Computers & Electrical Engineering最新文献_第6页

A game-theoretic approach to fair and grid-aware load flexibility allocation in residential distribution networks 基于博弈论的居民配电网负荷柔性公平分配方法

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

Computers & Electrical Engineering

Pub Date : 2026-01-15 DOI: 10.1016/j.compeleceng.2026.110976

Gabriel Gómez-Ruiz, Jesús Clavijo-Camacho, Reyes Sánchez-Herrera, José M. Andújar

This article evaluates the potential of thermostatically controlled loads (TCL) as flexible resources to improve power quality―particularly phase unbalance―in low-voltage residential distribution networks while ensuring fair consumer participation. To address both grid-level and social objectives, the adaptive fairness and grid-aware allocation (AFGA) algorithm is proposed. This algorithm integrates cooperative game theory and Nash bargaining principles to jointly optimize phase balancing and consumer utility. The proposed approach dynamically allocates residential consumer flexibility by accounting for phase-level constraints, individual flexibility capacity, and historical participation, thereby preventing the persistent overuse of specific consumers and promoting equitable long-term engagement. Simulation results on a representative residential network with 100 households demonstrate that, with only 20% participation, the AFGA algorithm reduces the unbalance load factor (ULF) to below 10%, achieves a highly equitable distribution of benefits (Gini index = 0.065), and effectively enforces adaptive fairness through penalty-feedback mechanisms. Furthermore, the algorithm completes a full-day simulation in 102 s with only 0.24 MB of peak memory usage. These findings position the AFGA algorithm as an effective and scalable solution for integrating fairness-aware residential flexibility into the operation of low-voltage residential distribution networks.

本文评估了恒温控制负载（TCL）作为灵活资源的潜力，以改善低压住宅配电网的电力质量，特别是相位不平衡，同时确保公平的消费者参与。为了同时解决网格级和社会级目标，提出了自适应公平和网格感知分配（AFGA）算法。该算法结合合作博弈理论和纳什议价原则，共同优化阶段平衡和消费者效用。该方法通过考虑阶段约束、个人灵活能力和历史参与来动态分配住宅消费者的灵活性，从而防止特定消费者的持续过度使用，促进公平的长期参与。在100户代表性居民网络上的仿真结果表明，在参与率仅为20%的情况下，AFGA算法将不平衡负荷因子（ULF）降低到10%以下，实现了高度公平的利益分配（基尼系数= 0.065），并通过惩罚反馈机制有效地实现了自适应公平。此外，该算法在102秒内完成全天模拟，峰值内存使用仅为0.24 MB。这些发现将AFGA算法定位为一种有效且可扩展的解决方案，用于将公平意识的住宅灵活性整合到低压住宅配电网的运行中。

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引用次数: 0

A reversible image steganography framework against gradient inversion attacks via saliency-guided embedding 一种基于显著性嵌入的抗梯度反转攻击的可逆图像隐写框架

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

Computers & Electrical Engineering

Pub Date : 2026-01-15 DOI: 10.1016/j.compeleceng.2026.110951

Chen Liang , Yuxin Zhou , Ziqi Wang , Jiamin Zheng

With the widespread application of edge collaborative inference, concerns regarding data privacy and model interpretability are increasingly prominent. Gradient inversion attacks can reconstruct sensitive input data from leaked gradients, posing a significant threat to image confidentiality. Meanwhile, traditional image steganography techniques do not take into full consideration the semantic structures inherent in visual content. This can lead to suboptimal embedding locations and limited resilience to semantic perturbations, ultimately resulting in reduced robustness and concealment performance. To address the dual challenge of preserving semantic fidelity and resisting gradient-based inversion attacks in image steganography, this paper proposes an image steganography framework,named CamDWT which integrates semantic attention, frequency-domain embedding, and adversarial reversibility optimization. The proposed method combines Grad-CAM, Discrete Wavelet Transform (DWT), and gradient inversion to achieve semantically aware and robust image steganography. Grad-CAM is used to identify salient regions in the image based on class-specific activations, and secret information is embedded into the high-frequency components of these regions using DWT. During the inversion process, a dual-loss strategy is employed to ensure both gradient consistency and frequency-domain alignment, enhancing the fidelity and recoverability of the hidden content. Experimental results show a high degree of consistency in the salient regions of the original, stego, and reconstructed images. This is validated by four metrics — PCC, cosine similarity, IoU, and Top-K overlap — all meeting the required thresholds. The proposed method achieves an information extraction accuracy of over 98%, representing a 7.3% improvement compared to existing approaches. Moreover, the method exhibits robustness in embedding fidelity and ensures reliable recovery under inversion attacks.

