Turkish J. Electr. Eng. Comput. Sci.最新文献_第9页

A Novel Subspace Decomposition with Rotational Invariance Technique to Estimate Low-Frequency Oscillatory Modes of the Power Grid 一种新的子空间分解旋转不变性技术估计电网低频振荡模态

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-01-01 DOI: 10.1155/2023/9482825

S. Samal, Rajendra Kumar Khadanga

引用次数: 0

Quadratic programming based partitioning for Block Cimmino with correct value representation 基于二次规划的Block cimino分区方法

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-01-01 DOI: 10.55730/1300-0632.4004

Zuhal Tas, F. S. Torun

{"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}

引用次数: 0

Reconstructing dynamic human shapes from sparse silhouettes via latent space optimization of Parametric shape models 利用参数化形状模型的潜在空间优化，从稀疏轮廓重构动态人体形状

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-01-01 DOI: 10.55730/1300-0632.3985

Kanika Singla, P. Nand

{"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}

引用次数: 1

Task offloading and resource allocation based on DL-GA in mobile edge computing 移动边缘计算中基于DL-GA的任务卸载与资源分配

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-01-01 DOI: 10.55730/1300-0632.3998

Hang Gu, Minjuan Zhang, Wenzao Li, Yuwen Pan

{"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}

引用次数: 0

Unbiased federated learning in energy harvesting error-prone channels 能量收集易出错通道中的无偏联邦学习

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-01-01 DOI: 10.55730/1300-0632.4005

Z. Çakir, Elif Tugçe Ceran Arslan

引用次数: 0

Boomerang Algorithm based on Swarm Optimization for Inverse Kinematics of 6 DOF Open Chain Manipulators 基于群优化的六自由度开链机械臂逆运动学回旋算法

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-01-01 DOI: 10.55730/1300-0632.3988

Okan Duymazlar, D. Engin

{"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}

引用次数: 0

A type-2 fuzzy rule-based model for diagnosis of COVID-19 基于2型模糊规则的新型冠状病毒诊断模型

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-01-01 DOI: 10.55730/1300-0632.3970

İhsan Şahin, E. Akdogan, Mehmet Emin Aktan

引用次数: 1

Teamwork optimization based DTC for enhanced performance of IM based electric vehicle 基于团队优化的DTC提高基于IM的电动汽车性能

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-01-01 DOI: 10.55730/1300-0632.3989

A. Sahoo, R. Jena

{"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}

引用次数: 1

LVQ Treatment for Zero-Shot Learning 零射击学习的LVQ处理

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-01-01 DOI: 10.2139/ssrn.4025907

Firat Ismailoglu

{"title":"LVQ Treatment for Zero-Shot Learning","authors":"Firat Ismailoglu","doi":"10.2139/ssrn.4025907","DOIUrl":"https://doi.org/10.2139/ssrn.4025907","url":null,"abstract":": In image classification, there are no labeled training instances for some classes, which are therefore called unseen classes or test classes. To classify these classes, zero-shot learning (ZSL) was developed, which typically attempts to learn a mapping from the (visual) feature space to the semantic space in which the classes are represented by a list of semantically meaningful attributes. However, the fact that this mapping is learned without using instances of the test classes affects the performance of ZSL, which is known as the domain shift problem. In this study, we propose to apply the learning vector quantization (LVQ) algorithm in the semantic space once the mapping is determined. First and foremost, this allows us to refine the prototypes of the test classes with respect to the learned mapping, which reduces the effects of the domain shift problem. Secondly, the LVQ algorithm increases the margin of the 1-NN classifier used in ZSL, resulting in better classification. Moreover, for this work, we consider a range of LVQ algorithms, from initial to advanced variants, and applied them to a number of state-of-the-art ZSL methods, then obtained their LVQ extensions. The experiments based on five ZSL benchmark datasets showed that the LVQ-empowered extensions of the ZSL methods are superior to their original counterparts in almost all settings.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"13 1","pages":"216-237"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80954173","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}

引用次数: 0

Variational autoencoder-based anomaly detection in time series data for inventory record inaccuracy 基于变分自编码器的库存记录不准确时间序列异常检测

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-01-01 DOI: 10.55730/1300-0632.3977

Hali̇l Arğun, S. Alptekin

{"title":"Variational autoencoder-based anomaly detection in time series data for inventory record inaccuracy","authors":"Hali̇l Arğun, S. Alptekin","doi":"10.55730/1300-0632.3977","DOIUrl":"https://doi.org/10.55730/1300-0632.3977","url":null,"abstract":": Retail companies monitor inventory stock levels regularly and manage them based on forecasted sales to sustain their market position. Inventory accuracy, defined as the difference between the warehouse stock records and the actual inventory, is critical for preventing stockouts and shortages. The root causes of inventory inaccuracy are the employee or customer theft, product damage or spoilage, and wrong shipments. In this paper, we aim at detecting inaccurate stocks of one of Turkey’s largest supermarket chain using the variational autoencoder (VAE), which is an unsupervised learning method. Based on the findings, we showed that VAE is able to model the underlying probability distribution of data, regenerate the pattern from time series data, and detect anomalies. Hence, it reduces time and effort to manually label the inaccuracy in data. Since the distribution of inventory data depends on selected product/product categories, we had to use a parametric approach to handle potential differences. For individual products, we built univariate time series, whereas for product categories we built multivariate time series. The experimental results show that the proposed approaches can detect anomalies both in the low and high inventory quantities.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"114 1","pages":"163-179"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79343121","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}

引用次数: 0