Turkish J. Electr. Eng. Comput. Sci.最新文献_第6页

A Proposed Supercapacitor Integrated with an Active Power Filter for Improving the Performance of a Wind Generation System under Nonlinear Unbalanced Loading and Faults 一种集成有源电力滤波器的超级电容器用于改善非线性不平衡负载和故障下风力发电系统的性能

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-04-10 DOI: 10.1155/2023/2863528

Ibrahim Hamdan, Marwa M. M. Youssef, Omar Noureldeen

{"title":"A Proposed Supercapacitor Integrated with an Active Power Filter for Improving the Performance of a Wind Generation System under Nonlinear Unbalanced Loading and Faults","authors":"Ibrahim Hamdan, Marwa M. M. Youssef, Omar Noureldeen","doi":"10.1155/2023/2863528","DOIUrl":"https://doi.org/10.1155/2023/2863528","url":null,"abstract":"This study proposes the integration of a supercapacitor (SC) with the DC link of a three-phase four-wire active power filter (APF) by using an interfaced three-level bidirectional buck-boost converter controlled by the fuzzy control approach. APF is a flexible alternating current transmission system (FACTS) device that enhances power quality in the electrical network by reducing the load current harmonics and compensating the reactive power. Regulating the DC voltage of the APF’s DC link and absorbing fluctuations in compensated reactive power during disturbances are the major objectives of the integration of an SC circuit to APF. The studied model is a wind farm of Gabal El-Zeit with a total capacity of 580 MW, connected to unbalanced nonlinear loads. The Gabal El-Zeit wind farm is divided into three projects with a capacity of 240 MW, 220 MW, and 120 MW. The model is simulated by the MATLAB/SIMULINK program, and the effectiveness of the proposed methodology is proved by applying different types of faults such as single-line-to-ground, double-line-to-ground, and three-line-to-ground faults. In addition, an additional SC circuit with a two-level converter is connected to the generators coupled to the wind turbines to enhance the performance of the wind farm during disturbances. The results show that SC-integrated APF can reduce the harmonic distortion and compensate the reactive power for high or low inductive loads. Also, it can regulate the DC voltage and absorb the fluctuations in the reactive power during faults. Finally, the performance and the stability of the overall electric system are improved.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"119 1","pages":"2863528:1-2863528:17"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77475312","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: 2

A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule 多融合加权证据推理规则的集成学习新方法

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-03-29 DOI: 10.1155/2023/8987461

YiZhe Zhang, Yunyi Zhang, Guohui Zhou, W. Zhang, Kangle Li, Quanqi Mu, W. He, Kai Tang

{"title":"A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule","authors":"YiZhe Zhang, Yunyi Zhang, Guohui Zhou, W. Zhang, Kangle Li, Quanqi Mu, W. He, Kai Tang","doi":"10.1155/2023/8987461","DOIUrl":"https://doi.org/10.1155/2023/8987461","url":null,"abstract":"Ensemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. However, the present ensemble learning methods mostly adopt voting fusion in combining strategies. This strategy has difficulty mining effective information from the classifiers and cannot effectively reflect the relationship between different classifiers. Ensemble learning based on the evidential inference rule (ER rule) can effectively excavate the internal relationships among different classifiers and has a certain interpretability. However, the ER rule depends on the weight distribution of different combination strategies, and the setting of the evidence weight will affect the accuracy and stability of the model. Therefore, this paper proposes a new ensemble learning method based on multiple fusion weighted evidential reasoning rules and constructs an ensemble learning framework for data fusion and decision mapping. This framework takes the evidence weight, confidence, and feature data of each classifier as input and the integration results as output. The weight of evidence was determined by multiple fusion weights of the entropy weight method and order relation method. Finally, the integrated learning process is set up by the ER algorithm. The method proposed in this paper is verified by multiple datasets. Experimental results show that the surface construction model has good performance, and the defects of single weighting instability are greatly improved under the premise of improving the integration effect.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"136 1","pages":"8987461:1-8987461:15"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86431033","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

