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Full-Text Articles in Physical Sciences and Mathematics
Artificial Intelligence-Based Evaluation Of The Factors Affecting The Sales Of An Iron And Steel Company, Mehmet Pekkaya, Zafer Uysal, Aytaç Altan, Seçkin Karasu
Artificial Intelligence-Based Evaluation Of The Factors Affecting The Sales Of An Iron And Steel Company, Mehmet Pekkaya, Zafer Uysal, Aytaç Altan, Seçkin Karasu
Turkish Journal of Electrical Engineering and Computer Sciences
It is important to predict the sales of an iron and steel company and to identify the variables that influence these sales for future planning. The aim in this study was to identify and model the key factors that influence the sales volume of an iron and steel company using artificial neural networks (ANNs). We attempted to obtain an integrated result from the performance/sales levels of 5 models, to use the ANN approach with hybrid algorithms, and also to present an exemplary application in the base metals industry, where there is a limited number of studies. This study contributes to …
Comparison Of Deep Learning And Regression-Based Mppt Algorithms In Pv Systems, Murat Sali̇m Karabi̇naoğlu, Beki̇r Çakir, Mustafa Engi̇n Başoğlu, Abdülvehhab Kazdaloğlu, Azi̇z Güneroğlu
Comparison Of Deep Learning And Regression-Based Mppt Algorithms In Pv Systems, Murat Sali̇m Karabi̇naoğlu, Beki̇r Çakir, Mustafa Engi̇n Başoğlu, Abdülvehhab Kazdaloğlu, Azi̇z Güneroğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Solar energy systems (SES) and photovoltaic (PV) modules should be operated at the maximum power point (MPP) to achieve the highest efficiency in the energy generation processes. Maximum power point tracking (MPPT) applications using conventional methods may not be able to follow the global MPP (GMPP) of the PV system under changing atmospheric conditions and they could oscillate around the local MPP. In this study, a machine learning and deep learning (DL) based long short-term memory (LSTM) model is proposed as an innovative solution for MPPT. Contrary to the traditional MPPT applications using current and voltage sensors, the output resistance …
A Novel Hybrid Algorithm For Morphological Analysis: Artificial Neural-Net-Xmor, Ayla Kayabaş, Ahmet Ercan Topcu, Özkan Kiliç
A Novel Hybrid Algorithm For Morphological Analysis: Artificial Neural-Net-Xmor, Ayla Kayabaş, Ahmet Ercan Topcu, Özkan Kiliç
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we present a novel algorithm that combines a rule-based approach and an artificial neural network-based approach in morphological analysis. The usage of hybrid models including both techniques is evaluated for performance improvements. The proposed hybrid algorithm is based on the idea of the dynamic generation of an artificial neural network according to two-level phonological rules. In this study, the combination of linguistic parsing, a neural network-based error correction model, and statistical filtering is utilized to increase the coverage of pure morphological analysis. We experimented hybrid algorithm applying rule-based and long short-term memory-based (LSTM-based) techniques, and the results …
Development Of A Hybrid System Based On Abc Algorithm For Selection Of Appropriate Parameters For Disease Diagnosis From Ecg Signals, Ersi̇n Ersoy, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel
Development Of A Hybrid System Based On Abc Algorithm For Selection Of Appropriate Parameters For Disease Diagnosis From Ecg Signals, Ersi̇n Ersoy, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel
Turkish Journal of Electrical Engineering and Computer Sciences
The number of people who die due to cardiovascular diseases is quite high. In our study, ECG (electrocar-diogram) signals were divided into segments and waves based on temporal boundaries. Signal similarity methods such as convolution, correlation, covariance, signal peak to noise ratio (PNRS), structural similarity index (SSIM), one of the basic statistical parameters, arithmetic mean and entropy were applied to each of these sections. In addition, a square error-based new approach was applied and the difference of the signs from the mean sign was taken and used as a feature vector. The obtained feature vectors are used in the artificial …
