Computers & Electrical Engineering最新文献

{"title":"A swank raccoon yin yang pair optimization (RYi-YaP) model for solving economic load dispatch (ELD) and combined emission dispatch (CED) problems","authors":"Pitchala Vijaya Kumar,&nbsp;C. Shilaja","doi":"10.1016/j.compeleceng.2025.110149","DOIUrl":"10.1016/j.compeleceng.2025.110149","url":null,"abstract":"<div><div>Scheduling power generators to decrease costs and meet system restrictions is known as economic load dispatch, or ELD, in power systems. While earlier research works have demonstrated strategies to lower production costs and carbon dioxide emissions, an ideal distribution of costs and pollutants must be taken into account, resulting in Combined Emission Dispatch (CED). The main contribution of this study is the formulation of a new hybrid meta-heuristic model for solving the ELD and CED problem, called the Raccoon Yin Yang Pair Optimization (RYi-YaP) model. To minimize the fuel costs in order to maximize the financial advantages of power systems. Power balance and generator power limit are two restrictions that are focused on in this work in order to efficiently optimize the ELD process. In addition, a thorough analysis is conducted to investigate the performance of the proposed approach under various load conditions. The generators must meet the load requirement with the least amount of gearbox loss in order to guarantee safe operation. This study also considers some of the most popular and widely used optimization procedures for a comprehensive performance comparison and evaluation. The performance of the proposed approach is found to be quite satisfactory when compared to the previously reviewed approaches.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110149"},"PeriodicalIF":4.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455017","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 Industry 5.0: An explainable deep learning model for intrusion detection in cyber-physical systems","authors":"Himanshu Nandanwar,&nbsp;Rahul Katarya","doi":"10.1016/j.compeleceng.2025.110161","DOIUrl":"10.1016/j.compeleceng.2025.110161","url":null,"abstract":"<div><div>Cyber-physical systems (CPS) security is critical, particularly with the advent of Industry 5.0, which seeks to revolutionize industrial ecosystems through enhanced automation, connectivity, and human-machine collaboration. While this shift promises increased efficiency and productivity, it exposes systems to advanced cyber threats. This paper introduces Cyber-Sentinet, a Deep Learning-based Intrusion Detection System (IDS) designed explicitly for CPS in industrial IoT environments to address these challenges. Unlike traditional IDS models, Cyber-Sentinet integrates Shapley Additive Explanations (SHAP) to enhance the interpretability of its decision-making process, allowing security experts to understand better and trust the system's detections. Rigorous experimentation on the Edge-IIoT-2022 dataset, which covers various cyber-attacks (e.g., DDoS, SQL injection, MITM), validates Cyber-Sentinet effectiveness. The model achieves an accuracy of 97.46 %, precision of 97.7 %, and recall of 97.2 %, with a low loss of 0.182. These results demonstrate Cyber-Sentinet ability to offer high-performance intrusion detection and valuable insights into network security, making it a robust solution for protecting Industry 5.0 CPS against sophisticated cyber threats.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110161"},"PeriodicalIF":4.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454238","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 Genetic Algorithm based Three HyperParameter optimization of Deep Long Short Term Memory (GA3P-DLSTM) for Predicting Electric Vehicles energy consumption","authors":"Boutheina Jlifi ,&nbsp;Syrine Ferjani ,&nbsp;Claude Duvallet","doi":"10.1016/j.compeleceng.2025.110185","DOIUrl":"10.1016/j.compeleceng.2025.110185","url":null,"abstract":"<div><div>To overcome Climate Change, countries are turning to greener transportation systems. Therefore, the use of Electric Vehicles (EVs) is leveraging substantially since they present multiple advantages, like reducing hazardous emissions. Recently, the demand for EVs has increased, which means that more charging stations need to be available. By the year 2030, 15 million EVs will be accessible, and since the number of charging stations is limited, the charging needs should be defined for better management of the charging infrastructure. In this research, we aim to tackle this problem by efficiently predicting the energy consumption of EVs. We proposed a Genetic Algorithm (GA) based Three HyperParameter optimization of Deep Long Short Term Memory (GA3P-DLSTM), which is an optimized LSTM model that incorporates a GA for Hyperparameter Tuning. After experimenting our methodology and performing a comparative analysis with previous studies from the literature, the obtained results showed the efficiency of our novel model, with Mean Squared Error (MSE) equals to 0.000112 and a Determination Coefficient (R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>) equals to 0.96470. It outperformed other models of the literature for predicting energy use based on real-world data collected from the campus of Georgia Tech in Atlanta, USA.