada, ilgili gazların miktarlarını tahmin etmek için MATLAB programlama dili kullanılarak özel bir yazılım geliştirilmiştir. Geliştirilen yazılım yardımıyla havalandırma çeşidine göre gaz kirleticilerin zamana bağlı konsantrasyon değişimleri grafiklerle sunulmuştur.
The mathematical modeling of the indoor air pollutants is an issue which draws interest today and... more The mathematical modeling of the indoor air pollutants is an issue which draws interest today and also is promoted to work on. The objective of this study is to predict the indoor air pollutants' (such as CO, SO 2 , O 3 ) concentration rates as a function of temperature, humidity, pressure and certain objects (odor sources) in a classroom using Nazaroff and Cass model. The effects of direct emission, filtration and ventilation are explained and the changes in the concentration of the pollutants in indoor air are determined by the modeling. In the study, specific software is also developed via MATLAB programming language to produce numerical values of the interested gas amount. The graphical output of this software shows emitted gas amount with respect to time.
Bu çalışma, üç moleküler yapının (DNA (DeoksiriboNükleik Asit), nanoteknoloji ve yonga) tıp alanı... more Bu çalışma, üç moleküler yapının (DNA (DeoksiriboNükleik Asit), nanoteknoloji ve yonga) tıp alanındaki geleceğine ilişkin bir bakış sunmaktadır.İlk olarak bu üç moleküler yapı kavramları açıklanmış, daha sonra tıp alanında yapılmış çalışmalarına örnekler verilmiş ve gelecekteki gelişimleri hakkında öngörüde bulunulmuştur. ABSTRACT This paper is an overview of the future of three molecular structures (DNA (Deoxyribonucleic Acid), nanotechnology and chip) in the medical.First, the concepts of this molecular structures are explained, then their recent past studies in the medical field are given and their improvements in the future are predicted.
Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It... more Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in classification of epileptic attacks and epilepsy diagnosis. In this paper, epilepsy diagnosis by evaluation of EEG records is presented. Artificial Neural Networks (ANN) is used as a classification technique. Particle Swarm Optimization (PSO) method, which doesn't require gradient calculation, derivative information and any solution of differential equations is preferred for ANN training. This training method is compared with back propagation algorithm, which is one of the traditional methods, and the results are interpreted. In case of using the PSO algorithm, the training and test classification accuracies are %99.67 and %100, respectively. PSO based neural network model (PSONN) has a better classification accuracy than backpropagation neural network model (BPNN) for epilepsy diagnosis.
Sezgisel optimizasyon algoritmaları, doğrusal olmayan sistemlerin optimizasyon problemlerinde old... more Sezgisel optimizasyon algoritmaları, doğrusal olmayan sistemlerin optimizasyon problemlerinde oldukça sık kullanılmaktadır. Bu çalışmada, Doğru Akım (DA) motor denetimi için Oransal-İntegral-Türevsel (Proportional-Integral-Derivative, PID) denetleç parametrelerinin tespitinde sezgisel optimizasyon algoritmalarından Yerçekimsel Arama Algoritması (YAA) kullanılmıştır. Algoritma 100 defa koşturulmuş, elde edilen ortalama PID denetleç parametre değerleri [1]' de verilen diğer optimizasyon algoritmalarının (Tur Atan Karınca Kolonisi Algoritması (TACO), Genetik Algoritma (GA), Parçacık Sürü Optimizasyonu (PSO) ve Diferansiyel Gelişim (DE)) 100 defa koşturulması sonucu elde edilen değerleri ile karşılaştırılmıştır, denetim başarımları ile kıyaslanmıştır. Sonuç olarak, YAA' nın birçok kategoride TACO ve GA' dan daha iyi, PSO ve DE algoritmalarından daha düşük başarım sergilediği görülmüştür.
In this paper, epilepsy diagnosis has been investigated by using Electroencephalogram (EEG) recor... more In this paper, epilepsy diagnosis has been investigated by using Electroencephalogram (EEG) records. For this purpose, a technique as the classifier Artificial Neural Networks (ANN), which is frequently used and known as an active classification technique, is used. Particle Swarm Optimization (PSO) method is preferred as training algorithm for ANN. PSO based neural network model (PSONN) is diversified according to PSO variants and seven PSO based neural network models are described. In these models, PSONN3 and PSONN4 are determined as appropriate models for the classification. In addition, different number of neurons, iterations/generations and swarm sizes have been considered and tried. Obtained results of the models have been evaluated.
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