Optimal Directional Overcurrent Relay Coordination Based On Computationalintelligence Technique: A Review, Suzana Pil Ramli, Muhammad Usama, Hazlie Mokhlis, Wei Ru Wong, Muhamad Hatta Hussain, Munir Azam Muhammad, Nurulafiqah Nadzirah Mansor
Optimal Directional Overcurrent Relay Coordination Based On Computationalintelligence Technique: A Review, Suzana Pil Ramli, Muhammad Usama, Hazlie Mokhlis, Wei Ru Wong, Muhamad Hatta Hussain, Munir Azam Muhammad, Nurulafiqah Nadzirah Mansor
Turkish Journal of Electrical Engineering and Computer Sciences
An exponential increase in diverse load demand in the last decade has influenced the integration of more power plants into the power system. This increases the fault current due to the bidirectional flow of current, resulting in unwanted tripping of the relays if not properly coordinated. Therefore, it is imperative to ensure the installation of relays in the grid being able to sense the fault current from any direction (i.e. upstream or downstream). This can be accomplished by introducing an optimal directional overcurrent relay (DOCR) coordination scheme into the system. This paper presents an in-depth review of the applications of …
Exploring The Attention Process Differentiation Of Attention Deficit Hyperactivity Disorder (Adhd) Symptomatic Adults Using Artificial Intelligence Onelectroencephalography (Eeg) Signals, Gökhan Güney, Esra Kisacik, Canan Kalaycioğlu, Görkem Saygili
Exploring The Attention Process Differentiation Of Attention Deficit Hyperactivity Disorder (Adhd) Symptomatic Adults Using Artificial Intelligence Onelectroencephalography (Eeg) Signals, Gökhan Güney, Esra Kisacik, Canan Kalaycioğlu, Görkem Saygili
Turkish Journal of Electrical Engineering and Computer Sciences
Attention deficit and hyperactivity disorder (ADHD) onset in childhood and its symptoms can last up till adulthood. Recently, electroencephalography (EEG) has emerged as a tool to investigate the neurophysiological connection of ADHD and the brain. In this study, we investigated the differentiation of attention process of healthy subjects with or without ADHD symptoms under visual continuous performance test (VCPT). In our experiments, artificial neural network (ANN) algorithm achieved 98.4% classification accuracy with 0.98 sensitivity when P2 event related potential (ERP) was used. Additionally, our experimental results showed that fronto-central channels were the most contributing. Overall, we conclude that the attention …
Deep Neural Network Based M-Learning Model For Predicting Mobile Learners'performance, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq, Arsalan Ali
Deep Neural Network Based M-Learning Model For Predicting Mobile Learners'performance, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq, Arsalan Ali
Turkish Journal of Electrical Engineering and Computer Sciences
The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features …
Experimental And Predicted Xlpe Cable Insulation Properties Under Uvradiation, Abdallah Hedir, Ali Bechouche, Mustapha Moudoud, Madjid Teguar, Omar Lamrous, Sebastien Rondot
Experimental And Predicted Xlpe Cable Insulation Properties Under Uvradiation, Abdallah Hedir, Ali Bechouche, Mustapha Moudoud, Madjid Teguar, Omar Lamrous, Sebastien Rondot
Turkish Journal of Electrical Engineering and Computer Sciences
This paper deals with the behavior of the crosslinked polyethylene (XLPE) used as high-voltage power cable insulation under ultraviolet (UV) radiations. For this, XLPE samples have been irradiated for 240 h using low-pressure vapor fluorescent lamps. Electrical (surface and volume resistivities), mechanical (tensile strength, elongation at break and surface hardness) and physical (weight loss, water absorption, work of water adhesion and contact angle) tests have been first carried out. Experimental results show that the XLPE characteristics are affected by UV radiation. Indeed, a decline in surface resistivity, mechanical properties, and contact angle, and an increase in the water retention amount …
Determination Of Distance Between Dc Traction Power Centers In A 1500-V Dc Subway Line With Artificial Intelligence Methods, Mehmet Taci̇ddi̇n Akçay, İlhan Kocaarslan
Determination Of Distance Between Dc Traction Power Centers In A 1500-V Dc Subway Line With Artificial Intelligence Methods, Mehmet Taci̇ddi̇n Akçay, İlhan Kocaarslan
