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Robust Image Hashing Based On Structural And Perceptual Features Forauthentication Of Color Images, Muhammad Farhan Khan, Syed Muhammad Monir, Imran Naseem
Robust Image Hashing Based On Structural And Perceptual Features Forauthentication Of Color Images, Muhammad Farhan Khan, Syed Muhammad Monir, Imran Naseem
Turkish Journal of Electrical Engineering and Computer Sciences
Image hashing is one of the most celebrated techniques regarding the discipline of image forensics, imageretrieval, image indexing, content verification, and zero watermarking. For such sensitive and complex problems,generation of a unique and robust image hash is an utmost prerequisite for an image identifier driven from the perceptualcontents of an image. As a design perspective, it is essential for an image hash to have robustness and optimizeddiscriminative capability. We propose a robust image hashing technique by acquiring perceptual features based on anovel distance magnitude profile utilizing color pixel incongruity among the contiguous pixels, as well as producing astructural image for …
Dynamic Distributed Trust Management Scheme For The Internet Of Things, Syed Wasif Abbas Hamdani, Abdul Waheed Khan, Naima Iltaf, Javed Iqbal Bangash, Yawar Abbas Bangash, Asfandyar Khan
Dynamic Distributed Trust Management Scheme For The Internet Of Things, Syed Wasif Abbas Hamdani, Abdul Waheed Khan, Naima Iltaf, Javed Iqbal Bangash, Yawar Abbas Bangash, Asfandyar Khan
Turkish Journal of Electrical Engineering and Computer Sciences
The Internet of Things (IoT) comprises of a diverse network of homogeneous and heterogeneous nodesthat can be accessed through network ubiquitously. In unattended environments, the IoT devices are prone to variousattacks including ballot-stu?ing, bad-mouthing, self-promotion, on-off, opportunistic behavior attacks, etc. The on-offattack is di?icult to detect as nodes switch their behavior from normal to malicious alternatively. A trust managementmodel is a tool to defend the IoT system against malicious activities and provide reliable data exchange. The majorityof existing IoT trust management techniques are based on static reward and punishment values in pursuit of trustcomputation thereby allowing the misbehaving nodes to …
Sliding Mode Pll-Pdm Controller For Induction Heating System, Harun Özbay, Aki̇f Karafi̇l, Seli̇m Öncü
Sliding Mode Pll-Pdm Controller For Induction Heating System, Harun Özbay, Aki̇f Karafi̇l, Seli̇m Öncü
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, a sliding mode controlled phase locked loop (SMC-PLL) was developed for induction heating(IH) applications with a resonant inverter. PLL applications are widely used in induction heating applications to achievezero voltage switching and zero current switching. In many PLL applications, the frequency tracking is too slow andunreliable. Therefore, a sliding mode controller was developed to provide robust and fast PLL. Furthermore, a pulsedensity modulation (PDM) control strategy was developed to work at the resonant frequency for all power levels. ThePDM power control is a good solution for the design of high-frequency inverters because of a great reduction of …
An Efficient Deep Learning Based Fog Removal Model For Multimedia Applications, Gaurav Saxena, Sarita Singh Bhadauria
An Efficient Deep Learning Based Fog Removal Model For Multimedia Applications, Gaurav Saxena, Sarita Singh Bhadauria
Turkish Journal of Electrical Engineering and Computer Sciences
In the present era of technology, several applications such as surveillances systems, security and object recognitions mainly depend on the contents of an image. In this context, the hazy/foggy environment and/or other adverse climatic conditions degrade the image contents that severely influences the result of related applications. The effective haze removal from a single image decides the reliability of these systems. The convolutional neural network (CNN) based techniques are widely used among the available image dehazing methods. However, in CNN based image dehazing techniques, the robustness and accuracy of the learning models are based on the improvement of transmission estimation …
Determining And Evaluating New Store Locations Using Remote Sensing Andmachine Learning, Berkan Höke, Zeynep Zerri̇n Turgay, Cem Ünsalan, Hande Küçükaydin
