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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 …
Shapeshifter: A Morphable Microprocessor For Low Power, Nazli Tokatli, İsa Ahmet Güney, Sercan Sari, Merve Güney, Uğur Nezi̇r, Gürhan Küçük
Shapeshifter: A Morphable Microprocessor For Low Power, Nazli Tokatli, İsa Ahmet Güney, Sercan Sari, Merve Güney, Uğur Nezi̇r, Gürhan Küçük
Turkish Journal of Electrical Engineering and Computer Sciences
A composite core contains large and small heterogeneous microengines. The most important property of composite cores is their ability to select the most proper microengine for running applications to save power without sacrificing too much performance. To achieve this, a composite core tries to predict the performance of the passive microengine by collecting various processor statistics from the active microengine at runtime. In the method proposed in the literature, the microengine, which is more ideal for running the rest of the application, is determined by a migrationdecision circuitry that is bound to collected statistics and complex functions, which are run …
Comparative Review Of Disk Type And Unconventional Transverse Flux Machines:Performance Analysis, Erhan Tuncel, Emi̇n Yildiriz
Comparative Review Of Disk Type And Unconventional Transverse Flux Machines:Performance Analysis, Erhan Tuncel, Emi̇n Yildiriz
Turkish Journal of Electrical Engineering and Computer Sciences
Transverse flux machines (TFM) can be designed with high pole numbers, so they are very useful in directdrive systems with high torque density. Although many TFM models have been proposed to date, no detailed classification and comparison has been made before. Conventional TFMs have a high power and torque density, but low power factors and high cogging torques have prevented them from being widely used. However, especially with the new disk type TFMs proposed in recent years and the methods developed, these drawbacks have been reduced. In this paper, the TFMs proposed in recent years have been classified and their …
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 …
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. …
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 New Method For Optimal Expansion Planning In Electrical Energy Distributionnetworks With Distributed Generation Resources Considering Uncertainties, Amir Masoud Mohaghegh, S Yaser Derakhshandeh, Abbas Kargar
A New Method For Optimal Expansion Planning In Electrical Energy Distributionnetworks With Distributed Generation Resources Considering Uncertainties, Amir Masoud Mohaghegh, S Yaser Derakhshandeh, Abbas Kargar
Turkish Journal of Electrical Engineering and Computer Sciences
The present study aims to introduce a robust model for distribution network expansion planning considering system uncertainties. The proposed method determines optimal size and placement of distributed generation resources, as well as installation and reinforcement of feeders and substations. This model is designed to minimize cost and to determine the best time for the installation of equipment in the expansion planning. In the proposed expansion planning, the fuzzy logic theory is employed to model uncertainties of loads and energy price. Also, since the proposed model is a nonlinear and nonconvex optimization problem, a tri-stage algorithm is developed to solve it. …
Analyzing The Performances Of Evolutionary Multi-Objective Optimizers On Designoptimization Of Robot Gripper Configurations, Murat Dörterler, Ümi̇t Ati̇la, Rafet Durgut, İsmai̇l Şahi̇n
Analyzing The Performances Of Evolutionary Multi-Objective Optimizers On Designoptimization Of Robot Gripper Configurations, Murat Dörterler, Ümi̇t Ati̇la, Rafet Durgut, İsmai̇l Şahi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
Robot grippers are widely used in a variety of areas requiring automation, precision, and safety. The performance of the grippers is directly associated with their design. In this study, four different multiobjective metaheuristic algorithms including particle swarm optimization (MOPSO), artificial algae algorithm (MOAAA), grey wolf optimizer (MOGWO) and nondominated sorting genetic algorithm (NSGA-II) were applied to two different configurations of highly nonlinear and multimodal robot gripper design problem including two objective functions and a certain number of constraints. The first objective is to minimize the difference between minimum and maximum forces for the assumed range in which the gripper ends …
A Novel Fibonacci Hash Method For Protein Family Identification By Usingrecurrent Neural Networks, Talha Burak Alakuş, İbrahi̇m Türkoğlu