Turkish Journal of Electrical Engineering and Computer Sciences
The electrification system in rail systems is designed with regard to the operating data and design parameters. While the electrification system is formed, the minimum voltage rating that the traction force requires during the operation needs to be provided. The highest value of the voltage drop occurring on the line is determined by the distance between power centers. This value needs to be kept within certain limits for the continuity of operation. In this study, the determination of the distance between DC traction power centers for a 1500-V DC-fed rail system is done by means of the adaptive neuro-fuzzy inference …
Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu
Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Colon cancer is one of the major causes of human mortality worldwide and the same can be said for Turkey. Various methods are used for the determination of cancer. One of these methods is Fourier transform infrared (FTIR) spectroscopy, which has the ability to reveal biochemical changes. The most common features used to distinguish patients with cancer and healthy subjects are peak densities, peak height ratios, and peak area ratios. The greatest challenge of studies conducted to distinguish cancer patients from healthy subjects using FTIR signals is that the signals of cancer patients and healthy subjects are similar. In the …
Decision-Making For Small Industrial Internet Of Things Using Decision Fusion, Tuğrul Çavdar, Nader Ebrahimpour
Decision-Making For Small Industrial Internet Of Things Using Decision Fusion, Tuğrul Çavdar, Nader Ebrahimpour
Turkish Journal of Electrical Engineering and Computer Sciences
The industrial Internet of Things (IIoT) is a new field of Internet of Things (IoT) that has gained more popularity recently in industrial units and makes it possible to access information anywhere and anytime. In other words, geographic coordinates cannot prevent obtaining equipment and its data. Today, it is possible to manage and control equipment simply without spending time in an operational area and just by using the IIoT. This system collects data from manufacturing and production units by using wireless sensor networks or other networks for classification of fault detection. These data are then used after analysis to allow …
Ann-Assisted Forecasting Of Adsorption Efficiency To Remove Heavy Metals, Magdi Buaisha, Şazi̇ye Balku, Şeni̇z Özalp Yaman
Ann-Assisted Forecasting Of Adsorption Efficiency To Remove Heavy Metals, Magdi Buaisha, Şazi̇ye Balku, Şeni̇z Özalp Yaman
Turkish Journal of Chemistry
In wastewater treatment, scientific and practical models utilizing numerical computational techniques such as artificial neural networks (ANNs) can significantly help to improve the process as a whole through adsorption systems. In the modeling of the adsorption efficiency for heavy metals from wastewater, some kinetic models have been used such as pseudo first-order and second-order. The present work develops an ANN model to forecast the adsorption efficiency of heavy metals such as zinc, nickel, and copper by extracting experimental data from three case studies. To do this, we apply trial-and-error to find the most ideal ANN settings, the efficiency of which …
Patient Comfort Level Prediction During Transport Using Artificial Neural Network, Zeljko Jovanovic, Marija Blagojevic, Dragan Jankovic, Aleksandar Peulic
Patient Comfort Level Prediction During Transport Using Artificial Neural Network, Zeljko Jovanovic, Marija Blagojevic, Dragan Jankovic, Aleksandar Peulic
Turkish Journal of Electrical Engineering and Computer Sciences
Since patient comfort during transport is a matter of paramount importance, this paper aims to determine the possibilities of applying neural networks for its prediction and monitoring. Specific objectives of the research include monitoring and predicting patient transport comfort, with subjective assessment of comfort by medical personnel. An original Android application that collects signals from an accelerometer and a GPS sensor was used with the aim of achieving the research goals. The collected signals were processed and a total of twelve parameters were calculated. A multilayer perceptron was created in the proposed research. The evaluation results indicate acceptable accuracy and …