随着边缘协同推理的广泛应用，对数据隐私和模型可解释性的关注日益突出。梯度反转攻击可以利用泄露的梯度重构敏感输入数据，对图像的保密性构成严重威胁。同时，传统的图像隐写技术没有充分考虑视觉内容固有的语义结构。这可能导致次优嵌入位置和对语义扰动的有限弹性，最终导致鲁棒性和隐藏性能降低。为了解决图像隐写中保持语义保真度和抵抗基于梯度的反转攻击的双重挑战，本文提出了一种集成了语义关注、频域嵌入和对抗可逆性优化的图像隐写框架CamDWT。该方法将梯度- cam、离散小波变换（DWT）和梯度反演相结合，实现了语义感知和鲁棒的图像隐写。Grad-CAM基于特定类别的激活来识别图像中的显著区域，并使用DWT将秘密信息嵌入到这些区域的高频成分中。在反演过程中，采用双损耗策略保证梯度一致性和频域对准，增强了隐藏内容的保真度和可恢复性。实验结果表明，在显著区域的原始，隐去和重建图像的高度一致性。这是通过四个指标验证的——PCC、余弦相似性、IoU和Top-K重叠——所有这些指标都满足所需的阈值。该方法的信息提取准确率达到98%以上，与现有方法相比提高了7.3%。此外，该方法在嵌入保真度方面具有鲁棒性，保证了在反攻击下的可靠恢复。

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引用次数: 0

NGCF-RVFL: Next Generation Convolutional Feature with Random Vector Functional Link for multi-grade diabetic retinopathy detection NGCF-RVFL：基于随机向量功能链接的新一代卷积特征用于多级别糖尿病视网膜病变检测

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

Computers & Electrical Engineering

Pub Date : 2026-01-15 DOI: 10.1016/j.compeleceng.2026.110972

Imtiyaz Ahmad, Vibhav Prakash Singh, Manoj Madhava Gore

Diabetic Retinopathy (DR) is one of the leading causes of vision impairment and blindness globally, necessitating early and accurate detection for timely clinical intervention. This paper proposes NGCF-RVFL, a novel Computer-aided-diagnosis system for multi-grade DR detection from retinal fundus images. The working of this system begins with an enhanced preprocessing pipeline that includes median filtering, Gaussian filtering, and Contrast-limited adaptive histogram equalization to reduce noise and improve contrast of the fundus images. Next, we introduce an adaptive image augmentation technique to address the issue of class imbalance. Minority class samples are increased using an augmentation that adapts the size of majority class samples. After that, we propose a Next Generation Convolutional Feature (NGCF) based on the fine-tuned ConvNeXt architecture, consisting of a hierarchical design with four feature extraction stages utilizing depthwise separable convolutions. The NGCF feature effectively encodes intricate retinal structures and disease patterns crucial for accurate DR grading. Further, the discriminative analysis with Principal Component Analysis confirms the significance and effectiveness of the extracted NGC feature in representing relevant retinal information. Furthermore, a lightweight network, Random Vector Functional Link (RVFL), is employed to evaluate the grade-wise detection performance of the proposed NGCF feature. Unlike traditional iterative learning models, the RVFL utilizes a single-pass training mechanism, significantly reducing computation time while maintaining high detection performance. Finally, we evaluate the effectiveness and detection performance of the NGCF feature on other machine learning classifiers such as Support vector machine, Multilayer perceptron, Random forest, and Decision tree. Comprehensive experiments on a benchmark dataset demonstrate that NGCF-RVFL achieves competitive scores across all DR grades with minimal training time, outperforming the state-of-the-art approaches.

