Computers & Electrical Engineering最新文献_第7页

Multiagent deep reinforcement learning-based distributed control strategy for energy management in DC Microgrid 基于多智能体深度强化学习的直流微电网能量管理分布式控制策略

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2025-12-31 DOI: 10.1016/j.compeleceng.2025.110927

Alankrita, Avadh Pati, Nabanita Adhikary

{"title":"Multiagent deep reinforcement learning-based distributed control strategy for energy management in DC Microgrid","authors":"Alankrita, Avadh Pati, Nabanita Adhikary","doi":"10.1016/j.compeleceng.2025.110927","DOIUrl":"10.1016/j.compeleceng.2025.110927","url":null,"abstract":"<div><div>This paper presents a Multi-Agent Deep Reinforcement learning (MARL) framework for distributed energy management in a DC Microgrid (DC MG) comprising Photovoltaic, Wind Turbine, and Energy Storage Systems, with the primary objective of maintaining DC link voltage stability. The decentralized control architecture employs local voltage measurements as agent state inputs and uses Deep Q-Networks to estimate individual action-value functions. Three algorithmic approaches are investigated: Independent DQN (IDQN), Value Decomposition Networks (VDN), and QMIX, each evaluated with Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) architectures. The custom reward function integrates voltage deviation penalties, power balance constraints, and battery cycling costs to achieve high renewable penetration and efficient storage dispatch. Case studies validate framework performance under diverse conditions, including variable generation and demand, network delays, false data injection attacks, ground faults, and plug-and-play topology changes. Results reveal scenario-dependent performance characteristics: RNN based VDN achieves superior voltage regulation under normal operation, IDQN demonstrates robust reward optimization during cyber-attacks, while RNN based QMIX excels in adversarial scenarios during false data injection and fastest transient response during plug-and-play events. Computational analysis identifies architecture-dependent scaling trade-offs, with QMIX requiring more compute requirements and centralized coordination overhead, while IDQN's distributed architecture and lower resource consumption suggest better scalability for multi-agent expansion. The framework demonstrates the practical viability of MARL-based distributed control for resilient energy management in DC MG with scenario-appropriate algorithm selection.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110927"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885936","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

Optimized 2-D FIR filter bank architecture using various symmetries with parallel processing and DA 利用并行处理和数据处理优化了二维FIR滤波器组结构

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.compeleceng.2026.110941

Venkata Krishna Odugu , P. Ramakrishna , T. Vasudeva Reddy , G Harish Babu , Janardhanarao S

{"title":"Optimized 2-D FIR filter bank architecture using various symmetries with parallel processing and DA","authors":"Venkata Krishna Odugu , P. Ramakrishna , T. Vasudeva Reddy , G Harish Babu , Janardhanarao S","doi":"10.1016/j.compeleceng.2026.110941","DOIUrl":"10.1016/j.compeleceng.2026.110941","url":null,"abstract":"<div><div>In this study, a new Filter Bank (FB) architecture for a 2-D FIR filter and implementation in VLSI design with the help of symmetric processing, parallelism, and Distributed Arithmetic (DA) ideas are presented. This work is motivated by the need for hardware-efficient 2D FIR filter architectures that reduce computational complexity, Power Consumption (PC), and resource usage in real-time image processing applications. Parallel processing is incorporated into the design to boost throughput and to decrease the quantity of multipliers, symmetry is introduced into the coefficients of the filter. In place of the remaining multipliers, Dual Port-Look-Up Table (DP-LUT)-based DA are proposed to reduce the area and power. Four types of symmetries are considered, and each architecture is explored and implemented using the proposed DA approach. Finally, all these filter structures are integrated by considering a common memory module and a control logic. Memory reuse and sharing are made possible by the FB design, which also allows for parallel processing. The suggested FB design has low resource requirements in terms of both memory and processing power. The hardware utilization synthesis summary is assessed for the target device of the Field Programmable Gate Array (FPGA). After that, the design is synthesized in 45 nm CMOS technology using Cadence's Genus tools for ASIC design. Existing 2-D FIR filter designs and traditional multiplier-based filter architectures are analyzed in terms of area, latency, and PC reports. The proposed FB architecture achieves up to 98.04% reduction in ADP and up to 64.51% reduction in PDP compared to existing designs, highlighting its efficiency in both area and power optimization. The proposed work's layout is then provided, including the Innovus tools used to determine the place and route.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110941"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978306","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