Determining And Evaluating New Store Locations Using Remote Sensing Andmachine Learning, Berkan Höke, Zeynep Zerri̇n Turgay, Cem Ünsalan, Hande Küçükaydin
Turkish Journal of Electrical Engineering and Computer Sciences
Decision making for store locations is crucial for retail companies as the profit depends on the location. The key point for correct store location is profit approximation, which is highly dependent on population of the corresponding region, and hence, the volume of the residential area. Thus, estimating building volumes provides insight about the revenue if a new store is about to be opened there. Remote sensing through stereo/tri-stereo satellite images provides wide area coverage as well as adequate resolution for three dimensional reconstruction for volume estimation. We reconstruct 3D map of corresponding region with the help of semiglobal matching and …
A New Design Algorithm For The Pmhs Motor Considering The Combination Ratio, Ali Behniafar, Ahmad Darabi
A New Design Algorithm For The Pmhs Motor Considering The Combination Ratio, Ali Behniafar, Ahmad Darabi
Turkish Journal of Electrical Engineering and Computer Sciences
Recently, the hysteresis motors have a special significance in the nuclear industries. This is because these motors have some advantages such as low noise, high mechanical strength and group feeding ability. They also have some disadvantages that make some limitations for related industries. These disadvantages include low synchronization torque, low power factor, low efficiency, and hunting. One solution to reduce these disadvantages is to combine the hysteresis motor with the PM motor. This however requires a correct and flexible design procedure as well as an appropriate choice of the machine structure. Accordingly, this paper aims to present a new design …
Visual Object Detection For Autonomous Transport Vehicles In Smart Factories, Nazlican Gengeç, Onur Eker, Hakan Çevi̇kalp, Ahmet Yazici, Hasan Serhan Yavuz
Visual Object Detection For Autonomous Transport Vehicles In Smart Factories, Nazlican Gengeç, Onur Eker, Hakan Çevi̇kalp, Ahmet Yazici, Hasan Serhan Yavuz
Turkish Journal of Electrical Engineering and Computer Sciences
Autonomous transport vehicles (ATVs) are one of the most substantial components of smart factories of Industry 4.0. They are primarily considered to transfer the goods or perform some certain navigation tasks in the factory with self driving. The recent developments on computer vision studies allow the vehicles to visually perceive the environment and the objects in the environment. There are numerous applications especially for smart traffic networks in outdoor environments but there is lack of application and databases for autonomous transport vehicles in indoor industrial environments. There exist some essential safety and direction signs in smart factories and these signs …
Field-Programmable Gate Array (Fpga) Hardware Design And Implementation Ofa New Area Efficient Elliptic Curve Crypto-Processor, Muhammad Kashif, İhsan Çi̇çek
Field-Programmable Gate Array (Fpga) Hardware Design And Implementation Ofa New Area Efficient Elliptic Curve Crypto-Processor, Muhammad Kashif, İhsan Çi̇çek
Turkish Journal of Electrical Engineering and Computer Sciences
Elliptic curve cryptography provides a widely recognized secure environment for information exchange in resource-constrained embedded system applications, such as Internet-of-Things, wireless sensor networks, and radio frequency identification. As the elliptic-curve cryptography (ECC) arithmetic is computationally very complex, there is a need for dedicated hardware for efficient computation of the ECC algorithm in which scalar point multiplication is the performance bottleneck. In this work, we present an ECC accelerator that computes the scalar point multiplication for the NIST recommended elliptic curves over Galois binary fields by using a polynomial basis. We used the Montgomery algorithm with projective coordinates for the scalar …
A Hybrid Technique Using Modified Icp Algorithm For Faster And Automatic 2d &3d Microscopic Image Stitching In Cytopathologic Examination, Hülya Doğan, Eli̇f Baykal Kablan, Murat Eki̇nci̇, Mustafa Emre Erci̇n, Şafak Ersöz
A Hybrid Technique Using Modified Icp Algorithm For Faster And Automatic 2d &3d Microscopic Image Stitching In Cytopathologic Examination, Hülya Doğan, Eli̇f Baykal Kablan, Murat Eki̇nci̇, Mustafa Emre Erci̇n, Şafak Ersöz