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110185"},"PeriodicalIF":4.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464336","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":"Unveiling energy usage patterns in industrial kitchens: From detection to clustering of appliance usage","authors":"Ricardo Martins ,&nbsp;Hugo Morais ,&nbsp;Lucas Pereira","doi":"10.1016/j.compeleceng.2025.110163","DOIUrl":"10.1016/j.compeleceng.2025.110163","url":null,"abstract":"<div><div>Industrial Kitchens (IKs) are characterized by high energy consumption, yet they remain largely overlooked in energy research. Understanding how electricity is used in IKs is crucial for identifying opportunities for energy optimization and improving sustainability in this sector. This paper presents a data-driven methodology for analyzing appliance consumption by automatically detecting and classifying appliance activations. The approach combines automatic activity detection with unsupervised clustering to reveal usage patterns. Evaluated on data from nine IK appliances, the methodology achieves outstanding performance, with average balanced accuracy and F1-scores exceeding 0.98. The unsupervised classification identifies distinct cycle modes for each appliance, with the optimal number of clusters varying across appliances. Load fluctuation patterns are found to be the most significant feature, with appliances like the ice machine exhibiting unique consumption behaviors compared to similar appliances like refrigerators. In contrast, appliances such as the salamander draw power consistently, regardless of activity duration. These findings not only contribute to a better understanding of energy use in IKs but also lay the groundwork for future research on demand response strategies and energy efficiency improvements in small-scale commercial kitchens.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110163"},"PeriodicalIF":4.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An effective deep learning approach enabling miners’ protective equipment detection and tracking using improved YOLOv7 architecture","authors":"Zheng Wang ,&nbsp;Yu Zhu ,&nbsp;Yingjie Zhang ,&nbsp;Siying Liu","doi":"10.1016/j.compeleceng.2025.110173","DOIUrl":"10.1016/j.compeleceng.2025.110173","url":null,"abstract":"<div><div>In the complex underground mining environment, ensuring the correct wearing of personal protective equipment (PPE) is crucial for coal mine safety production. To overcome the limitations of existing PPE detection and tracking technologies, which often suffer from low precision, slow performance, and complex feature extraction processes, this paper introduces an enhanced, lightweight, and high-precision object detection network model based on YOLOv7. The proposed model incorporates a streamlined backbone feature extraction architecture that combines the Mobile Inverted Bottleneck Convolution module with the GhostBottleneck Lightweight module. This integration significantly improves the detection accuracy of miners’ PPE while simultaneously reducing the number of network parameters. Furthermore, the model adopts adaptive spatial feature fusion to enhance its capability in effectively integrating cross-scale features, thereby further boosting its detection performance. To enable continuous and stable tracking of miners’ PPE usage, this paper integrates the DeepSort tracking algorithm, which is based on OSNet, with the improved YOLOv7 detection model. This combination constructs an efficient video-based multi-object tracking algorithm, providing essential support for enhancing the tracking performance of coal miners’ PPE. Experimental results demonstrate that, compared to other state-of-the-art methods, the proposed model achieves a 2.25% increase in mean Average Precision (mAP), a 2.91% improvement in F1 score, a 0.41% enhancement in precision, and a 5.34% increase in recall for PPE detection. Additionally, it exhibits significant improvements in multi-object tracking metrics, with a 5.9% increase in Multi-Object Tracking Accuracy (MOTA), a 3.5% increase in Multi-Object Tracking Precision (MOTP), and a 6.2% increase in IDF1 score. These results fully validate the model’s efficient detection and tracking capabilities for miners’ PPE in complex underground mining environments.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110173"},"PeriodicalIF":4.