Attack a class of dynamic cryptosystem based on chaos 攻击一类基于混沌的动态密码系统

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.compeleceng.2026.110965

Rong Zhou

{"title":"Attack a class of dynamic cryptosystem based on chaos","authors":"Rong Zhou","doi":"10.1016/j.compeleceng.2026.110965","DOIUrl":"10.1016/j.compeleceng.2026.110965","url":null,"abstract":"<div><div>This study presents a cryptanalysis of a dynamic image cryptosystem based on chaos, referred to as DIC-BOC. Using DIC-BOC as a case study, the work introduces an innovative concept — termed T-ADTC (Thought of Applying Database to Cryptanalysis) — specifically designed to mount attacks against various instances of DIC-BOC. The particular DIC-BOC under investigation is an enhanced version of a plaintext-independent cryptosystem, featuring two key improvements to its dynamic mechanism: (1) linking the chaotic sequence used for encryption directly to the plaintext during the permutation stage, and (2) incorporating dynamic ciphertext feedback into the diffusion process. These enhancements significantly boost security compared to the original scheme. Although the authors assert the robustness of DIC-BOC based on empirical tests, rigorous cryptanalysis reveals critical vulnerabilities that render it susceptible to the proposed T-ADTC attack. Guided by T-ADTC, the study further refines this specific DIC-BOC, achieving additional advancements. Moreover, T-ADTC is not limited to this instance; it can be generalized to evaluate other DIC-BOC variants and offers crucial insights for the future development of cryptographic systems. Both theoretical analysis and experimental results confirm the feasibility and effectiveness of the proposed approach.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110965"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927512","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

Affinity-based fuzzy twin random vector functional link network classifier 基于亲和的模糊双随机向量功能链接网络分类器

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.compeleceng.2025.110923

Chittabarni Sarkar , Deepak Gupta , Rajat Subhra Goswami , Barenya Bikash Hazarika

{"title":"Affinity-based fuzzy twin random vector functional link network classifier","authors":"Chittabarni Sarkar , Deepak Gupta , Rajat Subhra Goswami , Barenya Bikash Hazarika","doi":"10.1016/j.compeleceng.2025.110923","DOIUrl":"10.1016/j.compeleceng.2025.110923","url":null,"abstract":"<div><div>In real-world, numerous leaf diseases are proliferating due to soil pollution and weather-related factors. Manual identification is slow and often ineffective. Identification hazards are created when noisy data and binary class imbalance problems are present. To address the noise and imbalanced data issue, several affinity and class probability-models were suggested, which reduce noise through regularization and handles class imbalance using affinity values from support vector data description (SVDD) and class probabilities from k-nearest neighbour (KNN). Minority samples with low affinity and probability receive less weight, while majority samples with higher values strongly influence the decision boundary. To enhance generalization an computational efficiency, an affinity and class probability-based fuzzy random vector functional link network (ACFRVFL) is introduced, combining fuzzy logic, SVDD, and KNN with RVFL. Moreover, an affinity and class probability-based fuzzy twin RVFL (ACFTRVFL) model is also suggested for improved performance. The study evaluates performance using various benchmark datasets.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110923"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927373","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 hybrid HBA-tuned DDPG reinforcement learning strategy for intelligent load frequency control in multi-area hybrid power systems 多区域混合电力系统负荷频率智能控制的混合hba调谐DDPG强化学习策略

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-10 DOI: 10.1016/j.compeleceng.2026.110945

