Computers & Electrical Engineering最新文献_第7页

Retraction notice to “Innovation of engineering teaching methods based on multimedia assisted technology” [Computers and Electrical Engineering 100 (2022) 107867] 关于“基于多媒体辅助技术的工程教学方法创新”的撤稿通知[计算机与电气工程100 (2022)107867]

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.compeleceng.2026.110981

Jianping Fu

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 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

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

Rong Zhou

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.

本研究提出一种基于混沌的动态图像密码系统的密码分析方法，称为DIC-BOC。使用DIC-BOC作为案例研究，该工作引入了一个创新概念-称为T-ADTC（将数据库应用于密码分析的想法）-专门设计用于对各种DIC-BOC实例进行攻击。正在研究的特定DIC-BOC是一种独立于明文的密码系统的增强版本，其动态机制有两个关键改进：(1)在排列阶段将用于加密的混沌序列直接链接到明文，以及(2)将动态密文反馈纳入扩散过程。与原始方案相比，这些增强功能显著提高了安全性。尽管作者基于经验测试断言DIC-BOC的稳健性，但严格的密码分析揭示了使其容易受到提议的T-ADTC攻击的关键漏洞。在T-ADTC的指导下，该研究进一步完善了这种特定的DIC-BOC，取得了额外的进展。此外，T-ADTC并不局限于这种情况；它可以推广到评估其他DIC-BOC变体，并为加密系统的未来发展提供重要见解。理论分析和实验结果均证实了该方法的可行性和有效性。

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引用次数: 0

From hardness assumptions to energy-secure protocols: A systematic survey of Euclidean lattice-based cryptography 从硬度假设到能量安全协议：欧几里得格密码学的系统研究

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-03-01 Epub Date: 2026-01-17 DOI: 10.1016/j.compeleceng.2026.110971

Mourad Yessef , Youness Hakam , Mohamed Tabaa , Lhoussaine Ahessab , Z.M.S. Elbarbary , Salman Arafath Mohammed , Naim Ahmad

The fast development of quantum computing poses major challenges for classical cryptographic techniques, hence post-quantum cryptography is needed. Emphasizing their fit for securing energy-critical infrastructure, this work methodically reviews lattice-based cryptographic systems. Theoretically strong and practically relevant fundamental lattices including the Shortest Vector Problem (SVP), Learning with Errors (LWE), and Module-LWE are investigated in limited environments including smart grids and IoT devices. Examined are important advancements in hardware implementations, algorithmic optimizations, and cryptanalysis with an eye toward programs including Falcon, Dilithium, and CRYSTALS-Kyber. Over systems including Vehicle-to- Grid (V2G) networks and Supervisory Control and Data Acquisition (SCADA) systems, lattice-based cryptography’s efficacy and deployability are shown. The review ends with a discussion of future research paths to support long-term quantum-safe infrastructure security and newly arising theoretical hazards.

量子计算的快速发展对传统的密码技术提出了挑战，因此需要后量子密码技术。强调它们适合保护能源关键基础设施，这项工作系统地回顾了基于格的密码系统。在包括智能电网和物联网设备在内的有限环境中，研究了包括最短向量问题（SVP）、带误差学习（LWE）和模块-LWE在内的理论强大和实际相关的基本格。研究了硬件实现、算法优化和密码分析方面的重要进展，并着眼于Falcon、Dilithium和CRYSTALS-Kyber等程序。在包括车辆到电网（V2G）网络和监控和数据采集（SCADA）系统在内的系统中，显示了基于格的加密技术的有效性和可部署性。文章最后讨论了未来的研究路径，以支持长期量子安全基础设施的安全和新出现的理论危害。

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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 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

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

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.