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429578","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 novel chemical property-based, alignment-free scalable feature extraction method for genomic data clustering","authors":"Rajesh Dwivedi ,&nbsp;Aruna Tiwari ,&nbsp;Neha Bharill ,&nbsp;Milind Ratnaparkhe ,&nbsp;Saurabh Kumar Singh ,&nbsp;Abhishek Tripathi","doi":"10.1016/j.compeleceng.2025.110175","DOIUrl":"10.1016/j.compeleceng.2025.110175","url":null,"abstract":"<div><div>In numerous fields of biological research, the precise clustering of genome sequences is of paramount importance. However, the inherent complexity and high dimensionality of genomic data produce substantial obstacles in achieving robust and efficient clustering results via traditional analysis, i.e., alignment-based approaches. The use of alignment-free approaches is one of the significant steps to perform the clustering efficiently. However, the majority of existing alignment-free approaches lack scalability, making it difficult to efficiently process the vast amount of genomic sequences. Moreover, the majority of approaches only extract the k-mer-based features and ignore the other significant features based on the classification of nucleotides according to their chemical properties. So, in order to handle these challenges, we present a novel scalable feature extraction method that efficiently handles large-scale genome data using the Apache Spark framework by distributing the tasks on various nodes and extracting the significantly important features based on the classification of nucleotides using their chemical properties in terms of entropy and the length of the sequence. The clustering of genome sequences is performed by taking extracted features using K-means, Fuzzy c-means, and Hierarchical agglomerative clustering. Our findings show that the proposed method improves generalization across many realistic plant genome and benchmark datasets and allows for the accurate clustering of formerly ambiguous cases.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110175"},"PeriodicalIF":4.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429576","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 method for detection of Low Frequency Oscillatory modes in power system for wide area monitoring system","authors":"Manoranjan Sahoo,&nbsp;Shekha Rai","doi":"10.1016/j.compeleceng.2025.110172","DOIUrl":"10.1016/j.compeleceng.2025.110172","url":null,"abstract":"<div><div>The integration of renewable energy sources with power grids has amplified interactions among energy resources, causing Low-Frequency Oscillations (LFOs). The assessment of LFOs is very crucial in order to incorporate corrective measures to ensure the small signal stability of power system. The Total Least Square Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT) method, known for precision in mode estimation, relies on prior knowledge of frequency components and loses accuracy at low signal-to-noise ratios (SNR). To overcome these limitations, this paper proposes an improved TLS-ESPRIT method utilizing a Singular Value (SV) ratio and a sequential partitioning technique for LFO modal parameter estimation. Initially, a SV ratio based low rank Hankel matrix filter is implemented to enhance noise resistance. Subsequently, a novel Model Order (MO) estimation technique is introduced using a convex combination of eigenvalue weightage to scale eigenvalue dominance in the trace of autocorrelation matrix and advanced medoid-based partitioning is explored to segregate the modes into signal and noise subspaces. In order to examine the efficacy of the suggested technique, comparison test of simulation results is conducted with some recently developed methods for synthetic signal, PMU data from a two area four machine system, practical probing data from the western electricity coordinating council, and oscillatory power data from the western system coordinated council 9 bus system and IEEE 39 bus system, simulated on real-time digital simulator.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110172"},"PeriodicalIF":4.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429575","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":"Multiple domain identification of fault arc based on KPCA-LSTM method","authors":"Puyi Cui ,&nbsp;Guoli Li ,&nbsp;Qian Zhang ,&nbsp;Zhenxing Qi","doi":"10.1016/j.compeleceng.2025.110171","DOIUrl":"10.1016/j.compeleceng.2025.110171","url":null,"abstract":"<div><div>Arc faults induce multi-domain variations, leading to low accuracy in identifying multi-domain arc faults. To address this issue, a multi-domain arc fault identification method based on Kernel Principal Component Analysis-Long Short-Term Memory (KPCA-LSTM) is proposed. This method utilizes Kernel Principal Component Analysis (KPCA) to reduce the dimensionality of multi-domain arc fault features, obtaining principal component vectors. Long Short-Term Memory (LSTM) is applied to extract features from the reduced dimensions, combined with Discrete Wavelet Transform (DWT) to model and extract frequency-domain features of arc faults. Furthermore, to mitigate noise interference, a signal threshold denoising method based on wavelet modulus maxima theory is proposed. The detail coefficients are calculated based on the type of arc fault point to capture signals across different frequency bands for multi-domain arc fault identification. Experimental results demonstrate that KPCA performs optimally in dimensionality reduction, achieving high model training accuracy. The accuracy of identifying individual branches and different types of faults exceeds 98 %, surpassing the Support Vector Machine (SVM) method. KPCA-LSTM exhibits superior performance in transient and continuous breakpoint faults, effectively improving the accuracy and efficiency of arc fault identification in power systems, thereby providing strong support for the safe operation of power systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110171"},"PeriodicalIF":4.