Identification Research of Trichagalma Glabrosa Insect Gall Pests Based on YOLOv5s 基于YOLOv5s的光斑赤霉病瘿虫鉴定研究

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-03-28 DOI: 10.1155/2023/4011188

Tianpeng Zhang, Wei Wang

{"title":"Identification Research of Trichagalma Glabrosa Insect Gall Pests Based on YOLOv5s","authors":"Tianpeng Zhang, Wei Wang","doi":"10.1155/2023/4011188","DOIUrl":"https://doi.org/10.1155/2023/4011188","url":null,"abstract":"In order to solve the problem of low image identification accuracy of Trichagalma glabrosa insect gall pests in a complex natural environment, an image identification method of Trichagalma glabrosa insect gall pests based on YOLOv5s was designed and introduced in this study. The original images were preprocessed with the grayscale maximum method and different gradients of noise, which reduced the color difference interference with complex backgrounds and improved the image identification rate. A total of 6090 images of insect gall pests under opposite light, back light, and complex backgrounds were constructed, which were divided into a training set and a test set with a ratio of 7 : 3. The results showed that the precision, recall, and mean average precision of YOLOv5s were 94.35%, 95.42%, and 95.8%, respectively. YOLOv5s, YOLOv4, and Faster-RCNN were compared and analyzed under the same test conditions. The identification accuracy of YOLOv5s was higher than that of YOLOv4 and Faster-RCNN, and its model size was only 13.8 MB. It was considered that the designed YOLOv5s method could help accurately and quickly identify Trichagalma glabrosa insect gall pests with high identification accuracy and a small model capacity, which was more conducive to the migration application of the model, and provide a new method for the rapid identification of Trichagalma glabrosa insect gall pests in a complex natural environment.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"180 1","pages":"4011188:1-4011188:7"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86227750","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

Statistical Analysis of Novel Ensemble Recursive Radial Basis Function Neural Network Performance on Global Solar Irradiance Forecasting 新型集合递归径向基函数神经网络全球太阳辐照度预报性能的统计分析

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-03-28 DOI: 10.1155/2023/2554355

M. Madhiarasan, M. Louzazni, Brahim Belmahdi

{"title":"Statistical Analysis of Novel Ensemble Recursive Radial Basis Function Neural Network Performance on Global Solar Irradiance Forecasting","authors":"M. Madhiarasan, M. Louzazni, Brahim Belmahdi","doi":"10.1155/2023/2554355","DOIUrl":"https://doi.org/10.1155/2023/2554355","url":null,"abstract":"Reliable operation of energy management systems, grid stability, and managing energy demand responses are becoming challenging because of the flickering nature of solar irradiance. Accurate forecasting of global solar irradiance, i.e., global horizontal irradiance (GHI), plays a significant role in energy policy-making and the energy market. This paper proposes a novel global solar irradiance forecasting model based on the ensemble recursive radial basis function neural networks (ERRBFNNs). The various atmospheric inputs based on the built ensemble recursive radial basis function neural networks make the network more stable and robust to climatic uncertainty. This paper statistically investigates the performance of novel feed-forward neural networks based on forecasting models with various hidden nodes for global solar irradiance forecasting applications. We validated the proposed ERRBFNN global solar irradiance forecasting model using real-time data sets. The simulation results confirm that the proposed ensemble recursive radial basis function neural network based on global solar irradiance forecasting improves the accuracy, generalization, and network stability. Furthermore, the proposed ERRBFNN lowers the forecasting error to the least compared to other state-of-the-art forecasting models.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"35 1","pages":"2554355:1-2554355:10"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86865089","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

Internet of Things Enabled Intelligent Automation for Smart Home with the Integration of PSO Algorithm and PID Controller 结合粒子群算法和PID控制器的智能家居物联网智能自动化