A Novel Fibonacci Hash Method For Protein Family Identification By Usingrecurrent Neural Networks, Talha Burak Alakuş, İbrahi̇m Türkoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Identification and classification of protein families are one of the most significant problem in bioinformatics and protein studies. It is essential to specify the family of a protein since proteins are highly used in smart drug therapies, protein functions, and, in some cases, phylogenetic trees. Some sequencing techniques provide researchers to identify the biological similarities of protein families and functions. Yet, determining these families with sequencing applications requires huge amount of time. Thus, a computer and artificial intelligence based classification system is needed to save time and avoid complexity in protein classification process. In order to designate the protein families …
Efficient Hybrid Passive Method For The Detection And Localization Of Copy-Moveand Spliced Images, Navneet Kaur, Neeru Jindal, Kulbir Singh
Efficient Hybrid Passive Method For The Detection And Localization Of Copy-Moveand Spliced Images, Navneet Kaur, Neeru Jindal, Kulbir Singh
Turkish Journal of Electrical Engineering and Computer Sciences
Digital passive image forgery methods are extensively used to verify the authenticity and integrity of images.Splicing and copy-move are the most common types of passive digital image forgeries. Several approaches have beenproposed to detect these forgeries separately, but very few approaches are available that can detect them simultaneously.However, a more e?icient method is still in demand to meet the day-to-day challenges to detect these forgeries at thesame time. So, a passive hybrid approach based on discrete fractional cosine transform (DFrCT) and local binarypattern (LBP) is proposed to detect copy-move and splicing forgeries simultaneously. The extra parameter i.e. fractionalparameter of DFrCT …
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 …
An Evolutionary-Based Image Classification Approach Through Facial Attributes, Seli̇m Yilmaz, Cemi̇l Zalluhoğlu
An Evolutionary-Based Image Classification Approach Through Facial Attributes, Seli̇m Yilmaz, Cemi̇l Zalluhoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
With the recent developments in technology, there has been a significant increase in the studies on analysisof human faces. Through automatic analysis of faces, it is possible to know the gender, emotional state, and even theidentity of people from an image. Of them, identity or face recognition has became the most important task whichhas been studied for a long time now as it is crucial to take measurements for public security, credit card verification,criminal identification, and the like. In this study, we have proposed an evolutionary-based framework that relies ongenetic programming algorithm to evolve a binary- and multilabel image classifier …
Heuristic Based Binary Grasshopper Optimization Algorithm To Solve Unitcommitment Problem, Muhammad Shahid, Tahir Nadeem Malik, Ahsan Said
Heuristic Based Binary Grasshopper Optimization Algorithm To Solve Unitcommitment Problem, Muhammad Shahid, Tahir Nadeem Malik, Ahsan Said
Turkish Journal of Electrical Engineering and Computer Sciences
The unit commitment problem in power system is a highly nonlinear, nonconvex, multiconstrained, complex,highly dimensional, mixed integer and combinatorial generation selection problem. The phenomenon of committing anddecommitting represents a discrete problem that requires binary/discrete optimization techniques to tackle with unitcommitment optimization problem. The key functions of the unit commitment optimization problem involve decidingwhich units to commit and then to decide their optimum power (economic dispatch). This paper confers a binarygrasshopper optimization algorithm to solve the unit commitment optimization problem under multiple constraints.The grasshopper optimization algorithm is a metaheuristic, multiple solutions-based algorithm inspired by the naturalswarming behavior of grasshopper towards food. …
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 …
An Adaptive Element Division Algorithm For Accurate Evaluation Of Singular Andnear Singular Integrals In 3d, Hakan Bayindir, Besi̇m Baranoğlu, Ali̇ Yazici
An Adaptive Element Division Algorithm For Accurate Evaluation Of Singular Andnear Singular Integrals In 3d, Hakan Bayindir, Besi̇m Baranoğlu, Ali̇ Yazici
Turkish Journal of Electrical Engineering and Computer Sciences
An adaptive algorithm for evaluation of singular and near singular integrals in 3D is presented. The algorithmis based on successive adaptive/selective subdivisions of the element until a prescribed error criteria is met. For evaluatingthe integrals in each subdivision, Gauss quadrature is applied. The method is computationally simple, memory efficientand can be applied for both triangular and quadrilateral elements, including the elements with nonplanar and/or curvedsurfaces. To assess the method, several examples are discussed. It has shown that the algorithm performs well forsingular and near-singular integral examples presented in the paper and evaluates the integrals with very high accuracy.
