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Articles 331 - 360 of 3020
Full-Text Articles in Physical Sciences and Mathematics
Scale-Invariant Histogram Of Oriented Gradients: Novel Approach For Pedestriandetection In Multiresolution Image Dataset, Sweta Panigrahi, Surya Narayana Raju Undi
Scale-Invariant Histogram Of Oriented Gradients: Novel Approach For Pedestriandetection In Multiresolution Image Dataset, Sweta Panigrahi, Surya Narayana Raju Undi
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a scale-invariant histogram of oriented gradients (SI-HOG) for pedestrian detection. Most of the algorithms for pedestrian detection use the HOG as the basic feature and combine other features with the HOG to form the feature set, which is usually applied with a support vector machine (SVM). Hence, the HOG feature is the most efficient and fundamental feature for pedestrian detection. However, the HOG feature produces feature vectors of different lengths for different image resolutions; thus, the feature vectors are incomparable for the SVM. The proposed method forms a scale-space pyramid wherein the histogram bin is calculated. Thus, …
Gene Expression Data Classification Using Genetic Algorithm-Basedfeature Selection, Öznur Si̇nem Sönmez, Mustafa Dağteki̇n, Tolga Ensari̇
Gene Expression Data Classification Using Genetic Algorithm-Basedfeature Selection, Öznur Si̇nem Sönmez, Mustafa Dağteki̇n, Tolga Ensari̇
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, hybrid methods are proposed for feature selection and classification of gene expression datasets. In the proposed genetic algorithm/support vector machine (GA-SVM) and genetic algorithm/k nearest neighbor (GA-KNN) hybrid methods, genetic algorithm is improved using Pearson's correlation coefficient, Relief-F, or mutual information. Crossover and selection operations of the genetic algorithm are specialized. Eight different gene expression datasets are used for classification process. The classification performances of the proposed methods are compared with the traditional GA-KNN and GA-SVM wrapper methods and other studies in the literature. Classification results demonstrate that higher accuracy rates are obtained with the proposed methods …
A Hybrid Approach Based On Transfer And Ensemble Learning For Improvingperformances Of Deep Learning Models On Small Datasets, Tunç Gülteki̇n, Aybars Uğur
A Hybrid Approach Based On Transfer And Ensemble Learning For Improvingperformances Of Deep Learning Models On Small Datasets, Tunç Gülteki̇n, Aybars Uğur
Turkish Journal of Electrical Engineering and Computer Sciences
The need for high-volume data is one of the challenging requirements of the deep learning methods, and it makes it harder to apply deep learning algorithms to domains in which the data sources are limited, in other words, small. These domains may vary from medical diagnosis to satellite imaging. The performances of the deep learning methods on small datasets can be improved by the approaches such as data augmentation, ensembling, and transfer learning. In this study, we propose a new approach that utilizes transfer learning and ensemble methods to increase the accuracy rates of convolutional neural networks for classification tasks …
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, …
Leukocyte Classification Based On Feature Selection Using Extra Trees Classifier: Atransfer Learning Approach, Diana Baby, Sujitha Juliet Devaraj, Jude Hemanth, Anishin Raj M M
Leukocyte Classification Based On Feature Selection Using Extra Trees Classifier: Atransfer Learning Approach, Diana Baby, Sujitha Juliet Devaraj, Jude Hemanth, Anishin Raj M M
Turkish Journal of Electrical Engineering and Computer Sciences
The criticality of investigating the white blood cell (WBC) count cannot be underestimated, as white blood cells are an important component of the body's defence system. From helping to diagnose hidden infections to insinuating the presence of comorbidities like immunodeficiency, an accurate white blood cell count can contribute significantly to shape a physician?s assessment. The manual process performed by the pathologists for the classification of WBCs is a time consuming and tedious task, which is further disadvantaged by a lack of accuracy. This study concentrates on the automatic detection and classification of WBC without data augmentation into four subtypes such …