糖尿病视网膜病变（DR）是全球视力损害和失明的主要原因之一，需要及早准确发现，及时进行临床干预。本文提出了一种新的基于视网膜眼底图像的多级DR检测计算机辅助诊断系统NGCF-RVFL。该系统的工作从增强的预处理管道开始，包括中值滤波、高斯滤波和对比度有限的自适应直方图均衡化，以减少噪声并提高眼底图像的对比度。接下来，我们引入一种自适应图像增强技术来解决类别不平衡的问题。使用适应多数类样本大小的增强来增加少数类样本。之后，我们提出了基于微调的ConvNeXt架构的下一代卷积特征（NGCF），该架构包括一个分层设计，利用深度可分离卷积进行四个特征提取阶段。NGCF特征有效地编码复杂的视网膜结构和疾病模式，这对准确的DR分级至关重要。此外，主成分分析的判别分析证实了提取的NGC特征在表示相关视网膜信息方面的重要性和有效性。此外，采用了一个轻量级网络随机向量功能链路（RVFL）来评估所提出的NGCF特征的分级检测性能。与传统的迭代学习模型不同，RVFL采用单次训练机制，在保持高检测性能的同时显著减少了计算时间。最后，我们评估了NGCF特征在其他机器学习分类器（如支持向量机、多层感知器、随机森林和决策树）上的有效性和检测性能。在一个基准数据集上进行的综合实验表明，NGCF-RVFL以最少的训练时间在所有DR等级中获得了具有竞争力的分数，优于最先进的方法。

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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 : 2026-01-14 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

A real-time smart energy management system for greenhouses using a hybrid optimization algorithm: Experimental implementation for efficient and sustainable operation 基于混合优化算法的温室实时智能能源管理系统：高效可持续运行的实验实现

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

Computers & Electrical Engineering

Pub Date : 2026-01-12 DOI: 10.1016/j.compeleceng.2026.110948

Mohamed W. Haggag , Asmaa H. Rabie , Islam Ismael , Waleed Shaaban

The increasing global demand for food and energy, along with climate change and resource scarcity, causes potential challenges to sustainable agriculture. Smart greenhouses create controlled environments that optimize crop production and minimize resource use, especially in arid regions. This paper introduces a Real-Time Smart Greenhouse Energy Management (SGEM) system that combines IoT-based sensing, renewable energy sources, and a Hybrid Optimization Algorithm (HOA). The HOA combines Particle Swarm Optimization (PSO) for global exploration with the Coati Optimization Algorithm (COA) for local exploitation. To optimize operating costs, battery State of Charge (SoC), and the use of renewable energy, the HOA dynamically gathers energy from photovoltaic panels (PV), battery storage, and the electrical grid. The system is designed as a hybrid PV-battery-grid configuration, validated through both simulation and experimental implementation, guaranteeing a steady supply of energy while minimizing grid dependency. Experimental validation shows that the SGEM system reduces costs by 49.98%, lowers daily grid consumption by 50.24%, cuts CO₂ emissions by 50.5%, and extends battery life by 14.7%. The results obtained demonstrate the system’s capability for adaptive, efficient, and sustainable greenhouse energy management, providing a scalable solution for modern smart agriculture.