在现实世界中，由于土壤污染和天气相关因素，许多叶片病害正在蔓延。手动识别是缓慢的，而且常常是无效的。当存在噪声数据和二元类不平衡问题时，会产生识别危害。为了解决噪声和不平衡数据问题，提出了几种亲和和类概率模型，这些模型通过正则化来降低噪声，并使用支持向量数据描述（SVDD）的亲和值和k近邻（KNN）的类概率来处理类不平衡。亲和性和概率较低的少数样本权重较小，而较高的多数样本对决策边界的影响较大。为了提高泛化和计算效率，将模糊逻辑、SVDD和KNN与RVFL相结合，提出了一种基于亲和和类概率的模糊随机向量功能链接网络（ACFRVFL）。此外，还提出了一种基于亲和性和类概率的模糊双RVFL （ACFTRVFL）模型来提高性能。该研究使用各种基准数据集评估性能。

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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 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

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

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.

本研究提出了一种先进的智能控制策略，用于多区域混合电力系统（HPS）的负载频率控制（LFC），该系统由再热热机组、核能发电和可再生能源（RESs）（如风能）组成，由电池储能系统（BESS）支持。该研究提出了一种新的hba调谐深度确定性策略梯度强化学习（DDPG-RL）控制器，旨在增强不同工作条件下的动态频率调节。在提出的方法中，强化学习代理自适应调节调节器设定值并协调辅助能量资源以抑制频率偏差。为了进一步提高策略的收敛性和优化质量，使用蜜獾算法（HBA）对代理的关键超参数进行微调。蜜獾算法是一种基于蜜獾觅食智能和挖掘行为的自然启发元启发式算法。混合HBA-DDPG框架能够在保持联络线功率平衡的同时，对负载波动、可再生间歇性和区域间干扰进行强大的适应。仿真研究表明，与传统控制器和独立的基于元启发式的方法相比，该方法有了显著的改进，显示了稳定时间（7.6秒）、最大超调量（1.4%）和总体误差指数（ISE为0.0022，ITAE为0.566），从而突出了将强化学习与元启发式优化相结合的有效性，为下一代智能电网提供了可扩展、有弹性和高性能的解决方案。

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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 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

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

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.

手语是那些听力或语言有障碍的人的主要交流手段。在日常生活中，由于耳聋或哑，人们依靠视觉信号来表达自己的思想和情感。最常见的手语是通过手势进行交流，本研究对其进行了分析，但由于特征提取不当，存在姿势检测不准确的问题。此外，对米佐语手语的研究和检测在文献中也很少见到。因此，本研究提出了一种结合机器学习和深度学习的新型混合模型来检测Mizo手语。Mizo手语数据集用于系统评估过程的第一阶段，以评估其有效性。下一步是对照片进行预处理，去除多余的背景。其次，利用深度卷积网络（DCN）和空频多尺度膨胀变压器（SF-MSDT）进行混合特征提取，以提取重要特征。混合特征提取器的输出通过特征融合模块独立馈送，生成单维特征向量。为了检测Mizo手语，最后使用支持向量机（SVM）、随机森林分类器和残差网络（ResNet）三种分类器进行分类。实验分析表明，最可行的ResNet分类器准确率为98.23%，精密度为92.36%，召回率为88.52%，f1评分为85.77%。使用ResNet分类器的模型与循环网络相比准确率提高了1.25%，与卷积网络相比准确率提高了4.3%。

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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 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

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

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.

智能电网是一个突出的物联网应用，在连接嵌入式设备激增的推动下，正在经历快速增长。这种演变导致时间序列数据呈指数级增长，强调了对高效数据存储和检索机制的需求，特别是对于实时物联网环境。现有的索引结构主要关注基于时间或基于消费的组织，通常忽略了这些维度之间的相互依赖关系，从而限制了它们的查询效率。为了解决这一限制，本文引入了一种新的时间消费二叉树（TCB-Tree），这是一种混合的基于树的索引结构，它联合利用时间和消费属性来进行有效的数据检索。该方法分为三个主要阶段：(i)水平分割，利用聚类来识别关键消费水平；（ii）垂直分割，将同一消费范围内的时间连续数据分组；(3)混合索引构建，其中内部节点索引时间，叶节点索引消费模式。使用三个真实数据集的实验评估表明，TCB-Tree在大型数据集上实现了快速的构建时间（低于0.20 s）和高效的混合查询执行（低于0.9 s），同时保持最小的存储开销（低于18%）。这些结果证实了所提出的结构在智能电网和实时物联网应用中的可扩展性、效率和适用性。