Classification And Regression Analysis Using Support Vector Machine For Classifying And Locating Faults In A Distribution System, Sophi Shilpa Gururajapathy, Hazlie Mokhlis, Hazlee Azil Bin Illias
Classification And Regression Analysis Using Support Vector Machine For Classifying And Locating Faults In A Distribution System, Sophi Shilpa Gururajapathy, Hazlie Mokhlis, Hazlee Azil Bin Illias
Turkish Journal of Electrical Engineering and Computer Sciences
Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified …
Usage Of Segmentation For Noise Elimination In Reconstructed Images In Digitalholographic Interferometry, Gülhan Ustabaş Kaya, Zehra Saraç
Usage Of Segmentation For Noise Elimination In Reconstructed Images In Digitalholographic Interferometry, Gülhan Ustabaş Kaya, Zehra Saraç
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we propose to enhance the image in digital holography by using an artificial neural network and an iterative algorithm with Nakamura's approach based on segmentation. It is well known that reconstructed three- dimensional (3D) images suffer from noise in digital holography. In addition, obtaining 3D reconstructed images takes a long time due to large pixel numbers in reconstructed images and lack of memory in the system. The segmentation process is an application that overcomes these problems. Therefore, we focus on the implementation of segmentation for image enhancement. In addition, the results of the segmentation process for both …
Sign Language Recognition With Multi Feature Fusion And Ann Classifier, Sunitha Ravi, Maloji Suman, Polurie Venkata Vijay Kishore, Kiran Kumar Eepuri
Sign Language Recognition With Multi Feature Fusion And Ann Classifier, Sunitha Ravi, Maloji Suman, Polurie Venkata Vijay Kishore, Kiran Kumar Eepuri
Turkish Journal of Electrical Engineering and Computer Sciences
Extracting and recognizing complex human movements such as sign language gestures from video sequences is a challenging task. In this paper this kind of a difficult problem is approached with Indian sign language (ISL) videos. A new segmentation algorithm is developed by fusion of features from discrete wavelet transform (DWT) and local binary pattern (LBP). A 2D point cloud is formed from fused features, which represent the local hand shapes in consecutive video frames. We validate the proposed feature extraction model with state of the art features such as HOG, SIFT and SURF for each sign video on the same …
Matching Points Of Interest With User Context: An Ann Approach, Özgün Yilmaz
Matching Points Of Interest With User Context: An Ann Approach, Özgün Yilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, the design and development of an artificial neural network (ANN) for similarity value calculation in a context-aware system is proposed. This neural network is used by the neural agent of the iConAwa system. Since iConAwa is an intelligent, context-aware, multiagent system, it provides mobile users with context-aware information and services, and also provides communication with each other. Context and points of interest are modeled in a flexible and extensible way by using ontologies. iConAwa derives high-level implicit context from low-level explicit context by inference performed over the context ontology. This approach decouples context reasoning from the source …
Ann-Based Shepwm Using A Harmony Search On A New Multilevel Inverter Topology, Fayçal Chabni, Rachid Taleb, M'Hamed Helaimi
Ann-Based Shepwm Using A Harmony Search On A New Multilevel Inverter Topology, Fayçal Chabni, Rachid Taleb, M'Hamed Helaimi
Turkish Journal of Electrical Engineering and Computer Sciences
This article presents the application of the harmony search (HS) optimization algorithm for selective harmonic elimination PWM (SHEPWM) in a new topology of multilevel inverters with reduced number of electronic switching elements. The main objective of the harmonic elimination strategy is eliminating undesired low-rank harmonics in order to improve the quality of the output waveform. The harmonic elimination strategy is achieved by solving a system of nonlinear equations. In this paper harmony search optimization is applied using artificial neural networks (ANNs) on a new 21-level inverter topology. The algorithm is based on a music improvisation process. MATLAB programming software is …