Turkish Journal of Electrical Engineering and Computer Sciences
Due to the limitations of the light microscopic system such as limited depth of field and narrow field of view, entire sample areas are invisible and pathologists move the light microscope stage along the X - Y - Z axes with eye-hand coordination. In order to reduce the dependence on the pathologist and to allow whole sample areas to be examined in a short time without any control (without eye-hand coordination), this study creates 2D & 3D panoramic images with wide-view of sample in the light microscopic systems. According to our literature research, there is no study that creates 2D …
Performance Evaluation Of Hht And Wt For Detection Of Hif And Ct Saturationin Smart Grids, Saeid Heidari, Saeed Asgharigovar, Pouya Pourghasem, Heresh Seyedi, Ömer Usta
Performance Evaluation Of Hht And Wt For Detection Of Hif And Ct Saturationin Smart Grids, Saeid Heidari, Saeed Asgharigovar, Pouya Pourghasem, Heresh Seyedi, Ömer Usta
Turkish Journal of Electrical Engineering and Computer Sciences
Hilbert-Huang transform (HHT), continuous wavelet transform (CWT) and discrete wavelet transform (DWT) are well-known signal processing methods that are widely utilized for feature extraction and fault detection by protection systems in smart grids. In this paper, we assess the performances of these methods encountering challenging situations in distribution networks, i.e. high impedance arcing fault (HIF) and current transformer (CT) saturation. Low fault current amplitude in HIF case causes the overcurrent protection, which is the predominant protection method in distribution grids, to fail. Furthermore, some faults may lead to CT saturation, which may result in delayed operation of the relay. To …
Prediction Of Long-Term Physical Properties Of Low Density Polyethylene (Ldpe)Cable Insulation Materials By Artificial Neural Network Modeling Approach Underenvironmental Constraints, Ferhat Slimani, Abdallah Hedir, Mustapha Moudoud, Ali̇ Durmuş, Mounir Amir, Mohamed Megherbi
Prediction Of Long-Term Physical Properties Of Low Density Polyethylene (Ldpe)Cable Insulation Materials By Artificial Neural Network Modeling Approach Underenvironmental Constraints, Ferhat Slimani, Abdallah Hedir, Mustapha Moudoud, Ali̇ Durmuş, Mounir Amir, Mohamed Megherbi
Turkish Journal of Electrical Engineering and Computer Sciences
This study quantifies long-term physical properties of low density polyethylene (LDPE) cables insulations exposed to environmental constraints such as UV radiation and temperature via both experimental measurements and mathematical modeling approach. For this purpose, tensile test and electrical breakdown test were carried out to determine elongation at break, tensile strength, and dielectric strength of unaged and aged specimens, respectively. Experimental results showed that both UV and temperature exposures affected the LDPE properties, significantly. A supervised artificial neural network (ANN) trained by the Levenberg?Marquardt algorithm was designed for predicting the long-term characteristics of specimens and also for minimizing the experimental procedures. …
A New Distributed Anomaly Detection Approach For Log Ids Management Based Ondeep Learning, Murat Koca, Muhammed Ali̇ Aydin, Ahmet Sertbaş, Abdül Hali̇m Zai̇m
A New Distributed Anomaly Detection Approach For Log Ids Management Based Ondeep Learning, Murat Koca, Muhammed Ali̇ Aydin, Ahmet Sertbaş, Abdül Hali̇m Zai̇m
Turkish Journal of Electrical Engineering and Computer Sciences
Today, with the rapid increase of data, the security of big data has become more important than ever for managers. However, traditional infrastructure systems cannot cope with increasingly big data that is created like an avalanche. In addition, as the existing database systems increase licensing costs per transaction, organizations using information technologies are shifting to free and open source solutions. For this reason, we propose an anomaly attack detection model on Apache Hadoop distributed file system (HDFS), which stands out in open source big data analytics, and Apache Spark, which stands out with its speed performance in analysis to reduce …
Learning Prototypes For Multiple Instance Learning, Özgür Emre Si̇vri̇kaya, Mert Yüksekgönül, Mustafa Gökçe Baydoğan
Learning Prototypes For Multiple Instance Learning, Özgür Emre Si̇vri̇kaya, Mert Yüksekgönül, Mustafa Gökçe Baydoğan