A Geodesic Deployment And Radial Shaped Clustering (Rsc) Algorithm Withstatistical Aggregation In Sensor Networks, Lalitha Krishnasamy, Thangarajan Ramasamy, Rajesh Kumar Dhanaraj, Poongodi Chinnasamy
A Geodesic Deployment And Radial Shaped Clustering (Rsc) Algorithm Withstatistical Aggregation In Sensor Networks, Lalitha Krishnasamy, Thangarajan Ramasamy, Rajesh Kumar Dhanaraj, Poongodi Chinnasamy
Turkish Journal of Electrical Engineering and Computer Sciences
Wireless sensor networks (WSN) comprise a large number of connected tiny or small sensor devices to sense physical phenomenon. In WSN, prolonging the network's lifetime is a biggest challenge due to absence of power harvesting facility and irreplaceable batteries of the sensor devices. Clustering is one of the widely accepted and standard technique to solve the energy issues faced in WSN. In addition to clustering, the shape of the deployment area also plays the major role especially for large scale sensor deployment. This paper proposes a radial shaped clustering (RSC) algorithm with angular inclination routing. The radial shaped deployed area …
Novel Fast Terminal Sliding Mode Controller With Current Constraint Forpermanent-Magnet Synchronous Motor, Yao Fang, Huifang Kong, Daoyuan Ding
Novel Fast Terminal Sliding Mode Controller With Current Constraint Forpermanent-Magnet Synchronous Motor, Yao Fang, Huifang Kong, Daoyuan Ding
Turkish Journal of Electrical Engineering and Computer Sciences
Under the noncascade structure, the balance between q-axis current constraint and dynamic performance in permanent-magnet synchronous motor system has become a critical problem. On the one hand, large transient current is required to provide high torque to achieve fast dynamic performance. On the other hand, current constraint becomes a state constraint problem, instead of governing q-axis reference current in the cascade structure directly. Aiming at this issue, a novel fast terminal sliding mode control (FTSMC)-based controller with current constraint is developed in this paper. The novelty of this scheme is related to the proposed penalty function based on interior point …
Distributed Denial Of Service Attack Detection In Cloud Computing Using Hybridextreme Learning Machine, Gopal Singh Kushwah, Virender Ranga
Distributed Denial Of Service Attack Detection In Cloud Computing Using Hybridextreme Learning Machine, Gopal Singh Kushwah, Virender Ranga
Turkish Journal of Electrical Engineering and Computer Sciences
One of the major security challenges in cloud computing is distributed denial of service (DDoS) attacks. In these attacks, multiple nodes are used to attack the cloud by sending huge traffic. This results in the unavailability of cloud services to legitimate users. In this research paper, a hybrid machine learning-based technique has been proposed to detect these attacks. The proposed technique is implemented by combining the extreme learning machine (ELM) model and the blackhole optimization algorithm. Various experiments have been performed with the help of four benchmark datasets namely, NSL KDD, ISCX IDS 2012, CICIDS2017, and CICDDoS2019, to evaluate the …
An Enhanced Bandwidth Disturbance Observer Based Control- S-Filter Approach, Mehmet Önder Efe, Coşku Kasnakoğlu
An Enhanced Bandwidth Disturbance Observer Based Control- S-Filter Approach, Mehmet Önder Efe, Coşku Kasnakoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
A continuous time enhanced bandwidth disturbance observer based control (DOBC) scheme is proposed in this paper. The classical Q -filter is implemented in feedback form and a signum function is inserted into the loop. The loop with this modification becomes capable of detecting small magnitude matched disturbances and we present an in depth discussion of the stability and performance issues comparatively. The proposed approach is called S-filter approach and the results outperform the classical approach under certain conditions. The contribution of the current paper is to advance the subject area to nonlinear filters for DOBC loops with guaranteed stability and …
Clustering Ensemble Selection Based On The Extended Jaccard Measure, Hajar Khalili, Mohsen Rabbani, Ebrahim Akbari
Clustering Ensemble Selection Based On The Extended Jaccard Measure, Hajar Khalili, Mohsen Rabbani, Ebrahim Akbari
Turkish Journal of Electrical Engineering and Computer Sciences
Clustering ensemble selection has shown high efficiency in the improvement of the quality of clustering solutions. This technique comprises two important metrics: diversity and quality. It has been empirically proved that ensembles of higher effectiveness can be achieved through taking into consideration the diversity and quality simultaneously. However, the relationships between these two metrics in base clusterings have remained uncertain. This paper suggests a new hierarchical selection algorithm using a diversity/quality measure based on the Jaccard similarity measure. In the proposed algorithm, the selection of the subsets of the clustering partitions is done based on their diversity measures. The proposed …
Radar-Based Microwave Breast Cancer Detection System With A High-Performanceultrawide Band Antipodal Vivaldi Antenna, Hüseyi̇n Özmen, Muhammed Bahaddi̇n Kurt
Radar-Based Microwave Breast Cancer Detection System With A High-Performanceultrawide Band Antipodal Vivaldi Antenna, Hüseyi̇n Özmen, Muhammed Bahaddi̇n Kurt
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, a novel ultrawide band (UWB) antipodal Vivaldi antenna with three pairs of slots was designed to be used as a sensor in microwave imaging systems for breast cancer detection. The proposed antenna operates in UWB frequency range of 3.05-12.2 GHz. FR4 was used as a dielectric material and as a substrate for forming the antenna that has a compact size of 36 mm x 36 mm x 1.6 mm. Frequency and time domain performance of the proposed antenna have been investigated and results show that it meets the requirements for UWB radar applications with linear phase response, …
Cover And Contents
Turkish Journal of Electrical Engineering and Computer Sciences
No abstract provided.