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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 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

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

Anju Mishra , Priya Ranjan

Emotion recognition from physiological signals is an emerging field due to its vast application areas. The electroencephalogram (EEG) as a physiological marker in developing automated emotion recognition systems is gaining popularity with its ability to capture the brain's electrical activity providing a window into understanding how these emotional states are represented and processed. Because of this inherent capability of EEG recordings, this systematic review intends to give the readers a comprehensive understanding of the state of the art of the emotion recognition domain and the tools and technologies used by other contemporary researchers in this field. The review outlines the latest research in the field and also performs a comprehensive analysis of available literature to identify the best tools and technologies used by researchers in the domain at every step of the development of such models. The final section of the review tries to point out some directions that can be worked out in the future by the researchers.

基于生理信号的情绪识别是一个新兴的领域，有着广阔的应用领域。脑电图（EEG）作为开发自动情绪识别系统的一种生理标记物越来越受欢迎，因为它能够捕捉大脑的电活动，为理解这些情绪状态是如何表征和处理的提供了一个窗口。由于脑电图记录的这种固有能力，本系统综述旨在让读者全面了解情绪识别领域的最新技术，以及该领域其他当代研究人员使用的工具和技术。该综述概述了该领域的最新研究，并对现有文献进行了全面分析，以确定该领域研究人员在开发此类模型的每一步中使用的最佳工具和技术。评论的最后一部分试图指出研究人员未来可以制定的一些方向。

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 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

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

Vedran Jurdana

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.

压缩感知（CS）可以实现非平稳信号的时频分布（TFDs）的高分辨率重建。基于r郁闷熵的两步迭代收缩/阈值（RTwIST）算法通过局部r郁闷熵（LRE）指导的组件收缩来解决正则化挑战。然而，由于全局收缩和不精确的LRE估计，RTwIST对具有不同时频方向分量的信号表现出重建不准确性。本研究提出了一个增强的RTwIST框架，该框架结合了一个组件对齐图（CAM），它利用方向估计将TFD分割成由时间或频率对齐组件主导的区域。这种局部分割可以根据每个区域进行自适应收缩，并自动选择LRE参数。在合成信号和现实世界数据集（包括引力波和脑电图（EEG）发作信号）上进行的实验表明，与标准RTwIST和最先进的方法相比，该方法提高了自动期分辨率，减少了交叉期干扰，降低了调谐复杂性。这些改进支持对天体物理学、生物医学工程等领域的复杂振荡和瞬态信号进行更准确的分析。

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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 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

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

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.

通过心电图（ecg）检测心律失常对医疗保健至关重要。虽然深度学习具有高级分类，但在复杂数据中捕获关键模式仍然具有挑战性。我们提出了一种新的模型Res_Bi-LSTM_MHA，该模型集成了多头自注意（MHA）机制，可以选择性地关注相关信号段。这增强了对传统方法经常错过的细微特征的捕捉。通过将残差网络（ResNet）用于鲁棒特征提取和双向长短期记忆（Bi-LSTM）用于时间依赖性，我们的方法显着提高了准确性。我们使用中国生理信号挑战（CPSC 2018）、圣彼得堡心脏病技术研究所（INCART）和麻省理工学院-贝斯以色列医院（MIT-BIH）的数据库在受试者和记录水平上评估了该模型。该模型在MIT-BIH数据集上的F1得分为98.01%，准确率为99.42%。我们的研究结果表明，有效地利用注意力机制可以大大改善心律失常的分类。

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引用次数: 0