Generalized Referenceless Image Quality Assessment Framework Using Texture Energy Measures And Pattern Strength Features, Jayashri Bagade, Kulbir Singh, Yogesh Dandawate
Generalized Referenceless Image Quality Assessment Framework Using Texture Energy Measures And Pattern Strength Features, Jayashri Bagade, Kulbir Singh, Yogesh Dandawate
Turkish Journal of Electrical Engineering and Computer Sciences
Referenceless image quality assessment is a challenging and critical problem in today's multimedia applica\-tions. Texture patterns in images are normally at high frequencies compared to lower ones. Due to the effect of distortions during acquisition, compression, and transmission, texture deviation artifacts are generated that cause a granular effect in the image. Other artifacts, such as blocking, affect high frequencies in an image, causing distorted edges. Combining the analysis of texture deviation and other artifacts helps in determining the quality of an image. The proposed approach uses variation in the energy of pixels to quantify the quality of an image. These …
Tourism Demand Modelling And Forecasting Using Data Mining Techniques In Multivariate Time Series: A Case Study In Turkey, Selçuk Cankurt, Abdülhami̇t Subaşi
Tourism Demand Modelling And Forecasting Using Data Mining Techniques In Multivariate Time Series: A Case Study In Turkey, Selçuk Cankurt, Abdülhami̇t Subaşi
Turkish Journal of Electrical Engineering and Computer Sciences
In this study multiple linear regression, multilayer perceptron (MLP) regression, and support vector regression (SVR) are used to make multivariate tourism forecasting for Turkey. This paper is a comparative study of data mining techniques based on multivariate regression modelling with monthly data points to forecast tourism demand; it focuses on Turkey. Both MLP and SVR methods are widely employed in the variety forecasting problems. Most of the previous research on tourism forecasting used univariate time series or a limited number of variables with mostly yearly or quarterly, and rarely monthly frequencies. However, the application of data mining techniques for multivariate …
Process Time And Mppt Performance Analysis Of Cf, Lut, And Ann Control Methods For A Pmsg-Based Wind Energy Generation System, Abdulhaki̇m Karakaya, Ercüment Karakaş
Process Time And Mppt Performance Analysis Of Cf, Lut, And Ann Control Methods For A Pmsg-Based Wind Energy Generation System, Abdulhaki̇m Karakaya, Ercüment Karakaş
Turkish Journal of Electrical Engineering and Computer Sciences
Due to environmental issues such as global warming and the greenhouse effect, there is a growing interest in renewable sources of energy. Wind energy, which is the most important of these energy sources, can potentially meet a portion of the global energy demand. Numerous studies are being conducted worldwide to determine how the maximum level of power can be obtained from wind energy. In these studies, there is a particular interest in permanent magnet synchronous generators (PMSGs). This is because PMSGs exhibit a good performance within a wide range wind speeds and can be driven directly. In this study, the …
Prediction-Based Reversible Image Watermarking Using Artificial Neural Networks, Mahsa Afsharizadeh, Majid Mohammadi
Prediction-Based Reversible Image Watermarking Using Artificial Neural Networks, Mahsa Afsharizadeh, Majid Mohammadi
Turkish Journal of Electrical Engineering and Computer Sciences
In prediction-based reversible watermarking schemes, watermark bits are embedded in the prediction errors. An accurate prediction results in smaller prediction errors, more efficient embedding, and less distortion for the watermarked image. In this paper, an accurate prediction is made using artificial neural networks. Before the embedding operation, 2 neural networks are trained by the pixel values of the image. Then the trained neural networks predict the pixel values that are used in the embedding operation. Due to the training ability of the neural networks, the prediction will be more accurate than the averaging technique. Experimental results show that the proposed …