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-03-28 DOI: 10.1155/2023/9611321

Rajesh Singh, A. Gehlot, P. Kuchhal, S. Choudhury, S. Akram, Neeraj Priyadarshi, B. Khan

{"title":"Internet of Things Enabled Intelligent Automation for Smart Home with the Integration of PSO Algorithm and PID Controller","authors":"Rajesh Singh, A. Gehlot, P. Kuchhal, S. Choudhury, S. Akram, Neeraj Priyadarshi, B. Khan","doi":"10.1155/2023/9611321","DOIUrl":"https://doi.org/10.1155/2023/9611321","url":null,"abstract":"Currently, due to the widespread population growth, there is a widespread concern about an electricity shortage. As a result, smart devices have evolved and gained significant attention to reduce power consumption in home appliances due to electricity shortages. However, it lacks a universal remote control that can control home appliances based on environmental conditions. To overcome these challenges, this study proposed a hardware-based remote-control system that operates both in autonomous and semiautonomous modes to control home appliances based on environmental conditions. In the autonomous mode, the receiver section regulates the parameters under ambient conditions by varying the appliance’s applied voltage levels via a dimmer. The parameters in semiautonomous are monitored by the user via various levels of remote control. A 2.4 GHz RF modem is used to establish wireless personal network (WPAN) communication between the remote and the receiver. In addition, a Wi-Fi modem is built into the receiver to enable internet-based mobile applications to operate appliances. During the MATLAB analysis, a proportional integral derivative (PID) controller with a particle swarm optimization (PSO) method was found as a superior approach to control the home appliance with adequate environmental conditions. It is concluded from the MATLAB study that the PSO-PID controller delivered an energy saving of 14.88% for the heater, 36.9% for the exhaust fan, and 37.49% for the light bulb compared to the conventional appliances.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"55 1","pages":"9611321:1-9611321:25"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81421119","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

Personalized Recommendation Model Based on Improved GRU Network in Big Data Environment 大数据环境下基于改进GRU网络的个性化推荐模型

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-03-24 DOI: 10.1155/2023/3162220

Hui Guo, Zheng Guo, Zhihong Liu

{"title":"Personalized Recommendation Model Based on Improved GRU Network in Big Data Environment","authors":"Hui Guo, Zheng Guo, Zhihong Liu","doi":"10.1155/2023/3162220","DOIUrl":"https://doi.org/10.1155/2023/3162220","url":null,"abstract":"To address the diversity of user preferences and dynamic changes of interests in the personalized recommendation scenario, a personalized recommendation model based on the improved gated recurrent unit (GRU) network in a big data environment is proposed. First, in order to deal with outliers in sequence recommendation, context awareness sequence recommendation is introduced, and the dynamic changes of users’ interests are modeled by redefining the update gate and the reset gate of the GRU. Then, the duration information about how long users browse each item is processed and transformed to obtain the duration attention factor of each recommended item. And the duration attention factors and the item information are together used as the input of the proposed model for training and prediction. Finally, the auxiliary loss function is introduced to make up for the shortcomings of the traditional negative logarithmic likelihood function, and a super-parameter is applied to combine the auxiliary loss function with the negative logarithmic likelihood function so as to enhance the relationship between the interest representation and the accuracy of recommendation. Experiments show that the root mean square error (RMSE) of the proposed method in the Criteo dataset and MovieLens-1M dataset is 0.7257 and 0.7869, respectively, and the mean absolute error (MAE) is 0.5147 and 0.5893, respectively, which are better than those of the comparison methods. Therefore, the proposed method significantly outperforms the comparison methods in improving the accuracy of personalized recommendation in the system.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"27 1","pages":"3162220:1-3162220:9"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83847649","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

Intrusion Detection-Data Security Protection Scheme Based on Particle Swarm-BP Network Algorithm in Cloud Computing Environment 云计算环境下基于粒子群- bp网络算法的入侵检测-数据安全防护方案

