Computers & Electrical Engineering最新文献

{"title":"Secure and optimized drone swarm operations with decentralized Adaptive Differential Evolution","authors":"Usama Arshad ,&nbsp;Zahid Halim","doi":"10.1016/j.compeleceng.2025.110487","DOIUrl":"10.1016/j.compeleceng.2025.110487","url":null,"abstract":"<div><div>Efficient drone swarm management requires real-time adaptive optimization and secure decentralized communication to ensure robust performance in dynamic environments. Traditional optimization methods such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) suffer from premature convergence and lack the adaptability required for large-scale swarm coordination. Similarly, centralized communication frameworks introduce security vulnerabilities, including single points of failure and susceptibility to cyberattacks. This study presents a novel integration of Adaptive Differential Evolution (ADE) and blockchain technology, leveraging ADE’s dynamic parameter tuning to improve swarm intelligence while utilizing blockchain’s decentralized ledger to secure inter-drone communication. The proposed framework was evaluated through extensive simulations on drone swarms ranging from 20 to 200 drones, demonstrating a 27% improvement in convergence speed and a 35% increase in task efficiency compared to PSO-based methods. Blockchain integration ensured 99.3% data integrity, preventing unauthorized modifications and cyber threats such as man-in-the-middle attacks and data corruption attempts. Energy consumption analysis indicated that ADE reduced power usage by 18% compared to traditional heuristic approaches. Additionally, adversarial testing revealed that denial-of-service (DoS) resilience improved by 42% due to the blockchain’s consensus validation mechanisms. These results highlight the feasibility of secure and adaptive drone swarm management, making it suitable for real-world applications in disaster response, autonomous surveillance, and smart logistics.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110487"},"PeriodicalIF":4.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279909","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}

{"title":"Robust electric load forecasting through ensemble learning: A stacking approach with empirical mode decomposition and transfer learning","authors":"Mohit Choubey,&nbsp;Rahul Kumar Chaurasiya,&nbsp;J.S. Yadav","doi":"10.1016/j.compeleceng.2025.110511","DOIUrl":"10.1016/j.compeleceng.2025.110511","url":null,"abstract":"<div><div>Recent advancements in artificial intelligence (AI) have significantly influenced various disciplines, including electricity demand forecasting within power systems. This study introduces a methodology that emphasizes on predicting total energy consumption rather than limiting the scope to specific sectors. By integrating Empirical Mode Decomposition (EMD) with Transfer Learning (TL), the proposed model enhances the accuracy and generalization capability of ensemble models. The methodology achieves this by decomposing input data features into linear and nonlinear components, that optimized the resource allocation, encourages to use simpler models, and mitigating overfitting risks. TL further strengthens the model's adaptability, allowing it to accommodate diverse load patterns from multiple sectors. This adaptability facilitates the integration of sector-specific load into a comprehensive framework, leading to more accurate predictions of net load demand for power station operations. Experimental evaluations validated the model’s superior performance, achieving a Mean Absolute Error (MAE) of 82.58, a Root Mean Square Error (RMSE) of 95.375, and a Mean Absolute Percentage Error (MAPE) of 1.06 %, this contributes 2.85 % improvement over conventional methods. The proposed model further validated on the widely utilized New South Wales (NSW) dataset revealed an MAE of 96.23, an RMSE of 102.16, and a MAPE of 1.02 %. A predictive accuracy of 98.98 % was achieved using the proposed model, which outperforms state-of-the-art models like N-BEATS and DLinear and other advanced ensemble techniques. Statistical tests, such as the Friedman test and Nemenyi post-hoc analysis, confirm the strength of the proposed model, regularly placing it as the top performer among the other methods. This enables the proposed model to be applicable in the real world by predicting the energy consumption at a broader level rather than at a sector level. Moreover, the proposed model’s outcomes illustrate, that the framework is reliable and generalizable in nature which leads to better resource optimization and promotion of energy efficiency practices in load forecasting can be achieved.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110511"},"PeriodicalIF":4.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279923","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}