Preparation And Characterization Of Zno/Mmt Nanocomposite For Photocatalytic Ozonation Of A Disperse Dye, Alireza Khataee, Murat Kiranşan, Semra Karaca, Samira Arefi- Oskoui
Preparation And Characterization Of Zno/Mmt Nanocomposite For Photocatalytic Ozonation Of A Disperse Dye, Alireza Khataee, Murat Kiranşan, Semra Karaca, Samira Arefi- Oskoui
Turkish Journal of Chemistry
ZnO was immobilized on the montmorillonite (MMT) to synthesize ZnO/MMT nanocomposite. Physicochemical properties of the as-synthesized nanocomposite were determined using X-ray diffraction, scanning electron microscopy, transmission electron microscope, Fourier transform infrared spectroscopy, N$_{2}$ adsorption/desorption, and point of zero charge pH (pH$_{pzc})$ analysis. The performance of the prepared ZnO/MMT nanocomposite was examined for the photocatalytic ozonation of Disperse Red 54 (DR54) and the highest decolorization efficiency (88.75{\%} after 60 min of reaction time) was the result for the mentioned process compared to adsorption, single ozonation, catalytic ozonation, and photolysis. The influence of various operational parameters including initial dye concentration, catalyst concentration, …
Classification Of Short-Circuit Faults In High-Voltage Energy Transmission Line Using Energy Of Instantaneous Active Power Components-Based Common Vector Approach, Mehmet Yumurtaci, Gökhan Gökmen, Çağri Kocaman, Semi̇h Ergi̇n, Osman Kiliç
Classification Of Short-Circuit Faults In High-Voltage Energy Transmission Line Using Energy Of Instantaneous Active Power Components-Based Common Vector Approach, Mehmet Yumurtaci, Gökhan Gökmen, Çağri Kocaman, Semi̇h Ergi̇n, Osman Kiliç
Turkish Journal of Electrical Engineering and Computer Sciences
The majority of power system faults occur in transmission lines. The classification of these faults in power systems is an important issue. In this paper, the real parameters of a 28 km, 154 kV transmission line between Simav and Demirci in Turkey's electricity transmission network is simulated in MATLAB/Simulink. Wavelet packet transform (WPT) is applied to instantaneous voltage signals. Instantaneous active power components are obtained by multiplying instantaneous currents obtained from a voltage source side with these WPT-based voltage signal components. A new feature vector extraction scheme is employed by calculating the energies of instantaneous active power components. Constructed feature …
A Novel Approach Of Design And Analysis Of Fractal Antenna Using A Neurocomputational Method For Reconfigurable Rf Mems Antenna, Paras Chawla, Rajesh Khanna
A Novel Approach Of Design And Analysis Of Fractal Antenna Using A Neurocomputational Method For Reconfigurable Rf Mems Antenna, Paras Chawla, Rajesh Khanna
Turkish Journal of Electrical Engineering and Computer Sciences
A mathematical neural approach/artificial neural network (ANN) for the design of a swastika-shaped reconfigurable antenna as a feedforward side is proposed. Further design parameter calculations using the reverse procedure of the above method is presented. Neural network computational is one of the optimization methods that could be considered to improve the performance of the device. In this paper, the proposed planar antenna up to the 2nd iteration is simulated using finite element method-based HFSS software. The developed ANN algorithm method allows the optimization of the antenna to be carried out by exchanging repetitive simulations and also provides reduced processing times …
Group Control And Identification Of Residential Appliances Using A Nonintrusive Method, Sunil Semwal, Munendra Singh, Rai Sachindra Prasad
Group Control And Identification Of Residential Appliances Using A Nonintrusive Method, Sunil Semwal, Munendra Singh, Rai Sachindra Prasad
Turkish Journal of Electrical Engineering and Computer Sciences
Identifying and controlling (ON/OFF) electrical appliance(s) from a remote location is an essential part of energy management. This motivated us to design a system that can collect the aggregate load signature from a single point, obtain the features, and finally identify the ON state of electrical appliance(s). The proposed disaggregation technique can be divided into two modules: the first part proposes an electrical installation system to disaggregate the appliance at the circuit level, whereas the second part consists of feature selection, dimension reduction, and classification algorithms. Load signatures of electrical appliances were combined with white Gaussian noise to analyze how …