Turkish Journal of Electrical Engineering and Computer Sciences
Multiple instance learning (MIL) is a weakly supervised learning method that works on the labeled bag of instances data. A prototypical network is a popular embedding approach in MIL. They overcome the common problems that other MIL approaches may have to deal with including dimensionality, loss of instance-level information, and complexity. They demonstrate competitive performance in classification. This work proposes a simple model that provides a permutation invariant prototype generator from a given MIL data set. We aim to find out prototypes in the feature space to map the collection of instances (i.e. bags) to a distance feature space and …
Joint Carrier Frequency And Phase Offset Estimation Algorithm For Cpm-Dsssbased Secure Point-To-Point Communication, Saima Shehzadi, Farzana Kulsoom, Muhammad Zeeshan, Qasim Umar Khan, Shahzad Amin Sheikh
Joint Carrier Frequency And Phase Offset Estimation Algorithm For Cpm-Dsssbased Secure Point-To-Point Communication, Saima Shehzadi, Farzana Kulsoom, Muhammad Zeeshan, Qasim Umar Khan, Shahzad Amin Sheikh
Turkish Journal of Electrical Engineering and Computer Sciences
A point-to-point (P2P) communication system based on the CPM-DSSS scheme ensures reliability, security, and antijamming capabilities. However, for reliable detection of data carrier synchronization of CPM-DSSS based system is one of the requirements. This paper presents a joint algorithm for carrier frequency offset (CFO) and carrier phase offset (CPO) estimation for CPM-DSSS based P2P system. The results indicate that the proposed CFO estimator is unbiased and can accurately estimate a wide range of offsets. Moreover, the proposed algorithm is compared with another research work. The results show that the proposed CFO and CPO estimation algorithm outperforms its counterpart with a …
A Hybrid Numerical Model For Long-Range Electromagnetic Wave Propagation, Gül Yesa Altun, Özlem Özgün
A Hybrid Numerical Model For Long-Range Electromagnetic Wave Propagation, Gül Yesa Altun, Özlem Özgün
Turkish Journal of Electrical Engineering and Computer Sciences
A hybrid numerical model is presented for solving long range electromagnetic wave propagation problems involving objects on or above the ground surface by hybridizing the two-way split-step parabolic equation (2W-SSPE) method with the method of moments (MoM). The advantages of the proposed model are twofold: (i) It reduces the staircasing error in irregular terrain modeling, which usually occurs when the standard SSPE method is used alone. This is achieved by employing the MoM to more accurately obtain the scattered fields from slanted/curved surfaces. (ii) It enables the SSPE method to handle the problems involving objects above the Earth's surface, which …
Comparison Of Metaheuristic Optimization Algorithms With A New Modifieddeb Feasibility Constraint Handling Technique, Murat Erhan Çi̇men, Zeynep Gari̇p, Ali̇ Fuat Boz
Comparison Of Metaheuristic Optimization Algorithms With A New Modifieddeb Feasibility Constraint Handling Technique, Murat Erhan Çi̇men, Zeynep Gari̇p, Ali̇ Fuat Boz
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, the modification of the Deb feasibility method is considered to solve the constrained optimization problems. In the developed modified Deb feasibility constraint method, the third rule in its procedure was revised in order to increase the performance of the Deb feasibility constraint handling method. The innovation in the method is based on generating a new individual by using both possible solutions that violate the constraints in the method used for solving the problem. In detail, discussions were given about the application and usefulness of six constrained handling techniques. Furthermore, genetic algorithm, particle swarm optimization, Harris hawks optimization, …
Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan
Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan
Turkish Journal of Electrical Engineering and Computer Sciences
Multimodal medical image fusion approaches have been commonly used to diagnose diseases and involve merging multiple images of different modes to achieve superior image quality and to reduce uncertainty and redundancy in order to increase the clinical applicability. In this paper, we proposed a new medical image fusion algorithm based on a convolutional neural network (CNN) to obtain a weight map for multiscale transform (curvelet/ non-subsampled shearlet transform) domains that enhance the textual and edge property. The aim of the method is achieving the best visualization and highest details in a single fused image without losing spectral and anatomical details. …
Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇
Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇
Turkish Journal of Electrical Engineering and Computer Sciences
Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a …
Impact Of Image Segmentation Techniques On Celiac Disease Classification Usingscale Invariant Texture Descriptors For Standard Flexible Endoscopic Systems, Manarbek Saken, Munkhtsetseg Banzragch Yağci, Nejat Yumuşak
Impact Of Image Segmentation Techniques On Celiac Disease Classification Usingscale Invariant Texture Descriptors For Standard Flexible Endoscopic Systems, Manarbek Saken, Munkhtsetseg Banzragch Yağci, Nejat Yumuşak
Turkish Journal of Electrical Engineering and Computer Sciences
Celiac disease (CD) is quite common and is a proximal small bowel disease that develops as a permanentintolerance to gluten and other cereal proteins in cereals. It is considered as one of the most di?icult diseases to diagnose.Histopathological evidence of small bowel biopsies taken during endoscopy remains the gold standard for diagnosis.Therefore, computer-aided detection (CAD) systems in endoscopy are a newly emerging technology to enhance thediagnostic accuracy of the disease and to save time and manpower. For this reason, a hybrid machine learning methodshave been applied for the CAD of celiac disease. Firstly, a context-based optimal multilevel thresholding technique wasemployed …
Ensemble Learning Of Multiview Cnn Models For Survival Time Prediction Of Braintumor Patients Using Multimodal Mri Scans, Abdela Ahmed Mossa, Ulus Çevi̇k
Ensemble Learning Of Multiview Cnn Models For Survival Time Prediction Of Braintumor Patients Using Multimodal Mri Scans, Abdela Ahmed Mossa, Ulus Çevi̇k
Turkish Journal of Electrical Engineering and Computer Sciences
Brain tumors have been one of the most common life-threatening diseases for all mankind. There have beenhuge efforts dedicated to the development of medical imaging techniques and radiomics to diagnose tumor patients quicklyand e?iciently. One of the main aims is to ensure that preoperative overall survival time (OS) prediction is accurate.Recently, deep learning (DL) algorithms, and particularly convolutional neural networks (CNNs) achieved promisingperformances in almost all computer vision fields. CNNs demand large training datasets and high computational costs.However, curating large annotated medical datasets are difficult and resource-intensive. The performances of singlelearners are also unsatisfactory for small datasets. Thus, this study …
Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia
Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia
Turkish Journal of Electrical Engineering and Computer Sciences
The main purpose of this paper is to process key information in medical text records and also classifypatients, per different levels of breast imaging-reporting and data system (BI-RADS). The BI-RADS is a scheme for thestandardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reportssupported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction fromtextual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosingkeywords from medical text reports. Word2vec and term frequency inverse document frequency (TFIDF) techniques areused for extracting features, finally, they are combined …
Improvements Of Torque Ripple Reduction In Dtc Im Drive Witharbitrary Number Of Voltage Intensities And Automatic Algorithm Modification, Marko Rosic, Sanja Antic, Milan Bebic
Improvements Of Torque Ripple Reduction In Dtc Im Drive Witharbitrary Number Of Voltage Intensities And Automatic Algorithm Modification, Marko Rosic, Sanja Antic, Milan Bebic
Turkish Journal of Electrical Engineering and Computer Sciences
Techniques of direct torque control (DTC) are very common in high-performance electric motor drives.Retaining the good features of the conventional DTC and reducing torque ripple have been the subject of many years ofresearch work on improving these algorithms. This paper presents the DTC algorithm with discretized voltage vectorsbased on the use of conventional switching table (ST-DTC). This algorithm enables a significant torque ripple reductionby defining the corresponding number of the given voltage intensity while retaining calculation simplicity and fast torqueresponse typical of the ST-DTC algorithms. The proposed algorithm has the ability for its automatic modificationdepending on the defined number of …
