Computers & Electrical Engineering最新文献_第6页

Enhanced one stage interleaved PFC Cuk configuration-based EV charger with reduced components 增强的一级交错PFC Cuk配置基于电动汽车充电器与减少组件

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-12 DOI: 10.1016/j.compeleceng.2025.110497

Narthana Sivaperumal , Gnanavadivel Jothimani , Albert Alexander Stonier , Geno Peter , Vijayakumar Arun , Vivekananda Ganji

{"title":"Enhanced one stage interleaved PFC Cuk configuration-based EV charger with reduced components","authors":"Narthana Sivaperumal , Gnanavadivel Jothimani , Albert Alexander Stonier , Geno Peter , Vijayakumar Arun , Vivekananda Ganji","doi":"10.1016/j.compeleceng.2025.110497","DOIUrl":"10.1016/j.compeleceng.2025.110497","url":null,"abstract":"<div><div>Drastic demand for the development of low cost and reliable battery chargers plays a key role for the zero emission vehicles due to increasing scarcity in fossil fuels. This work covers the evolution of single stage modified interleaved Cuk converter (MILCC) based EV charger incorporating fewer number of components. The presented configuration is composed of two parallel cells of Cuk converter with positive output polarity to charge the light electric vehicles (LEVs) at optimum power level. By interleaving the cells, the efficient and reliable charging of the battery is nurtured with reduced current stress and minimal input-output ripples improving the power quality (PQ) of the line current. This topology is devised to function as an intrinsic power factor corrector (PFC) by implementing the discontinuous conduction mode (DCM) of operation. The operative principle, modelling and control strategy of the proposed LEV charger is analyzed and harmonic spectrum of the line current is found to be as low as 2.7% that satisfies the recommended limits. The simulation and experimental findings were conducted during both steady state and transient conditions followed by the theoretical analysis.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110497"},"PeriodicalIF":4.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of variable strategy adaptive differential evolution with preselection mechanism to fault reconstruction of multi-load independent distribution system 带预选机制的变策略自适应差分进化在多负荷独立配电系统故障重构中的应用

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-12 DOI: 10.1016/j.compeleceng.2025.110472

Tianhao Gong, Dazhi Wang, Tianyi Li, Yupeng Zhang

{"title":"Application of variable strategy adaptive differential evolution with preselection mechanism to fault reconstruction of multi-load independent distribution system","authors":"Tianhao Gong, Dazhi Wang, Tianyi Li, Yupeng Zhang","doi":"10.1016/j.compeleceng.2025.110472","DOIUrl":"10.1016/j.compeleceng.2025.110472","url":null,"abstract":"<div><div>As power system capacity and network size increase, fault reconstruction techniques for multi-load independent distribution systems assume a pivotal role in ensuring the stable operation of distribution networks. The paper proposes a novel fault reconstruction method, addressing the challenges posed by the inherent characteristics of multi-load independent distribution system and the limitations of existing algorithms. The proposed approach involves three key components. First, an enhanced coding method is introduced to ensure that the branch correlation matrix of a multi-load independent distribution system effectively captures the network structure characteristics of the system. Then, a pre-selection mechanism is incorporated into the algorithm to ensure that the algorithm obtains a more optimal initial population during the initialization phase, thus optimizing the convergence speed of the algorithm. Finally, a variable strategy adaptive differential evolutionary algorithm is designed to improve the accuracy of the fault reconstruction scheme. The algorithm has been demonstrated to achieve a 24.1 % increase in combined optimization capability and a 28.34 % increase in optimization speed. The efficacy of the proposed method is demonstrated by its ability to identify the optimal fault reconfiguration scheme for multi-load independent distribution system, achieving a higher level of accuracy and efficiency.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110472"},"PeriodicalIF":4.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive review of power quality disturbance detection and classification techniques 电能质量干扰检测与分类技术综述

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-11 DOI: 10.1016/j.compeleceng.2025.110512