A Real-Time American Sign Language Word Recognition System Based On Neural Networks And A Probabilistic Model, Neelesh Sarawate, Ming Chan. Leu, Cemi̇l Öz
A Real-Time American Sign Language Word Recognition System Based On Neural Networks And A Probabilistic Model, Neelesh Sarawate, Ming Chan. Leu, Cemi̇l Öz
Turkish Journal of Electrical Engineering and Computer Sciences
The development of an American Sign Language (ASL) word recognition system based on neural networks and a probabilistic model is presented. We use a CyberGlove and a Flock of Birds motion tracker to extract the gesture data. The finger joint angle data obtained from the sensory glove defines the handshape while the data from the motion tracker describes the trajectory of the hand movement. The four gesture features, namely handshape, hand position, hand orientation, and hand movement, are recognized using different functions that include backpropagation neural networks. The sequence of these features is used to generate a specific sign or …
Performance Of Support Vector Regression Machines On Determining The Magnetic Characteristics Of The E-Core Transverse Flux Machine, Çi̇ğdem Gündoğan Türker, Feri̇ha Erfan Kuyumcu, Nurhan Türker Tokan
Performance Of Support Vector Regression Machines On Determining The Magnetic Characteristics Of The E-Core Transverse Flux Machine, Çi̇ğdem Gündoğan Türker, Feri̇ha Erfan Kuyumcu, Nurhan Türker Tokan
Turkish Journal of Electrical Engineering and Computer Sciences
The E-core transverse flux machine (ETFM) has major advantages with its different and unique structure in conventional electrical machines. It is a combination of transverse flux and reluctance principle. In this work, support vector regression machines (SVRMs) are used to obtain the magnetic characteristic parameters of the ETFM for the first time and it is compared with its artificial neural network model. The data for the training and testing of the SVRMs are obtained from experimental measurements. It is proven that SVRMs can conveniently be used in the modeling of the magnetic behaviors of highly nonlinear ETFM with better accuracy …
Mechanical Fault Detection In Permanent Magnet Synchronous Motors Using Equal Width Discretization-Based Probability Distribution And A Neural Network Model, Mehmet Akar, Mahmut Heki̇m, Umut Orhan
Mechanical Fault Detection In Permanent Magnet Synchronous Motors Using Equal Width Discretization-Based Probability Distribution And A Neural Network Model, Mehmet Akar, Mahmut Heki̇m, Umut Orhan
Turkish Journal of Electrical Engineering and Computer Sciences
This paper focuses on detecting the static eccentricity and bearing faults of a permanent magnet synchronous motor (PMSM) using probability distributions based on equal width discretization (EWD) and a multilayer perceptron neural network (MLPNN) model. In order to achieve this, the PMSM stator current values were measured in the cases of healthy, static eccentricity, and bearing faults for the conditions of three speeds and five loads. The data was discretized into several ranges through the EWD method, the probability distributions were computed according to the number of current values belonging to each range, and these distributions were then used as …
An Artificial Neural Network Approach For Sensorless Speed Estimation Via Rotor Slot Harmonics, Hayri̇ Arabaci
An Artificial Neural Network Approach For Sensorless Speed Estimation Via Rotor Slot Harmonics, Hayri̇ Arabaci
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a sensorless speed estimation method with an artificial neural network for squirrel cage induction motors is presented. Motor current is generally used for sensorless speed estimation. Rotor slot harmonics are available in the frequency spectrum of the current. The frequency components of these determined harmonics are used to estimate the speed of the motor in which the number of rotor slots is given. In the literature, individual algorithms have been used to calculate the speed from the slot harmonics. Unlike the literature, in the proposed method, an artificial neural network is used to extract the speed from …