Optimal Planning Dg And Bes Units In Distribution System Consideringuncertainty Of Power Generation And Time-Varying Load, Mansur Khasanov, Salah Kamel, Ayman Awad, Francisco Jurado
Optimal Planning Dg And Bes Units In Distribution System Consideringuncertainty Of Power Generation And Time-Varying Load, Mansur Khasanov, Salah Kamel, Ayman Awad, Francisco Jurado
Turkish Journal of Electrical Engineering and Computer Sciences
Global environmental problems associated with traditional energy generation have led to a rapid increasein the use of renewable energy sources (RES) in power systems. The integration of renewable energy technologiesis commercially available nowadays, and the most common of such RES technology is photovoltaic (PV). This paperproposes an application of hybrid teaching-learning and artificial bee colony (TLABC) technique for determining theoptimal allocation of PV based distributed generation (DG) and battery energy storage (BES) units in the distributionsystem (DS) with the aim of minimizing the total power losses. Besides, some potential nodes identified by the powerloss sensitivity factor (PLSF). Thereupon TLABC is …
Placement Accuracy Algorithm For Smart Street Lights, Zulkifli Ishak, Wan Siti Halimatul Munirah Wan Ahmad, Nurul Asyikin Mohamed Radzi, Suhaila Sulaiman, Noor Emilia Ramli
Placement Accuracy Algorithm For Smart Street Lights, Zulkifli Ishak, Wan Siti Halimatul Munirah Wan Ahmad, Nurul Asyikin Mohamed Radzi, Suhaila Sulaiman, Noor Emilia Ramli
Turkish Journal of Electrical Engineering and Computer Sciences
The smart street light (SSL) system is an emerging technology in which a street light is equipped withan advanced control system for dimming and turning the light on or off. SSL also improves the maintenance work byproviding an enhanced inventory, which includes Global Positioning System (GPS) coordinates that can be retrieved froma GPS-enabled SSL. However, GPS coordinates may be inaccurate due to human error and GPS inaccuracy. This workproposes new algorithms for identifying human error and GPS inaccuracy in SSL installation by using distance analysisand the solving point-in-polygon method. The algorithms are important for inventory and maintenance purposes. Faultylight poles …
The Nearest Polyhedral Convex Conic Regions For High-Dimensional Classification, Hakan Çevi̇kalp, Emre Çi̇men, Gürkan Öztürk
The Nearest Polyhedral Convex Conic Regions For High-Dimensional Classification, Hakan Çevi̇kalp, Emre Çi̇men, Gürkan Öztürk
Turkish Journal of Electrical Engineering and Computer Sciences
In the nearest-convex-model type classifiers, each class in the training set is approximated with a convexclass model, and a test sample is assigned to a class based on the shortest distance from the test sample to these classmodels. In this paper, we propose new methods for approximating the distances from test samples to the convex regionsspanned by training samples of classes. To this end, we approximate each class region with a polyhedral convex conicregion by utilizing polyhedral conic functions (PCFs) and its extension, extended PCFs. Then, we derive the necessary formulations for computing the distances from test samples to these …
A Step-Down Isolated Three-Phase Igbt Boost Pfc Rectifier Using A Novel Controlalgorithm With A Novel Start-Up Method, Hüseyi̇n Köse, Mehmet Ti̇mur Aydemi̇r
A Step-Down Isolated Three-Phase Igbt Boost Pfc Rectifier Using A Novel Controlalgorithm With A Novel Start-Up Method, Hüseyi̇n Köse, Mehmet Ti̇mur Aydemi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
For some industrial converter applications such as battery chargers, a DC/DC converter is needed to stepdown the high DC (direct current) voltages generated by PWM (pulse with modulated) rectifiers to lower voltage levelssuch as 110V/220V. These both complicate the design and decrease the efficiency. In this paper, a novel topology thatincludes a step-down transformer and a novel control algorithm is proposed. The proposed current, which is synchronized,look-up table based, sinusoidal PWM control method (CS-LUT-SPWM method) using switching frequency-orientedsynchronization (SWFOS) results in a very fast PWM generation and stable operation. Finally, a new thyristor-basedstart-up circuit providing safe operation when there is …