{"title":"Smart residential electric vehicle charging and discharging scheduling via multi-agent asynchronous-updating deep reinforcement learning","authors":"Qiang Zhao ,&nbsp;Chengwei Xu ,&nbsp;Chuan Sun ,&nbsp;Yinghua Han","doi":"10.1016/j.compeleceng.2025.110473","DOIUrl":"10.1016/j.compeleceng.2025.110473","url":null,"abstract":"<div><div>Despite the increasing penetration of electric vehicle (EV), significant efforts are still required to transition towards a low-carbon future while balancing economy and power system stability. Coordinating EV charging and discharging scheduling in residential areas faces challenges due to uncertainties in EV owners’ commuting behaviors, complex energy demands, and unpredictable power information. This paper formulates the <strong>R</strong>esidential <strong>EV C</strong>harging and <strong>D</strong>ischarging <strong>S</strong>cheduling (REV-CDS) as a Markov Decision Process with an unknown transition function. <strong>M</strong>ulti-<strong>A</strong>gent-<strong>A</strong>synchronous-<strong>S</strong>oft-<strong>A</strong>ctor-<strong>C</strong>ritic (MAASAC) algorithm is proposed to solve the Markov Decision Process. The proposed method employs the asynchronous updating process instead of synchronous updating, which allows agents to maintain consistent policy update direction, enabling better learning of a charging and discharging strategy to improve coordination among EVs and highly align with the overall scheduling goals in the REV-CDS environment. Finally, several numerical studies were conducted to compare the proposed approach with classical multi-agent reinforcement learning methods. The studies demonstrate the effectiveness of improvement in minimizing charging costs, reducing carbon emissions, alleviating charging anxiety, and preventing transformer overload.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110473"},"PeriodicalIF":4.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271026","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}

{"title":"A decision framework for privacy-preserving synthetic data generation","authors":"Pablo Sanchez-Serrano,&nbsp;Ruben Rios,&nbsp;Isaac Agudo","doi":"10.1016/j.compeleceng.2025.110468","DOIUrl":"10.1016/j.compeleceng.2025.110468","url":null,"abstract":"<div><div>Access to realistic data is essential for various purposes, including training machine learning models, conducting simulations, and supporting data-driven decision making across diverse domains. However, the use of real data often raises significant privacy concerns, as it may contain sensitive or personal information. Generative models have emerged as a promising solution to this problem by generating synthetic datasets that closely resemble real data. Nevertheless, these models are typically trained on original datasets, which carries the risk of leaking sensitive information. To mitigate this issue, privacy-preserving generative models have been developed to balance data utility and privacy guarantees. This paper examines existing generative models for synthetic tabular data generation, proposing a taxonomy of solutions based on the privacy guarantees they provide. Additionally, we present a decision framework to aid in selecting the most suitable privacy-preserving generative model for specific scenarios, using privacy and utility metrics as key selection criteria.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110468"},"PeriodicalIF":4.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271028","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}

{"title":"Fault diagnosis of multimodal feature fusion convolutional neural network based on differential evolution optimization","authors":"Min Ji ,&nbsp;Shaofeng Zhang ,&nbsp;Jinghui Yang","doi":"10.1016/j.compeleceng.2025.110518","DOIUrl":"10.1016/j.compeleceng.2025.110518","url":null,"abstract":"<div><div>To overcome the performance degradation caused by data scarcity in bearing fault diagnosis -where acquiring sufficient fault samples proves particularly challenging, an effective fault detection approach has been developed. To utilize fault feature information more effectively, we have presented the vibration signal of bearings in a multimodal manner. Two forms of data, time series data in one dimension and gray images in two dimensions, are used as sample data. According to the characteristics of these two kinds of data, two modules are used for targeted feature extraction, and obtain the local frequency, vibration mode characteristics and global dependence of the original vibration signal. A multimodal feature fusion convolutional neural network (AMFCNN) is constructed. Simultaneously, the differential evolution algorithm undergoes optimization to ascertain the optimal combination of key hyper-parameters for the model. Three datasets are used for comparative and variable operating condition experiments to validate this method. The experimental results show that AMFCNN achieves an average accuracy of 91.56 % with only 21.43 % of the data used, which is &gt;13 % improvement over the unimodal approach. AMFCNN is constructed from the two starting points of optimizing the vibration signal data and improving the feature extraction ability of the model, effectively avoiding the problems of over fitting and insufficient feature extraction ability of most fault diagnosis models.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110518"},"PeriodicalIF":4.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271029","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}