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, …
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 …
Deep Learning-Based Covid-19 Detection System Using Pulmonary Ct Scans, Rajit Nair, Adi Alhudhaif, Deepika Koundal, Rumi Iqbal Doewes, Preeti Sharma
Deep Learning-Based Covid-19 Detection System Using Pulmonary Ct Scans, Rajit Nair, Adi Alhudhaif, Deepika Koundal, Rumi Iqbal Doewes, Preeti Sharma
Turkish Journal of Electrical Engineering and Computer Sciences
One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 (COVID19). Many researchers have faced various types of challenges for finding the accurate model, which can automatically detect the COVID-19 using computed pulmonary tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed, which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based …
Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap
Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap
Turkish Journal of Electrical Engineering and Computer Sciences
As per 2016 updates by World Health Organization (WHO) on cancer disease, gliomas are categorized and further treated based on genomic mutations. The imaging modalities support a complimentary but immediate noninvasive diagnosis of cancer based on genetic mutations. Our aim is to train a deep convolutional neural network for isocitrate dehydrogenase (IDH) genotyping of glioma by auto-extracting the most discriminative features from magnetic resonance imaging (MRI) volumes. MR imaging data of total 217 patients were obtained from The Cancer Imaging Archives (TCIA) of high and low-grade gliomas. A 3-pathway convolutional neural network was trained for IDH classification. The multipath neural …
New Normal: Cooperative Paradigm For Covid-19 Timely Detection Andcontainment Using Internet Of Things And Deep Learning, Farooque Hassan Kumbhar, Ali Hassan Syed, Soo Young Shin
New Normal: Cooperative Paradigm For Covid-19 Timely Detection Andcontainment Using Internet Of Things And Deep Learning, Farooque Hassan Kumbhar, Ali Hassan Syed, Soo Young Shin
Turkish Journal of Electrical Engineering and Computer Sciences
The spread of the novel coronavirus (COVID-19) has caused trillions of dollars of damages to the governments and health authorities by affecting the global economies. It is essential to identify, track and trace COVID-19 spread at its earliest detection. Timely action can not only reduce further spread but also help in providing an efficient medical response. Existing schemes rely on volunteer participation, and/or mobile traceability, which leads to delays in containing the spread. There is a need for an autonomous, connected, and centralized paradigm that can identify, trace and inform connected personals. We propose a novel connected Internet of Things …
Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay
Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay
Turkish Journal of Electrical Engineering and Computer Sciences
The recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the …
An Adversarial Framework For Open-Set Human Action Recognition Usingskeleton Data, Özge Özti̇mur Karadağ
An Adversarial Framework For Open-Set Human Action Recognition Usingskeleton Data, Özge Özti̇mur Karadağ
Turkish Journal of Electrical Engineering and Computer Sciences
Human action recognition is a fundamental problem which is applied in various domains, and it is widelystudied in the literature. Majority of the studies model action recognition as a closed-set problem. However, in real-life applications it usually arises as an open-set problem where a set of actions are not available during training butare introduced to the system during testing. In this study, we propose an open-set action recognition system, humanaction recognition and novel action detection system (HARNAD), which consists of two stages and uses only 3D skeletoninformation. In the first stage, HARNAD recognizes a given action and in the second …
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 …