全球对粮食和能源的需求不断增加，加上气候变化和资源短缺，对可持续农业构成了潜在挑战。智能温室创造可控环境，优化作物生产，最大限度地减少资源使用，特别是在干旱地区。本文介绍了一种结合物联网传感、可再生能源和混合优化算法（HOA）的实时智能温室能源管理（SGEM）系统。该算法将粒子群优化算法（PSO）与Coati优化算法（COA）相结合，进行全局勘探和局部开发。为了优化运营成本、电池充电状态（SoC）和可再生能源的使用，HOA动态地从光伏板（PV）、电池存储和电网收集能量。该系统被设计为混合pv -电池-电网配置，通过仿真和实验实施验证，保证稳定的能源供应，同时最大限度地减少对电网的依赖。实验验证表明，SGEM系统降低了49.98%的成本，降低了50.24%的日电网消耗，减少了50.5%的二氧化碳排放量，延长了14.7%的电池寿命。所获得的结果证明了该系统具有适应性、高效和可持续的温室能源管理能力，为现代智能农业提供了可扩展的解决方案。

{"title":"A real-time smart energy management system for greenhouses using a hybrid optimization algorithm: Experimental implementation for efficient and sustainable operation","authors":"Mohamed W. Haggag , Asmaa H. Rabie , Islam Ismael , Waleed Shaaban","doi":"10.1016/j.compeleceng.2026.110948","DOIUrl":"10.1016/j.compeleceng.2026.110948","url":null,"abstract":"<div><div>The increasing global demand for food and energy, along with climate change and resource scarcity, causes potential challenges to sustainable agriculture. Smart greenhouses create controlled environments that optimize crop production and minimize resource use, especially in arid regions. This paper introduces a Real-Time Smart Greenhouse Energy Management (SGEM) system that combines IoT-based sensing, renewable energy sources, and a Hybrid Optimization Algorithm (HOA). The HOA combines Particle Swarm Optimization (PSO) for global exploration with the Coati Optimization Algorithm (COA) for local exploitation. To optimize operating costs, battery State of Charge (SoC), and the use of renewable energy, the HOA dynamically gathers energy from photovoltaic panels (PV), battery storage, and the electrical grid. The system is designed as a hybrid PV-battery-grid configuration, validated through both simulation and experimental implementation, guaranteeing a steady supply of energy while minimizing grid dependency. Experimental validation shows that the SGEM system reduces costs by 49.98%, lowers daily grid consumption by 50.24%, cuts CO₂ emissions by 50.5%, and extends battery life by 14.7%. The results obtained demonstrate the system’s capability for adaptive, efficient, and sustainable greenhouse energy management, providing a scalable solution for modern smart agriculture.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110948"},"PeriodicalIF":4.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978314","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 comprehensive review of computational techniques for obscenity detection: Past, present, and future 淫秽检测的计算技术的全面回顾：过去，现在和未来

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

Computers & Electrical Engineering

Pub Date : 2026-01-12 DOI: 10.1016/j.compeleceng.2026.110960

Pundreekaksha Sharma , Dr. Vijay Kumar , Dr. Neeraj Kumar

With the rapid proliferation of obscene content over the internet, detecting and preventing obscenity has become the most prominent way of maintaining a safe digital environment. The accessibility of obscene content has significant psychological, social, ethical, and technological impacts. To overcome these challenges, it is essential to develop an obscenity detection system using advanced artificial intelligence techniques, including social and ethical considerations that prevent the spread of obscenity. This research has presented a comprehensive literature analysis covering traditional to advanced computational techniques for obscenity detection. It also serves as a valuable resource for researchers improving obscenity detection techniques. Analyse computer vision techniques for obscenity detection, featuring hybrid deep learning methods including Transformers, vision transformers, diffusion models, and other techniques. Additionally, this research discusses the strengths and limitations of these techniques. Examines the mathematical formulations and equations of the models, and the impact of input and additional parameters. Compare the performance of models on various datasets and discuss how to develop a diverse dataset. A significant overview of social and ethical considerations included in obscenity detection. The research paper also highlights challenges and potential future research directions in obscenity detection. In conclusion, this research provides a gap analysis that helps researchers enhance computational techniques for obscenity detection.