Evolutionary Neural Networks For Improving The Prediction Performance Ofrecommender Systems, Berna Şeref, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel
Evolutionary Neural Networks For Improving The Prediction Performance Ofrecommender Systems, Berna Şeref, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel
Turkish Journal of Electrical Engineering and Computer Sciences
Recommender systems provide recommendations to users using background data such as ratings of users about items and features of items. These systems are used in several areas such as e-commerce, news websites, and article websites. By using recommender systems, customers are provided with relevant data as soon as possible and are able to make good decisions. There are more studies about recommender systems and improving their performance. In this study, prediction performances of neural networks are evaluated and their performances are improved using genetic algorithms. Performances obtained in this study are compared with those of other studies. After that, superiority …
A Novel Optimum Pi Controller Design Based On Stability Boundary Locussupported Particle Swarm Optimization In Avr System, Mahmut Temel Özdemi̇r
A Novel Optimum Pi Controller Design Based On Stability Boundary Locussupported Particle Swarm Optimization In Avr System, Mahmut Temel Özdemi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
This study proposes a new approach that combines stability and optimization in the design of proportional? integral (PI) controller of automatic voltage regulators (AVR) of synchronous generators with variable system parameters. Thanks to this approach, a PI controller, providing the desired performance and the stability of the AVR system, has been designed. The approach follows a method investigating the PI gain values to achieve the desired goals. In the first step of the study, a new stability boundary locus is calculated for the case in which AVR system?s parameters have changed. The stability boundary locus (SBL) method is a graphic-based …
A Software Availability Model Based On Multilevel Software Rejuvenation Andmarkov Chain, Zahra Rahmani Ghobadi, Hassan Rashidi
A Software Availability Model Based On Multilevel Software Rejuvenation Andmarkov Chain, Zahra Rahmani Ghobadi, Hassan Rashidi
Turkish Journal of Electrical Engineering and Computer Sciences
Increasing use of software, rapid and unavoidable changes in the operational environment bring many problemsfor software engineers. One of these problems is the aging and degradation of software performance. Software rejuvenationis a proactive and preventive approach to counteract software aging. Generally, when software is initiated, amounts ofmemory are allocated. Then, the body of software is executed for providing a service and when the software is terminated,the allocated memory is released. In this paper, a rejuvenation model based on multilevel software rejuvenation andMarkov chain presented. In this model, the system performance as a result of degraded physical memory and memoryusage 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 …
Maritime Automatic Target Recognition For Ground-Based Scanning Radars By Usingsequential Range Profiles, Baki̇ Bati, Nevci̇han Duru
Maritime Automatic Target Recognition For Ground-Based Scanning Radars By Usingsequential Range Profiles, Baki̇ Bati, Nevci̇han Duru
Turkish Journal of Electrical Engineering and Computer Sciences
Classification of marine targets using radar data products has become an important area for modern researchsociety. However, due to several reasons such as the similarity between ship structures and spatial specifications,classification of marine targets constitutes a challenging problem. In almost all of the studies, this problem has beenhandled by focusing on a single instance of range profiles or synthetic aperture radar data. However, this approachis seen to achieve only a particular success. This study introduces a novel classification approach that is shown toprovide additional classification enhancements by exploiting the extra information extracted from sequential rangeprofiles generated by ground-based marine surveillance …
Haze-Level Prior Approach To Enhance Object Visibility Under Atmosphericdegradation, Vijaya Lakshmi Thirumala, Venkata Satyanarayana Karanam, Pratap Reddy Lankireddy, Aruna Kumari Kakumani, Rakesh Kumar Yacharam