Ahsan Ali Memon , Mohsin Ali Koondhar , Saad F. Al-Gahtani , Z.M.S. Elbarbary , Zuhair Muhammed Alaas

{"title":"Comprehensive review of power quality disturbance detection and classification techniques","authors":"Ahsan Ali Memon , Mohsin Ali Koondhar , Saad F. Al-Gahtani , Z.M.S. Elbarbary , Zuhair Muhammed Alaas","doi":"10.1016/j.compeleceng.2025.110512","DOIUrl":"10.1016/j.compeleceng.2025.110512","url":null,"abstract":"<div><div>In recent decades, Power Quality Disturbances (PQD) analysis has gained significant attention due to the excessive use of non-linear power electronics. This review paper provides a comprehensive analysis of PQD detection and classification using signal processing methods for feature extraction. Methods such as Artificial Intelligence (AI), Artificial Neural Networks (ANN), Neuro-Fuzzy (NF), Genetic Algorithm (GA), and Deep Learning methods (DL), among others. Additionally, Discrete Wavelet Transform (DWT), S-Transform (ST), Multi-Resolution Analysis (MRA), and Wavelet Transform (WT) techniques are discussed. Herein, various feature extraction techniques and their combinations with intelligent methods were also evaluated for classifying PQDs. While various AI and feature extraction techniques have been examined for PQD classification, they often suffer from limitations such as high computational complexity and constraints in real-time conditions. However, experiments on datasets demonstrate an improvement in detection accuracy compared to state-of-the-art methods. A novel hybrid framework combining DL and GA methods, such as CNN with optimized DWT and MRA, aims to improve classification accuracy while maintaining computational efficiency. This framework demonstrates the potential of traditional techniques as reliable and effective classifiers compared to other algorithms.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110512"},"PeriodicalIF":4.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A robust multi-scale feature fusion model for low-quality multi-source data in underwater environments 水下低质量多源数据鲁棒多尺度特征融合模型

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-11 DOI: 10.1016/j.compeleceng.2025.110469

Yixiang Luo , Ning Li , Yuting Zhang , Mengyun Liu , Yun Peng , Yuyan Luo , Xiaoying Wang

{"title":"A robust multi-scale feature fusion model for low-quality multi-source data in underwater environments","authors":"Yixiang Luo , Ning Li , Yuting Zhang , Mengyun Liu , Yun Peng , Yuyan Luo , Xiaoying Wang","doi":"10.1016/j.compeleceng.2025.110469","DOIUrl":"10.1016/j.compeleceng.2025.110469","url":null,"abstract":"<div><div>Efficient and accurate perception of complex underwater scenes is crucial for ensuring the success of subsequent tasks. Multi-source image fusion techniques offer an effective solution, however, the presence of complex factors such as feature distortion, imaging blur, and lighting variations in low-quality multi-source (sonar-optical) underwater images leads to significant degradation in fusion performance. To address this issue, we propose a novel underwater multi-source data fusion model, incorporating multi-scale features detection and fusion. First, we extract shallow and deep features from multi-source data to detect rich local texture features and global structural features. Then, the detailed features and semantic information in the fusion process were enhanced through the designed multi-scale feature fusion module, and the problems such as low saturation and partial feature loss in the fusion image reconstruction were alleviated. This provides accurate multi-source fusion capabilities for downstream tasks. Extensive experiments on the public dataset demonstrate that our fusion method significantly improves the performance of tasks by 0.74% and 3.34%, surpassing related state-of-the-art methods.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110469"},"PeriodicalIF":4.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual representation propagation comparative learning for recommendations 推荐的双表示传播比较学习

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-10 DOI: 10.1016/j.compeleceng.2025.110474