随着互联网上淫秽内容的迅速扩散，检测和防止淫秽内容已成为维护安全数字环境的最重要方式。淫秽内容的可及性具有显著的心理、社会、伦理和技术影响。为了克服这些挑战，必须开发一种使用先进人工智能技术的淫秽内容检测系统，包括防止淫秽内容传播的社会和伦理考虑。本研究提出了一个全面的文献分析，涵盖传统到先进的计算技术的淫秽检测。它也为研究人员改进淫秽检测技术提供了宝贵的资源。分析用于淫秽内容检测的计算机视觉技术，包括混合深度学习方法，包括变形金刚、视觉变形金刚、扩散模型和其他技术。此外，本研究还讨论了这些技术的优点和局限性。检查模型的数学公式和方程，以及输入和附加参数的影响。比较模型在不同数据集上的性能，并讨论如何开发不同的数据集。一个重要的社会和道德考虑的概述，包括在淫秽检测。研究论文还强调了淫秽物检测面临的挑战和潜在的未来研究方向。总之，这项研究提供了一个差距分析，帮助研究人员提高淫秽检测的计算技术。

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引用次数: 0

A multi-feature distance measure for time series classification 一种用于时间序列分类的多特征距离度量

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

Computers & Electrical Engineering

Pub Date : 2026-01-12 DOI: 10.1016/j.compeleceng.2025.110925

Sai Zhang , Wu Le , Zhen-Hong Jia , Hao Wu

Existing time series similarity measures are often difficult to apply to large-scale datasets due to their high computational complexity. Some solutions that pursue linear complexity usually come at the expense of fine-grained analysis of sequence dynamics, resulting in insufficient discriminative ability in complex scenarios. In this paper, we propose a multi-feature fusion algorithm that can achieve a fine-grained measure of sequence similarity while maintaining linear complexity. First, this paper introduces a novel subsequence trend encoding mechanism, which provides a new perspective beyond the traditional structural features for similarity judgment by quantifying the dynamic direction within the subsequence. Second, the algorithm comprehensively evaluates candidate subsequences from both complexity and trend perspectives, and forms a more robust distance metric by weighted fusion of the two features, thus effectively reducing the misjudgments that a single perspective may cause. Experimental results on 70 UCR benchmark datasets validate our approach, which not only achieves the #1 average rank in classification accuracy among 17 state-of-the-art algorithms but also demonstrates exceptional efficiency, proving to be orders of magnitude faster in single sequence prediction than many traditional, computationally intensive distance measures.

现有的时间序列相似性度量由于其较高的计算复杂度，往往难以应用于大规模数据集。一些追求线性复杂性的解决方案通常以牺牲对序列动态的细粒度分析为代价，导致在复杂场景中的判别能力不足。在本文中，我们提出了一种多特征融合算法，该算法可以在保持线性复杂性的同时实现序列相似性的细粒度度量。首先，本文引入了一种新的子序列趋势编码机制，通过量化子序列内部的动态方向，为相似性判断提供了一个超越传统结构特征的新视角。其次，该算法从复杂性和趋势两个角度对候选子序列进行综合评价，并通过两种特征的加权融合形成更加鲁棒的距离度量，从而有效减少单一视角可能造成的误判。在70个UCR基准数据集上的实验结果验证了我们的方法，该方法不仅在17种最先进的算法中实现了分类精度的平均排名，而且还展示了卓越的效率，证明在单序列预测中比许多传统的计算密集型距离度量快了几个数量级。

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引用次数: 0

Blockchain-based federated learning with metric and imbalanced learning for visual classification 基于区块链的基于度量和不平衡学习的视觉分类联合学习

