Mustafa Poyraz

Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition

The aim of this paper is to estimate the fault location on transmission lines quickly and accurately. The faulty current and voltage signals obtained from a simulation are decomposed by wavelet packet transform (WPT). The extracted features are applied to artificial neural network (ANN) for estimating fault location. As data sets increase in size, their analysis become more complicated and time consuming. The energy and entropy criterion are applied to wavelet packet coefficients to decrease the size of feature vectors. The test results of ANN demonstrate that the applying of energy criterion to current signals after WPT is a very powerful and reliable method for reducing data sets in size and hence estimating fault locations on transmission lines quickly and accurately.

Intelligent target recognition based on wavelet packet neural network

In this paper, an intelligent target recognition system is presented for target recognition from target echo signal of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms using X-band pulse radar. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in noisy radar target echo (RTE) signals. The test results showed that this system was effective in detecting real RTE signals. The correct classification rate was about 95% for used target subjects.

Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals.

A transmission line fault locator based on Elman recurrent networks

In this paper, a transmission line fault location model which is based on an Elman recurrent network (ERN) has been presented for balanced and unbalanced short circuit faults. All fault situations with different inception times are implemented on a 380-kV prototype power system. Wavelet transform (WT) is used for selecting distinctive features about the faulty signals. The system has the advantages of utilizing single-end measurements, using both voltage and current signals. ERN is able to determine the fault location occurred on transmission line rapidly and correctly as an important alternative to standard feedforward back propagation networks (FFNs) and radial basis functions (RBFs) neural networks.

Intelligent target recognition based on wavelet adaptive network based fuzzy inference system

In this paper, an intelligent target recognition system is presented for target recognition from target echo signal of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms using X –band pulse radar. Because of this, a wavelet adaptive network based fuzzy inference systemmodel developed by us is used. The model consists of two layers: wavelet and adaptive network based fuzzy inference system. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet decomposition and wavelet entropy. The used for classification is an adaptive network based fuzzy inference system. The performance of the developed system has been evaluated in noisy radar target echo signals. The test results showed that this system was effective in detecting real radar target echo signals. The correct classification rate was about 93% for used target subjects.

An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings.

Derivation of state and output equations for systems containing switches and a novel definition of a switch using the bond graph model

Abstract This study gives a novel switch definition which is at the same time simple and more general. In addition, a formulation giving state and output equations is presented by using the bond graph model of switch-containing systems. The use of a computer program called BONDSO is described and examples of the application are given.

Stereo Vision and Statistical Based Behaviour Prediction of Driver

The goal of this project is to develop a Webcam-based system for monitoring the activities of automobile drivers. As in any system deployed for monitoring driver activities, the primary goal is to distinguish between safe and unsafe driving actions. There is no fixed list of actions that qualifies the unsafe driving behaviors. In general, an activity or an action that reduces a drivers alertness of their surroundings should be classified as unsafe driving behavior. Some examples of unsafe driving behavior include fatigue, talking on a cellular telephone, eating, and adjusting the controls of the dashboard stereo while driving. In this study, we also investigated the relationship between 2D and 3D face and pose recognition.

Analysis of switched systems using the bond graph methods

Abstract In recent years, the analysis of switching systems has gained importance. In this paper, the formulation of state and output equations and solutions of switched-systems are presented by using the bond graph model with a new simple and more general switch definition. The theory is illustrated by a few examples and the output of the computer programme called BOMAS is presented.

An educational tool for fundamental DC–DC converter circuits and active power factor correction applications

Computer‐aided education has become popular thanks to its flexible and useful structure in classroom environment. In this study, a computer‐based educational tool for DC–DC converters and its active power factor correction (PFC) applications are presented for effective education of these circuits at power electronics courses and reinforcement of the theoretical instruction given at these courses. Firstly, simulations were generated by MATLAB/Simulink toolbox. After then, graphical user interfaces (GUIs) were designed for visual approach to specifying all input and output parameters. The results of the educational tool developed are illustrated with screenshots and graphics.