Turkish J. Electr. Eng. Comput. Sci.最新文献

{"title":"A Novel Subspace Decomposition with Rotational Invariance Technique to Estimate Low-Frequency Oscillatory Modes of the Power Grid","authors":"S. Samal, Rajendra Kumar Khadanga","doi":"10.1155/2023/9482825","DOIUrl":"https://doi.org/10.1155/2023/9482825","url":null,"abstract":". Tis paper proposes modifed Karhunen–Loeve transform with total least square estimation of signal parameters using rotational in-variance technique (MKLT-TLS-ESPRIT) to approximate the low-frequency oscillatory modes. MKLTdecreases the impact of highly correlated additive colored Gaussian noise (ACGN) from the signal by diferentiating the correlation matrix w.r.t from the fnal time instance. A quantitative study of the suggested method with other estimation methods is used to evaluate the effectiveness of the proposed method. Monte Carlo simulations with 50,000 runs are conducted to test the robustness of the estimation scheme for MKLT-TLS-ESPRIT. Te evaluation of the efciency of the proposed method in real-time perspective, the two-area system, and New England sixty-eight bus test system has been considered. Te analysis shows that the suggested methodology correctly measures the interarea modes and lowers their mean and standard deviation to a minimum value.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"9 1","pages":"9482825:1-9482825:11"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82833057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quadratic programming based partitioning for Block Cimmino with correct value representation","authors":"Zuhal Tas, F. S. Torun","doi":"10.55730/1300-0632.4004","DOIUrl":"https://doi.org/10.55730/1300-0632.4004","url":null,"abstract":": The block Cimmino method is successfully used for the parallel solution of large linear systems of equations due to its amenability to parallel processing. Since the convergence rate of block Cimmino depends on the orthogonality between the row blocks, advanced partitioning methods are used for faster convergence. In this work, we propose a new partitioning method that is superior to the state-of-the-art partitioning method, GRIP, in several ways. Firstly, our proposed method exploits the Mongoose partitioning library which can outperform the state-of-the-art methods by combining the advantages of classical combinatoric methods and continuous quadratic programming formulations. Secondly, the proposed method works on the numerical values in a floating-point format directly without converting them to integer format as in GRIP. This brings an additional advantage of obtaining higher quality partitionings via better representation of numerical values. Furthermore, the preprocessing time is also improved since there is no overhead in converting numerical values to integer format. Finally, we extend the Mongoose library, which originally partitions graphs into only two parts, by using the recursive bisection paradigm to partition graphs into more than two parts. Extensive experiments conducted on both shared and distributed memory architectures demonstrate the effectiveness of the proposed method for solving different types of real-world problems.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"105 1","pages":"596-611"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79211802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstructing dynamic human shapes from sparse silhouettes via latent space optimization of Parametric shape models","authors":"Kanika Singla, P. Nand","doi":"10.55730/1300-0632.3985","DOIUrl":"https://doi.org/10.55730/1300-0632.3985","url":null,"abstract":": The problem of dynamic 3D reconstruction has gained popularity over the last few years with most approaches relying on data driven learning and optimization methods. However this is quite a challenging task because of the need for tracking different features in both space and time—that too of deformable objects—where such robust tracking may not always be possible. A common way to better ground the problem is by using some forms of regularizations primarily on the shape representations. Over the years, mesh-based linear blend skinning models have been the standard for fitting templates of humans to the observed time series data of human deformation. However, this approach suffers from optimization difficulties arising from maintaining a consistent mesh topology. In this paper, a novel algorithm for reconstructing dynamic human shapes has been proposed, which uses only sparse silhouette information. This is achieved by first creating shape models based on the signed distance neural fields which are subsequently optimized via volumetric differentiable rendering to best match the observed data. Several experiments have been carried out in this work to test the robustness of this method and the results show it to be quite robust, outperforming prior state of the art on dynamic human shape reconstruction by 45% .","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"66 3 1","pages":"295-311"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84250516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Task offloading and resource allocation based on DL-GA in mobile edge computing","authors":"Hang Gu, Minjuan Zhang, Wenzao Li, Yuwen Pan","doi":"10.55730/1300-0632.3998","DOIUrl":"https://doi.org/10.55730/1300-0632.3998","url":null,"abstract":": With the rapid development of 5G and the Internet of Things (IoT), the traditional cloud computing architecture struggle to support the booming computation-intensive and latency-sensitive applications. Mobile edge computing (MEC) has emerged as a solution which enables abundant IoT tasks to be offloaded to edge services. However, task offloading and resource allocation remain challenges in MEC framework. In this paper, we add the total number of offloaded tasks to the optimization objective and apply algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA) to maximize the value function, which is defined as a weighted sum of energy consumption, latency, and the number of offloaded