Huohui Huang , Xin Fan , Shengwei Tian , Long Yu

{"title":"Dual representation propagation comparative learning for recommendations","authors":"Huohui Huang , Xin Fan , Shengwei Tian , Long Yu","doi":"10.1016/j.compeleceng.2025.110474","DOIUrl":"10.1016/j.compeleceng.2025.110474","url":null,"abstract":"<div><div>Graph neural networks combined with comparative learning have become a very popular paradigm in recommender systems. However, most methods still suffer from data sparsity, noise and difficulty in extracting multi-granularity information. To address these limitations, we propose a <strong>D</strong>ual <strong>R</strong>epresentation <strong>P</strong>ropagation <strong>C</strong>omparative <strong>L</strong>earning(DRPCL) method, which uses propagation representations based on two rules to extract multi-granularity signals, including graph convolutional propagation pathway and message node propagation pathway. Where the message node propagation pathway generates message nodes containing local information, and the message nodes are used as the hub to extract global signals, so as to fuse local and global signals. And the dual-pathway node representations generate two comparative views for misaligned comparative learning to alleviate the problem of sparse data. At the same time, denoising auxiliary supervision signals are generated to affect the propagation of node representations to reduce the negative effects of noise. Experimental results show that our DRPCL is able to demonstrate performance superiority over other bases on different datasets. Some in-depth experimental analysis demonstrates the robustness of DRPCL against data sparsity and noise.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110474"},"PeriodicalIF":4.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modality emotion semantic correlation analysis for multimodal emotion recognition 面向多模态情感识别的情态情感语义关联分析

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-10 DOI: 10.1016/j.compeleceng.2025.110467

Yuqing Zhang , Dongliang Xie , Dawei Luo , Baosheng Sun

{"title":"Modality emotion semantic correlation analysis for multimodal emotion recognition","authors":"Yuqing Zhang , Dongliang Xie , Dawei Luo , Baosheng Sun","doi":"10.1016/j.compeleceng.2025.110467","DOIUrl":"10.1016/j.compeleceng.2025.110467","url":null,"abstract":"<div><div>Affective computing serves as the fundamental technology and a crucial prerequisite for attaining naturalized and anthropomorphic human–computer interaction. Nevertheless, the expression of emotion is complex and multi-dimensional, posing significant challenges for multimodal emotion recognition due to the heterogeneity gap among distinct modalities. To tackle this issue, we propose a novel approach named modality emotion semantic correlation analysis (MESCA), which enhances multimodal affective semantic consistency by leveraging modality correlation learning to achieve multimodal information complementation. Specifically, we first design a modal-pair correlation module that calculates emotion semantic consistency across text, audio and video information. This module contributes to a comprehensive understanding of the emotional state by fusing complementary semantic information and assists in mitigating redundancy in pairwise interaction methods. Next, we introduce structural re-parameterization technology that transforms the multi-branch training structure into a single-branch inference structure to solve the problem of excessive computational expense, thereby facilitating a more efficient and effective recognition process. Additionally, the proposed model is verified on two public datasets, IEMOCAP and CMU-MOSEI. Compared to baseline methods, MESCA significantly enhances efficiency while maintaining prediction accuracy on IEMOCAP, and outperforms on both efficiency and accuracy on CMU-MOSEI.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110467"},"PeriodicalIF":4.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A 0.45-V supply, 22.77-nW resistor-less switched-capacitor bandgap voltage reference 0.45 v电源，22.77 nw无电阻开关电容带隙参考电压

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-09 DOI: 10.1016/j.compeleceng.2025.110496

Hamidreza Rashidian

引用次数: 0

Novel ubiquitous configuration algorithm for power enhancement of PV array operating under partial shading conditions 部分遮阳条件下光伏阵列功率增强的泛在配置算法

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-07 DOI: 10.1016/j.compeleceng.2025.110435

Rahul Anand , Bhavnesh Kumar , Swaroop D , Arjun Tyagi

引用次数: 0

Development of intelligent monitoring system for soil erosion in pipeline engineering based on deep learning 基于深度学习的管道工程水土流失智能监测系统的开发