Shasya Shukla, S.K. Jha

{"title":"A hybrid HBA-tuned DDPG reinforcement learning strategy for intelligent load frequency control in multi-area hybrid power systems","authors":"Shasya Shukla, S.K. Jha","doi":"10.1016/j.compeleceng.2026.110945","DOIUrl":"10.1016/j.compeleceng.2026.110945","url":null,"abstract":"<div><div>This study presents an advanced intelligent control strategy for Load Frequency Control (LFC) in a multi-area hybrid power system (HPS) comprising reheat thermal units, nuclear generation, and renewable energy sources (RESs) such as wind power, supported by a Battery Energy Storage System (BESS). The study proposes a novel HBA-tuned Deep Deterministic Policy Gradient Reinforcement Learning (DDPG-RL) controller designed to enhance dynamic frequency regulation under varying operating conditions. In the proposed approach, a reinforcement learning agent adaptively modulates governor setpoints and coordinates auxiliary energy resources to suppress frequency deviations. To further improve policy convergence and optimization quality, the critical hyperparameters of the agent are fine-tuned using the Honey Badger Algorithm (HBA), a recent nature-inspired metaheuristic based on the foraging intelligence and digging behavior of honey badgers. The hybrid HBA-DDPG framework enables robust adaptation to load fluctuations, renewable intermittency, and inter-area disturbances while maintaining tie-line power balance. Simulation studies demonstrate significant improvements over conventional controllers and standalone metaheuristic-based methods showing settling time (7.6 s.), maximum overshoot (1.4%), and overall error indices (ISE as 0.0022 and ITAE as 0.566) hence highlighting the effectiveness of combining reinforcement learning with metaheuristic optimization, offering a scalable, resilient, and high-performance solution for next-generation smart grids.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110945"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927368","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

Mizo hand sign language detection using a multi-scale transformer-based hybrid feature extractor and fusion network 基于多尺度变压器混合特征提取和融合网络的Mizo手语检测

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.compeleceng.2025.110916

Barrister R , Ambeth Kumar V. D , Ashok Kumar V. D

{"title":"Mizo hand sign language detection using a multi-scale transformer-based hybrid feature extractor and fusion network","authors":"Barrister R , Ambeth Kumar V. D , Ashok Kumar V. D","doi":"10.1016/j.compeleceng.2025.110916","DOIUrl":"10.1016/j.compeleceng.2025.110916","url":null,"abstract":"<div><div>Sign language is the primary means of communication for those who are hard of hearing or speaking. In daily lives, people rely on visual signals to express their thoughts and emotions because of deafness or being dumb. Most commonly, sign language is communicated through hand gestures and is analyzed in the present research, but it faces the problem of inaccurate detection of poses due to improper extraction of features. Also, the study and detection concerning Mizo sign language are very rarely seen in the literature. Hence, the proposed study presents a novel hybrid model that combines machine learning and deep learning to detect Mizo hand sign language. The Mizo hand sign language datasets are used in the first phase of the system evaluation process to assess its effectiveness. The next step involves pre-processing to remove extraneous background from photos. Next, a hybrid feature extraction is carried out using a depth-wise convolutional network (DCN) and a spatial-frequency multi-scale dilated transformer (SF-MSDT) in order to extract the significant features. The output of the hybrid feature extractor is fed independently over the feature fusion module to generate a single dimensional feature vector. In order to detect the Mizo sign language, classification is finally performed using three classifiers named support vector machine (SVM), random forest classifier, and Residual network (ResNet). The experimental analysis demonstrates the most feasible ResNet classifier with an accuracy of 98.23 %, precision of 92.36 %, recall of 88.52 %, and F1-score of 85.77 %. The proposed model using a ResNet classifier possesses 1.25 % improved accuracy when compared with recurrent networks and 4.3 % with convolutional networks.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110916"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842637","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

Hybrid tree-based indexing for efficient data retrieval in Smart Grids 基于混合树的智能电网数据检索方法

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-17 DOI: 10.1016/j.compeleceng.2026.110973

Abdelbacet Brahmia , Zineddine Kouahla , Ala Eddine Benrazek , Brahim Farou , Hamid Seridi