Haze-Level Prior Approach To Enhance Object Visibility Under Atmosphericdegradation, Vijaya Lakshmi Thirumala, Venkata Satyanarayana Karanam, Pratap Reddy Lankireddy, Aruna Kumari Kakumani, Rakesh Kumar Yacharam
Turkish Journal of Electrical Engineering and Computer Sciences
Outdoor captured scenes are degraded by atmospheric particles and water droplets. Due to scattering andabsorption effects in the atmosphere, degraded images lose contrast and color fidelity. Performances of the computervision algorithms are bound to suffer from low-contrast scene radiance. In many single-image dehazing models, thelarger the deviation in estimation of the key parameters such as transmission map and atmospheric light, the higherthe halo artifacts and loss of fine details in the dehazed image. The available models assume that the scattering lightis independent of wavelength, as the size of the atmospheric particles is larger compared to the wavelength of light.The model …
Learning Multiview Deep Features From Skeletal Sign Language Videos Forrecognition, Ashraf Ali Shaik, Venkata Durga Prasad Mareedu, Venkata Vijaya Kishore Polurie
Learning Multiview Deep Features From Skeletal Sign Language Videos Forrecognition, Ashraf Ali Shaik, Venkata Durga Prasad Mareedu, Venkata Vijaya Kishore Polurie
Turkish Journal of Electrical Engineering and Computer Sciences
The most challenging objective in machine translation of sign language has been the machine?s inability tolearn interoccluding finger movements during an action process. This work addresses the problem of teaching a deeplearning model to recognize differently oriented skeletal data. The multi-view 2D skeletal sign language video data isobtained using 3D motion-captured system. A total of 9 signer views were used for training the proposed network andthe 6 for testing and validation. In order to obtain multi-view deep features for recognition, we proposed an end-to-endtrainable multistream convolutional neural network (CNN) with late feature fusion. The fused multiview features arethen inputted to …
Analytical Modeling And Study On Noise Characteristics Of Rotor Eccentric Spmsmwith Unequal Magnetic Poles Structure, Pengpeng Xia, Shenbo Yu, Rutong Dou, Fengchen Zhai
Analytical Modeling And Study On Noise Characteristics Of Rotor Eccentric Spmsmwith Unequal Magnetic Poles Structure, Pengpeng Xia, Shenbo Yu, Rutong Dou, Fengchen Zhai
Turkish Journal of Electrical Engineering and Computer Sciences
he establishment of the analytical model of the rotor eccentric surface-mounted permanent magnet syn-chronous motor (SPMSM) with unequal magnetic poles structure will be beneficial to calculating the magnetic field andstudying noise characteristics quickly. Based on the equivalent surface current (ESC) method and equivalent boundarymethod, the analytical model of the rotor eccentric SPMSM with unequal magnetic poles structure is proposed. Duringthe modeling process, the magnetic field produced by a permanent magnet (PM) is obtained using the ESC method, andthe effect on the air gap magnetic field, which arised from stator, is replaced with the concentric current sheet (CCS)magnetic field. And, the …
Dual Bit Control Low-Power Dynamic Content Addressable Memory Design For Iotapplications, V V Satyanarayana Satti, Sridevi Sriadibhatla
Dual Bit Control Low-Power Dynamic Content Addressable Memory Design For Iotapplications, V V Satyanarayana Satti, Sridevi Sriadibhatla
Turkish Journal of Electrical Engineering and Computer Sciences
The Internet of things (IoT) is an emerging area in the semiconductor industry for low-power and high-speedapplications. Many search engines of IoT applications require low power consumption and high-speed content addressablememory (CAM) devices for the transmission of data packets between servers and end devices. A CAM is a hardwaredevice used for transfer of packets in a network router with high speed at the cost of power consumption. In this paper,a new dual bit control precharge free (PF) dynamic content addressable memory (DCAM) has been introduced. Theproposed design uses a new charge control circuitry, which is used to control the dual …
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 …
Information Retrieval-Based Bug Localization Approach With Adaptive Attributeweighting, Mustafa Erşahi̇n, Semi̇h Utku, Deni̇z Kilinç, Buket Erşahi̇n