tasks. First, we use GA to optimize the task offloading scheme and store the states and labels of scenario. Each state consists of five parameters: the IDs of all tasks generated in this scenario, the cost of each task, whether the task is offloaded, bandwidth occupied by offloaded task and remaining bandwidth of edge server. The labels are the tasks that are currently selected for offloading. Then, these states and labels will be used to train neural network. Finally, the trained neural network can quickly give optimization solutions. Simulation results show that DL-GA can execute 75 to 450 times faster than GA without losing much optimization power. At the same time, DL-GA has stronger optimization capability compared to Deep Q-Learning Network (DQN)","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"45 1","pages":"498-515"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84775149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unbiased federated learning in energy harvesting error-prone channels","authors":"Z. Çakir, Elif Tugçe Ceran Arslan","doi":"10.55730/1300-0632.4005","DOIUrl":"https://doi.org/10.55730/1300-0632.4005","url":null,"abstract":": Federated learning (FL) is a communication-efficient and privacy-preserving learning technique for collaborative training of machine learning models on vast amounts of data produced and stored locally on the distributed users. This paper investigates unbiased FL methods that achieve a similar convergence as state-of-the-art methods in scenarios with various constraints like an error-prone channel or intermittent energy availability. For this purpose, we propose FL algorithms that jointly design unbiased user scheduling and gradient weighting according to each user’s distinct energy and channel profile. In addition, we exploit a prevalent metric called the age of information (AoI), which quantifies the staleness of the gradient updates at the parameter server and adaptive momentum attenuation to increase the accuracy and accelerate the convergence for nonhomogeneous data distribution of participant users. The effect of AoI and momentum on fair FL with heterogeneous users on various datasets is studied, and the performance is demonstrated by experiments in several settings.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"56 1","pages":"612-625"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85240083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boomerang Algorithm based on Swarm Optimization for Inverse Kinematics of 6 DOF Open Chain Manipulators","authors":"Okan Duymazlar, D. Engin","doi":"10.55730/1300-0632.3988","DOIUrl":"https://doi.org/10.55730/1300-0632.3988","url":null,"abstract":": In this study, a feasible swarm intelligence algorithm is proposed that computes the inverse kinematics solution of 6 degree of freedom (DOF) industrial robot arms, which are frequently used in industrial and medical applications. The proposed algorithm is named as Boomerang algorithm due to its recursive structure. The proposed algorithm aims to reduce the computation time to feasible levels without increasing the position and orientation errors. In order to reduce the computational time in swarm optimization algorithms and increase feasibility, an alternative definition method was used instead of the DH method in defining the robot arm kinematic configuration. The effect of the proposed alternative definition method in reducing the computational time is presented through example inverse kinematic analysis. The proposed algorithm was compared with 3 different particle swarm optimization (PSO) variants that include orientation in the inverse kinematic solution of 6 DOF robot arms. Comparative simulation studies were carried out with 20 randomly selected position and orientation data from the workspaces of PUMA 560 and ABB IRB120 manipulators to measure performance of the algorithms. Using the error and computation time values obtained from the simulation results, the algorithms are compared using the Wilcoxon nonparametric statistical test. When the simulation results are analysed by considering the calculation time, positioning accuracy and solution finding rates, it is seen that the Boomerang algorithm is more feasible than the other PSO variants. Verification of the simulation results, and the physical applications were carried out with the ABB IRB120 6 DOF robot arm. Simulation studies and experimental studies showed that the proposed algorithm may be an efficient method for inverse kinematics of time-critical applications.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"10 1","pages":"342-359"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85292864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A type-2 fuzzy rule-based model for diagnosis of COVID-19","authors":"İhsan Şahin, E. Akdogan, Mehmet Emin Aktan","doi":"10.55730/1300-0632.3970","DOIUrl":"https://doi.org/10.55730/1300-0632.3970","url":null,"abstract":"In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods in the diagnosis of COVID-19 has been developed. The developed system consists of three fuzzy units. The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests. In the third fuzzy unit, the COVID-19 positivity risks according to the clinical examination and blood analysis results, which are the outputs of the first and second fuzzy units, are evaluated together and the result is obtained. As a result of the evaluation of the trials with 60 different scenarios by physicians, it has been revealed that the system can detect COVID-19 risk with 86.6% accuracy. [ FROM AUTHOR]","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"37 9 1","pages":"39-52"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82819572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Teamwork optimization based DTC for enhanced performance of IM based electric vehicle","authors":"A. Sahoo, R. Jena","doi":"10.55730/1300-0632.3989","DOIUrl":"https://doi.org/10.55730/1300-0632.3989","url":null,"abstract":": The tailpipe emissions caused by vehicles using internal combustion engines are a significant source