{"title":"ExVQA: a novel stacked attention networks with extended long short-term memory model for visual question answering","authors":"Bui Thanh Hung,&nbsp;Ho Vo Hoang Duy","doi":"10.1016/j.compeleceng.2025.110439","DOIUrl":"10.1016/j.compeleceng.2025.110439","url":null,"abstract":"<div><div>Visual Question Answering (VQA) has garnered significant attention in recent years due to its potential for broad applications across fields such as medicine, education, and entertainment. However, existing VQA methods still face several limitations, including challenges in handling abstract and complex questions, poor generalization, lack of explainability, and susceptibility to noise and bias. In this study, we propose a novel ExVQA model that leverages Stacked Attention Networks (SANs) and Extended Long Short-Term Memory (xLSTM) for Visual Question Answering. Image features are extracted using Sigmoid loss for Language-Image Pre-training (SigLIP), while question features are represented using the Autoregressive Transformer Decoder model (GPT-Neo) and Extended Long Short-Term Memory networks to facilitate the answer generation process. By utilizing the strengths of SANs and xLSTM, our approach aims to overcome the limitations of previous models and enhance the performance and reliability of VQA systems. Evaluation results on three datasets: PathVQA, VQA-Med 2019 and GQA show that our proposed ExVQA model achieves better performance than existing methods, demonstrating great application potential in the fields of medicine, education and entertainment.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110439"},"PeriodicalIF":4.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271025","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}

{"title":"Securing in-vehicle communications through post-quantum cryptography and network segmentation","authors":"Arcangelo Castiglione,&nbsp;Teresa Elia","doi":"10.1016/j.compeleceng.2025.110488","DOIUrl":"10.1016/j.compeleceng.2025.110488","url":null,"abstract":"<div><div>Modern vehicles rely on Electronic Control Units (ECUs) communicating via in-vehicle networks, where the Controller Area Network (CAN) protocol is the industry standard. Although CAN is efficient and robust, it lacks essential security features such as authentication, confidentiality, and integrity, leaving it vulnerable to cyberattacks. These vulnerabilities are amplified by the rise of quantum computing, which threatens traditional cryptographic methods and increases the need for more resilient security mechanisms for vehicles. This paper proposes a segmented-based CAN model that integrates Post-Quantum Cryptography. It uses the CRYSTALS-Kyber algorithm for secure session key sharing and lightweight symmetric encryption to protect CAN messages in real-time. Experimental evaluation in a simulated automotive environment shows that the model adds negligible latency and network load. The findings confirm that quantum-resistant security can be achieved without compromising the performance or reliability of existing CAN-based systems, offering a scalable and future-proof solution for automotive cybersecurity.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110488"},"PeriodicalIF":4.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279908","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}

{"title":"Enhanced one stage interleaved PFC Cuk configuration-based EV charger with reduced components","authors":"Narthana Sivaperumal ,&nbsp;Gnanavadivel Jothimani ,&nbsp;Albert Alexander Stonier ,&nbsp;Geno Peter ,&nbsp;Vijayakumar Arun ,&nbsp;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}

{"title":"Application of variable strategy adaptive differential evolution with preselection mechanism to fault reconstruction of multi-load independent distribution system","authors":"Tianhao Gong,&nbsp;Dazhi Wang,&nbsp;Tianyi Li,&nbsp;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}

{"title":"Comprehensive review of power quality disturbance detection and classification techniques","authors":"Ahsan Ali Memon ,&nbsp;Mohsin Ali Koondhar ,&nbsp;Saad F. Al-Gahtani ,&nbsp;Z.M.S. Elbarbary ,&nbsp;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}