Information Retrieval-Based Bug Localization Approach With Adaptive Attributeweighting, Mustafa Erşahi̇n, Semi̇h Utku, Deni̇z Kilinç, Buket Erşahi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
Software quality assurance is one of the crucial factors for the success of software projects. Bug fixing has an essential role in software quality assurance, and bug localization (BL) is the first step of this process. BL is difficult and time-consuming since the developers should understand the flow, coding structure, and the logic of the program. Information retrieval-based bug localization (IRBL) uses the information of bug reports and source code to locate the section of code in which the bug occurs. It is difficult to apply other tools because of the diversity of software development languages, design patterns, and development …
Determination Of Pneumonia In X-Ray Chest Images By Using Convolutionalneural Network, Özlem Polat, Zümray Ölmez, Tamer Ölmez
Determination Of Pneumonia In X-Ray Chest Images By Using Convolutionalneural Network, Özlem Polat, Zümray Ölmez, Tamer Ölmez
Turkish Journal of Electrical Engineering and Computer Sciences
Pneumonia is one of the major diseases that cause a lot of deaths all over the world. Determining pneumonia from chest X-ray (CXR) images is an extremely difficult and important image processing problem. The discrimination of whether pneumonia is of bacterium or virus origin has also become more important during the pandemic. Automatic determination of the presence and origin of pneumonia is crucial for speeding up the treatment process and increasing the patient's survival rate. In this study, a convolutional neural network (CNN) framework is proposed for detection of pneumonia from CXR images. Two different binary CNNs and a triple …
New Hyperchaotic System With Single Nonlinearity, Its Electronic Circuit Andencryption Design Based On Current Conveyor, Anitha Karthikeyan, Serdar Çi̇çek, Karthikeyan Rajagopal, Prakash Duraisamy, Ashokkumar Srinivasan
New Hyperchaotic System With Single Nonlinearity, Its Electronic Circuit Andencryption Design Based On Current Conveyor, Anitha Karthikeyan, Serdar Çi̇çek, Karthikeyan Rajagopal, Prakash Duraisamy, Ashokkumar Srinivasan
Turkish Journal of Electrical Engineering and Computer Sciences
Nowadays, hyperchaotic system (HCSs) have been started to be used in engineering applications because they have complex dynamics, randomness, and high sensitivity. For this purpose, HCSs with different features have been introduced in the literature. In this work, a new HCS with a single discontinuous nonlinearity is introduced and analyzed. The proposed system has one saddle focus equilibrium. When the dynamic properties and bifurcation graphics of the system are analyzed, it is determined that the proposed system exhibits the complex phenomenon of multistability. Moreover, analog electronic circuit design of the proposed system is performed with positive second-generation current conveyor. In …
A Novel Hybrid Global Optimization Algorithm Having Training Strategy: Hybridtaguchi-Vortex Search Algorithm, Mustafa Saka, Meli̇h Çoban, İbrahi̇m Eke, Suleyman Sungur Tezcan, Müslüm Cengi̇z Taplamacioğlu
A Novel Hybrid Global Optimization Algorithm Having Training Strategy: Hybridtaguchi-Vortex Search Algorithm, Mustafa Saka, Meli̇h Çoban, İbrahi̇m Eke, Suleyman Sungur Tezcan, Müslüm Cengi̇z Taplamacioğlu
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a novel hybrid Taguchi-vortex search algorithm (HTVS) is proposed for solving global optimization problems. Taguchi orthogonal approximation and vortex search algorithm (VS) are hybridized in presenting method. In HTVS, orthogonal arrays in the Taguchi method are trained and obtained better solutions are used to find global optima in VS. Thus, HTVS has better relation between exploration and exploitation, and it exhibits more powerful approximation to find global optimum value. Proposed HTVS algorithm is applied to sixteen well-known benchmark optimization test functions with different dimensions. The results are compared with the Taguchi orthogonal array approximation (TOAA), vortex search …
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 …
Deep Q-Network-Based Noise Suppression For Robust Speech Recognition, Tae-Jun Park, Joon-Hyuk Chang