of air pollution. To reduce the health hazards caused by air pollution, advanced countries are now adopting the use of electric vehicles (EVs). Due to the advancement of electric vehicles, research and development efforts are being made to improve the performance of EV motors. With a nominal reference stator flux, the classical induction motor drive generates significant flux, torque ripple, and current harmonics. In this work, a teamwork optimization algorithm (TOA)-based optimal stator flux strategy is suggested for torque ripple reduction applied in a classical direct torque-controlled induction motor drive. The suggested algorithm’s responsiveness is investigated under various steady-state and dynamic operating conditions. The proposed DTC-IM drive’s simulation results are compared to those of the classical and fuzzy DTC-IM drives. The proposed system has been evaluated and found to reduce torque ripple, flux ripple, current harmonics, and total energy consumption by the motor. Further, a comparative simulation study of the above methods at different standard drive cycles is presented. Experimental verification of the proposed algorithm using OPAL-RT is presented. The results represent the superiority of the proposed algorithm compared to the classical DTC and fuzzy DTC IM drives. The torque ripple reduction approach described in this study can also be applied to all induction motors, not only those for electric vehicles or hybrid electric vehicles (HEVs).","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"1 1","pages":"360-380"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90982410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An effective Hilbert-Huang transform-based approach for dynamic eccentricity fault diagnosis in double-rotor double-sided stator structure axial flux permanent magnet generator under various load and speed conditions","authors":"Makan Torabi, Y. Alinejad‐Beromi","doi":"10.55730/1300-0632.3971","DOIUrl":"https://doi.org/10.55730/1300-0632.3971","url":null,"abstract":": Eccentricity fault in double-sided axial flux permanent magnet generator is very difficult to be detected as the fault generated variations in terminal electrical parameters are very weak and chaotic, especially at the initial stages of the fault occurrence. In addition, one of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned difficulty and problems, this paper adopts a novelty detection algorithm based on Hilbert–Huang transform (HHT) which is a time-frequency signal analysis approach based on empirical mode decomposition and the Hilbert transform. It is well suited for reliable fault diagnosis since it is unaffected by transient conditions which make the diagnosis process incur into false alarms. The HHT-based methods has been demonstrated in recent years for rotor and bearing faults detection of induction machine and also for stator faults identification in PM synchronous machines with radial flux structure. This study explores the possibility of applying the technique to the detection of dynamic eccentricity faults in double-rotor double-sided stator structure axial flux permanent magnet generator under variable load and speed conditions. This approach relies on two steps: estimating the intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD) and computing the instantaneous amplitude (IA) and instantaneous frequency (IF) of IMFs using the Hilbert transform. The more significant IMFs are determined by means of Hilbert spectrum, which is applied for accurate eccentricity fault diagnosis. The eccentricity severity can be evaluated based on the IMFs energy value. The theoretical basis of the proposed method is presented. The effectiveness of the proposed method is verified by a series of simulation and experimental tests under different conditions. The results show that the presented approach in this paper is robust against","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"40 1","pages":"53-76"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77487301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust model for spot virtual machine bidding in the cloud market using information gap decision theory (IGDT)","authors":"Mona Naghdehforoushha, M. Dehghan, Mohammad Hossein Rezvani, M. Sadeghi","doi":"10.55730/1300-0632.3979","DOIUrl":"https://doi.org/10.55730/1300-0632.3979","url":null,"abstract":": The spot market is one of the most common cloud markets where cloud providers, such as Amazon EC2, rent their surplus computing resources at lower prices in the form of spot virtual machines (SVMs). In this market, which is often managed through an auction mechanism, users seek optimal bidding strategies for renting SVMs to minimize cost and risk. Uncertainty in the price of SVMs and their low availability/reliability is a challenging issue to bid on the user side. In this paper, we present a robust model for minimizing the cost of executing tasks by considering the uncertainty of the price of SVMs based on the Information Gap Decision Theory (IGDT). It evaluates the risk-aversion and the risk-seeker nature of the user’s bidding strategy and measures the cost of risk/immunity. The main advantage of this method is that it formulates user decisions without the need for any presumption about the price distribution of SVMs. With this decision-support system, users can rely on the predicted confidence intervals to make the optimal bid for future time slots according to the selected risk level. The results are compared with Monte Carlo simulations and a scenario-based approach to evaluate the effectiveness of the proposed IGDT-based model. Evaluation results based on historical Amazon EC2 prices confirm the efficiency of the proposed method to handle the uncertain nature of the price of SVMs in terms of significant criteria such as robustness cost, opportunity cost, uncertainty budget, and execution time.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"517 1","pages":"193-215"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80161430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}