{"title":"Hybrid tree-based indexing for efficient data retrieval in Smart Grids","authors":"Abdelbacet Brahmia , Zineddine Kouahla , Ala Eddine Benrazek , Brahim Farou , Hamid Seridi","doi":"10.1016/j.compeleceng.2026.110973","DOIUrl":"10.1016/j.compeleceng.2026.110973","url":null,"abstract":"<div><div>The Smart Grid, a prominent IoT application, is experiencing rapid growth driven by the proliferation of connected embedded devices. This evolution has resulted in an exponential increase in time-series data, emphasizing the need for efficient data storage and retrieval mechanisms, particularly for real-time IoT environments. Existing indexing structures primarily focus on either time-based or consumption-based organization, often overlooking the interdependence between these dimensions, which limits their query efficiency. To address this limitation, this paper introduces a novel Temporal-Consumption Binary Tree (TCB-Tree), a hybrid tree-based indexing structure that jointly exploits temporal and consumption attributes for efficient data retrieval. The proposed method operates in three main phases: (i) horizontal segmentation, which applies clustering to identify key consumption levels; (ii) vertical segmentation, which groups temporally successive data within the same consumption range; and (iii) hybrid index construction, where internal nodes index time while leaf nodes index consumption patterns. Experimental evaluation using three real-world datasets demonstrates that the TCB-Tree achieves rapid construction times (under 0.20 s) and efficient hybrid query execution (under 0.9 s) on large datasets, while maintaining minimal storage overhead (below 18%). These results confirm the scalability, efficiency, and suitability of the proposed structure for Smart Grid and real-time IoT applications.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110973"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978233","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 analysis of the state of art on emotion recognition using EEG 基于脑电图的情绪识别研究现状综合分析

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.compeleceng.2026.110958

Anju Mishra , Priya Ranjan

引用次数: 0

Component alignment-aware sparse time–frequency distribution reconstruction for complex signals with coexisting oscillatory and transient components 振荡分量和瞬态分量共存的复信号稀疏时频分布重构

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.compeleceng.2026.110986

Vedran Jurdana

{"title":"Component alignment-aware sparse time–frequency distribution reconstruction for complex signals with coexisting oscillatory and transient components","authors":"Vedran Jurdana","doi":"10.1016/j.compeleceng.2026.110986","DOIUrl":"10.1016/j.compeleceng.2026.110986","url":null,"abstract":"<div><div>Compressive sensing (CS) enables high-resolution reconstruction of time–frequency distributions (TFDs) for non-stationary signals. The Rényi entropy-based two-step iterative shrinkage/thresholding (RTwIST) algorithm addresses regularization challenges through component-wise shrinkage guided by local Rényi entropy (LRE). However, RTwIST exhibits reconstruction inaccuracies for signals with components of differing time–frequency orientations due to global shrinkage and imprecise LRE estimation. This study proposes an enhanced RTwIST framework incorporating a component alignment map (CAM), which utilizes orientation estimation to segment the TFD into regions dominated by time- or frequency-aligned components. This localized segmentation enables adaptive shrinkage tailored to each region, and automates LRE parameter selection. Experiments on synthetic signals and real-world datasets, including gravitational wave and electroencephalogram (EEG) seizure signals, demonstrate improved auto-term resolution, reduced cross-term interference, and lower tuning complexity compared to standard RTwIST and state-of-the-art methods. These improvements support more accurate analysis of complex oscillatory and transient signals found in astrophysics, biomedical engineering, and beyond.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110986"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978313","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

Enhanced ECG arrhythmia detection with deep learning and multi-head attention mechanism 利用深度学习和多头注意机制增强心律失常检测

IF 4.9 3区计算机科学

Computers & Electrical Engineering Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.compeleceng.2026.110957

Saoueb Kerdoudi , Larbi Guezouli , Tahar Dilekh

{"title":"Enhanced ECG arrhythmia detection with deep learning and multi-head attention mechanism","authors":"Saoueb Kerdoudi , Larbi Guezouli , Tahar Dilekh","doi":"10.1016/j.compeleceng.2026.110957","DOIUrl":"10.1016/j.compeleceng.2026.110957","url":null,"abstract":"<div><div>Detecting arrhythmias via electrocardiograms (ECGs) is vital for healthcare. While deep learning has advanced classification, capturing critical patterns in complex data remains challenging. We propose Res_Bi-LSTM_MHA, a novel model integrating a multi-head self-attention (MHA) mechanism to selectively focus on relevant signal segments. This enhances the capture of subtle features often missed by conventional methods. By combining Residual Networks (ResNet) for robust feature extraction with Bidirectional Long Short-Term Memory (Bi-LSTM) for temporal dependencies, our approach significantly improves accuracy. We evaluated the model at subject and record levels using the China Physiological Signal Challenge (CPSC 2018), St. Petersburg Institute of Cardiological Technics (INCART), and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) databases. The model achieved an F1 score of 98.01% and 99.42% accuracy on the MIT-BIH dataset. Our results demonstrate that effectively utilizing attention mechanisms offers a substantial improvement in arrhythmia classification.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110957"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927375","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