Deep Q-Network-Based Noise Suppression For Robust Speech Recognition, Tae-Jun Park, Joon-Hyuk Chang
Turkish Journal of Electrical Engineering and Computer Sciences
This study develops the deep Q-network (DQN)-based noise suppression for robust speech recognition purposes under ambient noise. We thus design a reinforcement algorithm that combines DQN training with a deep neural networks (DNN) to let reinforcement learning (RL) work for complex and high dimensional environments like speech recognition. For this, we elaborate on the DQN training to choose the best action that is the quantized noise suppression gain by the observation of noisy speech signal with the rewards of DQN including both the word error rate (WER) and objective speech quality measure. Experiments demonstrate that the proposed algorithm improves speech …
An Admm-Based Incentive Approach For Cooperative Data Analysis In Edgecomputing, Weiwei Fang, Xue Wang, Qingli Wang, Yi Ding
An Admm-Based Incentive Approach For Cooperative Data Analysis In Edgecomputing, Weiwei Fang, Xue Wang, Qingli Wang, Yi Ding
Turkish Journal of Electrical Engineering and Computer Sciences
Edge computing is a new paradigm that provides data processing capabilities at the network edge. In view of the uneven data distribution and the constrained onboard resource, an edge device often needs to call for a number of neighboring devices as followers to cooperate on data analysis tasks. However, these followers may be rational and selfish, having their private optimization objectives such as energy efficiency. Therefore, the leader device needs to incentivize the followers to achieve a certain global objective, e.g., maximizing task accomplishment, rather than their own objectives. In this paper, we model the aforementioned challenges in edge computing …
Privacy Preserving Hybrid Recommender System Based On Deep Learning, Sangeetha Selvaraj, Sudha Sadasivam Gangadharan
Privacy Preserving Hybrid Recommender System Based On Deep Learning, Sangeetha Selvaraj, Sudha Sadasivam Gangadharan
Turkish Journal of Electrical Engineering and Computer Sciences
Deep learning models are widely being used to provide relevant recommendations in hybrid recommender systems. These hybrid systems combine the advantages of both content based and collaborative filtering approaches. However, these learning systems hamper the user privacy and disclose sensitive information. This paper proposes a privacy preserving deep learning based hybrid recommender system. In hybrid deep neural network, user?s side information such as age, location, occupation, zip code along with user rating is embedded and provided as input. These embedding?s pose a severe threat to individual privacy. In order to eliminate this breach of privacy, we have proposed a private …
Robust And Efficient Ebg-Backed Wearable Antenna For Ism Applications, Ayesha Saeed, Asma Ejaz, Humayun Shahid, Yasar Amin, Hannu Tenhunen
Robust And Efficient Ebg-Backed Wearable Antenna For Ism Applications, Ayesha Saeed, Asma Ejaz, Humayun Shahid, Yasar Amin, Hannu Tenhunen
Turkish Journal of Electrical Engineering and Computer Sciences
A structurally compact, semiflexible wearable antenna composed of a distinctively miniaturized electromagnetic band gap (EBG) structure is presented in this work. Designed for body-centric applications in the 5.8 GHz band, the design draws heavily from a novel planar geometry realized on Rogers RT/duroid 5880 laminate with a compact physical footprint spanning lateral dimensions of $0.6$$\lambda$$_0$$\times$$0.06$$\lambda$$_0$. Incorporating a 2$\times$2 EBG structure at the rear of the proposed design ensures sufficient isolation between the body and the antenna, doing away with the performance degradation associated with high permittivity of the tissue layer. The peculiar antenna geometry allows for reduced backward radiation and …
Improved Online Sequential Extreme Learning Machine: Os-Celm, Olcay Tosun, Recep Eryi̇ği̇t
Improved Online Sequential Extreme Learning Machine: Os-Celm, Olcay Tosun, Recep Eryi̇ği̇t
Turkish Journal of Electrical Engineering and Computer Sciences