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-03-24 DOI: 10.1155/2023/1128545

Zhun Wang, Xue Chen

{"title":"Intrusion Detection-Data Security Protection Scheme Based on Particle Swarm-BP Network Algorithm in Cloud Computing Environment","authors":"Zhun Wang, Xue Chen","doi":"10.1155/2023/1128545","DOIUrl":"https://doi.org/10.1155/2023/1128545","url":null,"abstract":"Aiming at the problems of low detection rate and high false detection rate of intrusion detection algorithms in the traditional cloud computing environment, an intrusion detection-data security protection scheme based on particle swarm-BP network algorithm in a cloud computing environment is proposed. First, based on the four modules of data collection, data preprocessing, feature selection, and intrusion detection, the overall framework of the intrusion detection model is constructed by designing corresponding functions. Then, by introducing the decision tree algorithm, the overfitting is reduced and the data processing speed of the model is improved, and on this basis, the feature selection is carried out through the “gain rate” optimization method, which reduces the redundant information of the feature vector. Finally, by introducing the Particle Swarm Optimization (PSO) algorithm into the optimization of the initial weights and thresholds of the BP neural network, the BP neural network is improved based on the momentum factor and adaptive learning rate, and the high detection rate and low false detection rate are realized. Through simulation experiments, the proposed intrusion detection method and the other three methods are compared and analyzed under the same conditions. The results show that the detection rate and false detection rate of the method proposed in this paper are the best under five different types of sample data, the highest detection rate reaches 95.72%, and the lowest false detection rate drops to 2.03%. The performance of the proposed algorithm is better than that of the other two comparison algorithms.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"101 1","pages":"1128545:1-1128545:10"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88969452","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

Smart Home System: A Comprehensive Review 智能家居系统:全面回顾

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-03-21 DOI: 10.1155/2023/7616683

Arindom Chakraborty, Monirul Islam, Fahim Shahriyar, S. Islam, Hasan U. Zaman, Mehedi Hasan

引用次数: 4

Gain of TlBr/BrCl Quantum Dot Semiconductor Optical Amplifier TlBr/BrCl量子点半导体光放大器的增益

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-03-17 DOI: 10.1155/2023/1941232

B. Al-Nashy, B. T. S. Al-Mosawi, M. Oleiwi, A. Al-khursan

引用次数: 1

EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET 基于特征技巧的VANET超图稳定聚类算法

Turkish J. Electr. Eng. Comput. Sci. Pub Date : 2023-03-15 DOI: 10.1155/2023/6327247

M. Jabbar, H. Trabelsi

{"title":"EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET","authors":"M. Jabbar, H. Trabelsi","doi":"10.1155/2023/6327247","DOIUrl":"https://doi.org/10.1155/2023/6327247","url":null,"abstract":"A smart city’s vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. To achieve high stability for the VANET, this paper presents a new efficient Eigen-trick-based hypergraph stable clustering algorithm (EtHgSC). This algorithm has a twofold scheme for stable CH selection. In the first part of the proposed scheme, the cluster generation is handled using an improved hypergraph-based spectral clustering algorithm using the Eigen-trick method. The “Eigen-trick” method is used to partition both vertices and hyperedges, which provides an approach for reducing the computational complexity of the clustering. The cluster head (CH) is chosen in the second part, taking into account the requirements for keeping a stable connection with most neighbors. In addition to relative speed, neighboring degree, and eccentricity that are used to select the CH, the vehicle time to leave metric is introduced to increase the CH stability. The grey relational analysis model is used to find each vehicle’s score, and the CH is selected based on the maximum vehicle’s score. The results show the supremacy of our proposed scheme in terms of CH lifetime, cluster member (CM) lifetime, and the change rate of CH. Also, the proposed scheme achieves a considerable reduction in terms of packet delay.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"16 1","pages":"6327247:1-6327247:15"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87981162","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