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-07 DOI: 10.1016/j.compeleceng.2025.110432

Xiying Cheng, Yi Han, Wei Zhang

{"title":"Development of intelligent monitoring system for soil erosion in pipeline engineering based on deep learning","authors":"Xiying Cheng, Yi Han, Wei Zhang","doi":"10.1016/j.compeleceng.2025.110432","DOIUrl":"10.1016/j.compeleceng.2025.110432","url":null,"abstract":"<div><div>The urbanization process has promoted the construction of pipeline projects, but it has also caused soil erosion problems, posing a threat to the ecological environment and engineering safety. Traditional monitoring methods have the problems of low efficiency, poor accuracy and difficulty in real-time monitoring, so the development of intelligent systems based on deep learning is imminent. The intelligent monitoring system for soil erosion in pipeline projects based on deep learning constructed in this paper has achieved remarkable results. After various tests and verifications, the accuracy of the system reached 94 %, the recall rate was 93 %, the AUC-ROC value was as high as 0.97, and the F1 score was 93.5 %, which far exceeded the traditional monitoring methods. In terms of real-time performance, the data collection cycle is only 3 min, the data processing time is 25 s, the warning response time is 1 min and 30 s, and the total process time is 7 min, which can quickly respond to soil erosion risks. The system is very innovative. With the powerful pattern recognition and data analysis capabilities of deep learning, it breaks through the limitations of traditional methods that rely on manual methods and realizes the intelligence of the entire process from data collection to warning decision-making. Specifically, the study used deep learning technology to collect and integrate multi-source data (including remote sensing images, meteorological data, field sensor data, etc.), and used the CNN-LSTM hybrid model for feature extraction and trend prediction to build a complete intelligent monitoring system. In practical applications, the system can be widely used in various pipeline projects, greatly improving the efficiency and accuracy of soil erosion monitoring, providing key support for ensuring engineering construction safety and maintaining ecological balance, and effectively promoting the process of sustainable development.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110432"},"PeriodicalIF":4.0,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine-learning-based strategy for online prediction of hotspot temperature in dry-type three-phase transformers 基于机器学习的干式三相变压器热点温度在线预测策略

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-06 DOI: 10.1016/j.compeleceng.2025.110490

Ali Behniafar, Mohammad Farshad

{"title":"A machine-learning-based strategy for online prediction of hotspot temperature in dry-type three-phase transformers","authors":"Ali Behniafar, Mohammad Farshad","doi":"10.1016/j.compeleceng.2025.110490","DOIUrl":"10.1016/j.compeleceng.2025.110490","url":null,"abstract":"<div><div>Finite element analysis is complex, time-consuming, and unsuitable for online implementation when all the details are considered in the electromagnetic and thermal models. This paper proposes an approach combining finite element analysis with a neural network, which can predict the steady-state hotspot temperature of dry-type three-phase transformers with desired accuracy in various operating conditions only based on the simply measurable ambient and electrical quantities. In the proposed approach, the losses of the transformer’s windings and core are calculated through a detailed electromagnetic analysis and used as input heat sources to perform a precise thermal analysis. A dataset is generated by repeating this procedure for various ambient and operating conditions. Then, a feed-forward neural network is trained based on this dataset, ready to predict the steady-state hotspot temperature only using the real-time measurements of current, voltage, and ambient temperature. In this study, the transformer’s electromagnetic-thermal behavior is simulated in COMSOL Multiphysics, and the temperature prediction algorithm is also implemented in MATLAB. Experimental tests on a prototype transformer confirm the validity of the implemented electromagnetic and thermal models. The numerical evaluations on this prototype and a real-scale transformer also show that the average absolute error of the hotspot temperature predictor does not exceed 1 °C in various ambient, loading, and harmonic distortion conditions, even in cases not seen in the training stage.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110490"},"PeriodicalIF":4.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0