Online learning methods (OLM) have been gaining traction as a solution to classification problems because of rapid renewal and fast growth in volume of available data. ELM-based sequential learning (OS-ELM) is one of the most frequently used online learning methodologies partly due to fast training algorithm but suffers from inefficient use of its hidden layers due to the random assignment of the parameters of those layers. In this study, we propose an improved online learning model called online sequential constrained extreme learning machine (OS-CELM), which replaces the random assignment of those parameters with better generalization performance using the CELM method …
Evaluation Of Cable And Busbar System In Multiconductor Distribution Systems Interms Of Current And Magnetic Field Distributions, Yunus Berat Demi̇rol, Mehmet Aytaç Çinar, Bora Alboyaci
Evaluation Of Cable And Busbar System In Multiconductor Distribution Systems Interms Of Current And Magnetic Field Distributions, Yunus Berat Demi̇rol, Mehmet Aytaç Çinar, Bora Alboyaci
Turkish Journal of Electrical Engineering and Computer Sciences
The selection of power distribution components is of great importance in electrical facilities. Cable and busbar systems are widely used applications, such as electric vehicle charge stations, microgrids and energy storage systems, for power distribution in the distribution grid. In this study, the current distribution on the parallel conductors and magnetic field distributions around cable and busbar structures is evaluated for studied application where the power is distributed using a cable system between a converter transformer and a converter. All modeling and analyzes are conducted using ANSYS Electronics Suite software, by applying balanced and pure sinusoidal current excitation. Obtained results …
Hc-Fft: Highly Configurable And Efficient Fft Implementation On Fpga, Paki̇ze Ergül, H. Fati̇h Uğurdağ, Doğancan Davutoğlu
Hc-Fft: Highly Configurable And Efficient Fft Implementation On Fpga, Paki̇ze Ergül, H. Fati̇h Uğurdağ, Doğancan Davutoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
FFT is one of the basic building blocks in many applications such as sensors, radars, communications. For some applications, e.g., real-time spectral monitoring and analysis, FFT needs to be "run-time configurable" so that the system is real-time. When examining the previous work on configurable real-time (FPGA-based) FFT implementations, we see that the degree of configurability is less than what is desired. In this paper, a new FFT architecture is proposed, which has a high degree of run-time configurability and yet does not compromise area or throughput. The configurable parameters of this design are the number of FFT points (up to …
Opinion Dynamics Of Stubborn Agents Under The Presence Of A Troll Asdifferential Game, Aykut Yildiz, Ari̇f Bülent Özgüler
Opinion Dynamics Of Stubborn Agents Under The Presence Of A Troll Asdifferential Game, Aykut Yildiz, Ari̇f Bülent Özgüler
Turkish Journal of Electrical Engineering and Computer Sciences
The question of whether opinions of stubborn agents result in Nash equilibrium under the presence of troll is investigated in this study. The opinion dynamics is modelled as a differential game played by n agents during a finite time horizon. Two types of agents, ordinary agents and troll, are considered in this game. Troll is treated as a malicious stubborn content maker who disagrees with every other agent. On the other hand, ordinary agents maintain cooperative communication with other ordinary agents and they disagree with the troll. Under this scenario, explicit expressions of opinion trajectories are obtained by applying Pontryagin?s …
Brain Tumor Detection From Mri Images With Using Proposed Deep Learningmodel: The Partial Correlation-Based Channel Selection, Atinç Yilmaz
Brain Tumor Detection From Mri Images With Using Proposed Deep Learningmodel: The Partial Correlation-Based Channel Selection, Atinç Yilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
A brain tumor is an abnormal growth of a mass or cell in the brain. Early diagnosis of the tumor significantly increases the chances of successful treatment. Artificial intelligence-based systems can detect the tumor in early stages. In this way, it could be possible to detect a tumor and resolve this problem that may endanger human life early. In the study, the partial correlation-based channel selection formula was presented that allowed the selection of the most prominent feature that differs from the other studies in the literature. Additionally, the multi-channel convolution structure was proposed for the feature network phase of …