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429574","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":"Bayesian-error-informed contrastive learning for knowledge-based question answering systems","authors":"Sudarshan Yerragunta ,&nbsp;Rajendra Prasath ,&nbsp;G.N. Girish","doi":"10.1016/j.compeleceng.2025.110142","DOIUrl":"10.1016/j.compeleceng.2025.110142","url":null,"abstract":"<div><div>The Knowledge-base Question Answering (KBQA) system aims to answer a question based on a knowledge base (KB). However, incomplete knowledge bases (KBs) limit the performance of KBQA systems. To address this issue, we propose a contrastive regularization method that considers two modules to tackle this problem: knowledge expansion and a contrastive loss function, Bayesian-error-informed Contrastive Learning (BeCoL). These modules leverage latent knowledge from context KBs and their associated question–answer pairs to generate more such pairs. Additionally, we use these question–answer pairs for informative representation learning, which makes hard positive pairs attract and hard negative pairs separate. This approach will enhance the ability of the system to distinguish the pairs better, ultimately improving the systems performance. We evaluate our proposed approach on the WebQuestionSP (WebQSP), ComplexWebQuestions (CompWebQ), and GrailQA datasets. The results indicate that our approach outperforms existing methods across different KB settings in the WebQSP dataset at 10%, 30%, 50%, and 100% with Hits@1 scores of 43.8, 49.7, 61.3, and 73.7 respectively, and with F₁-scores of 28.2, 32.5, 44.3, and 61.1 respectively. Similarly, we achieved Hits@1 score of 52.7 and F₁-score of 44.2 on the CompWebQ dataset with 100% KB setting. For the GrailQA dataset under the 100% KB setting, our method attained an Exact Match (EM) score of 67.5 and an F<span><math><msub><mrow></mrow><mrow><mn>1</mn></mrow></msub></math></span>-score of 76.4. The findings demonstrate the proposed methods capacity to address low-resource settings and significantly improve the performance of KBQA systems. The code is available at <span><span>https://github.com/ysudarshan-collab/BeCoL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110142"},"PeriodicalIF":4.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429577","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":"Advanced genetic image encryption algorithms for intelligent transport systems","authors":"Ismahane Souici ,&nbsp;Meriama Mahamdioua ,&nbsp;Sébastien Jacques ,&nbsp;Abdeldjalil Ouahabi","doi":"10.1016/j.compeleceng.2025.110162","DOIUrl":"10.1016/j.compeleceng.2025.110162","url":null,"abstract":"<div><div>Ensuring the security of sensitive or private information is crucial to prevent malicious tampering, especially in multimedia applications like intelligent transport systems (ITS), which are vital components of a smart city. These systems can be vulnerable to traffic management and rerouting techniques that manipulate the images captured by roadside units. To address this challenge, this paper introduces advanced image encryption algorithms designed specifically for securing image manipulation and transmission in roadside ITS units. Initially, a sequential version of the algorithm is proposed, demonstrating a high level of confusion achieved through the chosen coding method (chromosomal representation). This sequential approach results in maximum interference between the original image and its encrypted counterpart, with an entropy level of 7.95, nearing the optimal value of 8. To improve computational efficiency, three additional algorithms are presented, utilizing parallelization based on the islanding model, both with and without migrations. The algorithms are designed to enhance security by increasing confusion and incorporating genetic diffusion. The performance and security of these algorithms are evaluated using established methods such as information entropy, differential attack analysis, and key space analysis. Our algorithms have also shown a strong ability to maintain performance and robustness even in the presence of noise. Furthermore, they exhibit superior resistance to attacks compared to recent competitive approaches. In summary, the proposed algorithms offer robust protection against image manipulation and unauthorized access in roadside ITS units, thereby contributing to the overall security and reliability of smart city infrastructure.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110162"},"PeriodicalIF":4.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420087","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}