Subjective Analysis Of Social Distance Monitoring Using Yolo V3 Architecture Andcrowd Tracking System, Muhammed Murat Özbek, Mustafa Syed, İlkay Öksüz
Subjective Analysis Of Social Distance Monitoring Using Yolo V3 Architecture Andcrowd Tracking System, Muhammed Murat Özbek, Mustafa Syed, İlkay Öksüz
Turkish Journal of Electrical Engineering and Computer Sciences
The lethal infection, World Health Organization (WHO) reported coronavirus (COVID-19) as a pandemic.Lack of proper vaccine, low levels of immunity against COVID-19 has led to vulnerability of the human beings. Due tolack of efficient vaccine treatment, the only options left to fight against this pandemic are lockdown and social distance.This work offers an autonomous monitoring system on social distancing using deep learning techniques. The proposedarchitecture tracks the humans on roads and calculates their distance between each other. This surveillance detects thefurore violation of social distance utilizing CCTV cameras. The proposed framework uses YOLO v3 object-detectionmodel built on COCO dataset and …
Design Of The Fractional Order Internal Model Controller Using The Swarmintelligence Techniques For The Coupled Tank System, Sateesh Kumar Vavilala, Vinopraba Thirumavalavan, Radhakrishnan Thota, Sivakumaran Natarajan
Design Of The Fractional Order Internal Model Controller Using The Swarmintelligence Techniques For The Coupled Tank System, Sateesh Kumar Vavilala, Vinopraba Thirumavalavan, Radhakrishnan Thota, Sivakumaran Natarajan
Turkish Journal of Electrical Engineering and Computer Sciences
The coupled tank system (comprising two tanks) is used in the chemical industries, water treatment plantsetc. Level control of the coupled tank system is a common problem in the process control industry. This work proposes afractional order internal model controller (FOIMC) with a higher order fractional filter for the level control of the coupledtank system. A first order plus delay time (FOPDT) model of the system is used in the controller design. FOIMC hasadvantages like robustness to changes in the system gain and extended stability margins. The proposed higher orderfractional filter makes the controller physically realizable and quickly roll off …
New Fail Operational Powernet Methods And Topologies For Automated Drivingwith Electric Vehicle, Ahmet Kiliç
New Fail Operational Powernet Methods And Topologies For Automated Drivingwith Electric Vehicle, Ahmet Kiliç
Turkish Journal of Electrical Engineering and Computer Sciences
Electric mobility and automation are important drivers for the future of the automotive industry. Thisrequires an extremely high level of safety, reliability, and efficiency of the energy supply in the vehicle compared to the stateof the art. It is not possible to fulfill these requirements with today's energy supply. To meet these requirements, a fault-operational, scalable powernet is needed. In this paper, a new methodology is presented for the development of powernetfor automated driving with electric vehicle. The new method enables the development of new fail operational powernettopologies, early detection of failures in powernet components and the fulfillment of automated …
A Deep Neural Network Classifier For P300 Bci Speller Based On Cohen's Classtime-Frequency Distribution, Hamed Ghazikhani, Modjtaba Rouhani
A Deep Neural Network Classifier For P300 Bci Speller Based On Cohen's Classtime-Frequency Distribution, Hamed Ghazikhani, Modjtaba Rouhani
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a new method of predicting the P300 component of an electroencephalography (EEG)signal to recognize the characters in a P300 brain-computer interface (BCI) speller accurately. This method consistsof a deep learning model and the nonlinear time-frequency features. It is believed that the combination of the deepmodel network and extracting the nonlinear features of the EEG led this research to a better prediction of the P300and, therefore, character recognition. Cohen's class distribution is used in order to extract the nonlinear features of theEEG. Evaluating all of the kernels, Butterworth found to be more informative and it produced better results. …