Browsing by Author "Koçyigit, Y"

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    Estimation of cross sections for molecule-cluster interactions by using artificial neural networks
    Böyükata, M; Koçyigit, Y; Güvenç, ZB
    The cross sections Of D-2 (v,j) + Ni-n (T), n = 19 and 20, collision systems have been estimated by using Artificial Neural Networks (ANNs). For training, previously determined cross section values via molecular dynamics simulation have been used. The performance of the ANNs for predicting any quantities in molecule-cluster interaction has been investigated. Effects of the temperature of the clusters and the rovibrational states of the molecule are analyzed. The results are in good agreement with previous studies.
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    Landing Sequencing Modelling with Fuzzy Logic: Opportunistic Approach for Unmanned Aerial Systems
    Ören, A; Koçyigit, Y
    From the beginning of 21th century, the quantities and types of Unmanned Aerial Systems (UAS) have grown enormously and they become to boost substitution for the manned systems. UAS are creating advancement with a massive potential to change military operations and also enabling the new civilian applications. The vital issue for the airspace designers and managers is how to integrate manned and unmanned systems to the interoperability airspace. It is global arrangement that UAS operation in the integrated airspace must meet in any operational standards, procedures and safety issues as manned aircraft. For today, Air Traffic Management (ATM) is a dynamic and integrated environment including both manned and unmanned systems. Air Traffic Control (ATC) systems have the obligation to sustain an efficient and safe airspace utilization of manned and unmanned systems together. On the other hand, tendency for civilian and military applications about future is substituting unmanned aerial systems for manned aerial systems. In this paper, we present an analytic approach for UAS landing sequencing modelling in the dynamic airspace including different aerodynamic specifications or mission types for both military and civilian UAS via fuzzy logic modelling. During the designing model, the MATLAB Fuzzy FIS (Fuzzy Inference System) is used with realistic data and the user friendly interface is created via MATLAB/GUI.
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    Determination of chemisorption probabilities of hydrogen molecules on a nickel surface by Artificial Neural Network
    Böyükata, M; Koçyigit, Y; Güvenç, ZB
    Dissociative chemisorption probabilities for H-2(v, j) + Ni(100) collision systems have been estimated by using Artificial Neural Network (ANN). For training, previously determined probability values via molecular dynamics simulations have been used. Performance of the ANN, for predicting any quantities in the molecule-surface interaction, has been investigated. Effects of the surface sites and the rovibrational states of the molecule on the process are analyzed. The results are in good agreement with the related previous studies.
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    Using LBG Algorithm for extracting the Features of EMG Signals
    Koçyigit, Y; Kiliç, I
    The Electromyographic (EMG) signals observed at the surface of the skin is the sum of many small action potentials generated in the muscle fibers. There is only a pattern for each EMG signals, which are generated by biceps and triceps muscles. There are different types of signal processing in order to find out the feature values for true classification in this pattern. In this study, the Feature values belong to 4 different arm movements are obtained by using clustering methods, i.e K-means, Fuzzy C-means, and LBG after applying Wavelet Transform to EMG signals. Then these feature values are compared each other by KEYK and Quadratic Discriminant Analysis classifier.
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    Heart sound signal classification using fast independent component analysis
    Koçyigit, Y
    The analysis of heart sound signals is a basic method for heart examination. It may indicate the presence of heart disorders and provide clinical information in the diagnostic process. In this study, a novel feature dimension reduction method based on independent component analysis (ICA) has been proposed for the classification of fourteen different heart sound types; the method was compared with principal component analysis. The feature vectors are classified by support vector machines, linear discriminant analysis, and naive Bayes (NB) classifiers using 10 -fold cross validation. The ICA combined with NB achieves the highest average performance with a sensitivity of 98.53%, specificity of 99.89%, g -means of 99.21%, and accuracy of 99.79%.
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    A New Approach to Genetic Algorithm in Image Compression
    Harman, F; Koçyigit, Y
    The importance of image compression problem has been progressing with the development of technology. The usage of genetic algorithm has become widespread in this field. In this study, the general structure of genetic algorithm and its effects on image compression are analyzed. In this study, it is seen that the creation of population via natural selection, the ratio of mutation and crossover affect the performance of image compression a lot. Roulette Wheel Selection and Elitist Selection that are the most known natural selections are firstly implemented on the standard image. But with these known natural selections, MSE (mean square error) and PSNR (peak signal noise ratio) are seen close to each other. It is seen that in all implementation with the 10% crossover and 5% mutation ratio, the natural selection algorithm based on pools has better MSE and PSNR values than genetic algorithm based on roulette wheel and elitist selection respectively.