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

Computers & Electrical Engineering

Pub Date : 2026-01-11 DOI: 10.1016/j.compeleceng.2026.110961

Fei Wu , Jiahuan Lu , Hao Jin , Yibo Song , Guangwei Gao , Xiao-Yuan Jing

Federated learning (FL) allows multiple parties to collectively train deep learning models without the need to disclose their local data. The data distributions among various parties are usually non-independently and identically distributed (non-IID), and simultaneously the class imbalance problem often exits locally and globally, which is the main challenge of FL. Although some FL works have been presented aiming to solve this issue, there still exist much room to enhance the image classification effect by using deep learning models. In addition, under the non-IID setting, how to ensure the security of FL methods against the attack of malicious clients or central servers has not been well researched. We develop a novel decentralized FL approach in this paper, namely Blockchain-based Federated learning with Metric and Imbalanced Learning (BFMIL). The triplet loss is introduced to promote the consistency of feature representations between the client model and server model. To address the class imbalance problem, a cost-sensitive semantic discrimination loss is designed to fully explore the discriminative information, and data in each party is divided into the majority classes and the minority classes for unequal training. To reduce malicious attack, we utilize the blockchain to store the local update and the global model, and a novel voting mechanism is used to select parties with better model parameters for aggregation in each round of FL. The effectiveness of BFMIL is demonstrated by experiments conducted on four imbalanced datasets.

联邦学习（FL）允许多方共同训练深度学习模型，而无需公开其本地数据。各方之间的数据分布通常是非独立同分布（non- independent and identity distribution, non-IID），同时局部和全局往往存在类不平衡问题，这是人工智能面临的主要挑战。尽管已经有一些针对这一问题的人工智能作品出现，但利用深度学习模型来增强图像分类效果仍有很大的空间。此外，在非iid设置下，如何保证FL方法不受恶意客户端或中央服务器攻击的安全性还没有得到很好的研究。我们在本文中开发了一种新的分散FL方法，即基于区块链的联邦学习与度量和不平衡学习（BFMIL）。为了提高客户端模型和服务器模型之间特征表示的一致性，引入了三元丢失。为了解决类不平衡问题，设计了一个代价敏感的语义歧视损失来充分挖掘歧视信息，并将每一方的数据分成多数类和少数类进行不平等训练。为了减少恶意攻击，我们利用区块链来存储本地更新和全局模型，并使用一种新的投票机制来选择具有更好模型参数的各方在每轮FL中进行聚合。通过在四个不平衡数据集上的实验证明了BFMIL的有效性。

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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 : 2026-01-10 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

A multi-stage framework for scalable and context-aware intrusion detection in IoT-cloud systems using deep latent modeling and graph-based attack classification 使用深度潜在建模和基于图的攻击分类，用于物联网云系统中可扩展和上下文感知入侵检测的多阶段框架

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

Computers & Electrical Engineering

Pub Date : 2026-01-10 DOI: 10.1016/j.compeleceng.2026.110949

Rajakumar Ponnumani , Nisha Vasudeva , Thenmozhi Elumalai , Prabu Kaliyaperumal , Balamurugan Balusamy , Francesco Benedetto

The rapid proliferation of Internet of Things (IoT) devices in cloud environments has led to an expanded attack surface and increased susceptibility to diverse and evolving cyber threats. This study proposes a robust, multi-stage hybrid intrusion detection framework designed to address the challenges of high-dimensional data, class imbalance, and dynamic traffic in IoT ecosystems. The framework integrates Variational AutoEncoder (VAE) for latent feature compression, Isolation Forest (IF) for unsupervised anomaly detection, and Graph Attention Network (GAT) for relational modeling and multi-class classification. The CIC IoT-DIAD 2024 dataset is utilized to evaluate performance across multiple attack categories. The VAE extracts compact latent representations, enabling effective anomaly detection through IF. Detected anomalies are then structured into graph topologies, and classified by GAT based on node-level features and inter-node relations. Experimental results demonstrate superior detection performance with an overall accuracy of 99.08% and an F1-score of 98.03%, outperforming traditional and deep learning baselines. The proposed system exhibits strong scalability, generalization, and adaptability to dynamic IoT-cloud threat landscapes. Furthermore, its graph-based reasoning enhances interpretability and supports actionable insights for real-time threat response. Overall, this framework establishes a practical pathway toward intelligent, adaptive, and interpretable intrusion diagnosis in next-generation IoT-cloud ecosystems.

物联网（IoT）设备在云环境中的快速扩散导致了攻击面的扩大，并增加了对各种不断发展的网络威胁的敏感性。本研究提出了一种鲁棒的多阶段混合入侵检测框架，旨在解决物联网生态系统中高维数据、类别不平衡和动态流量的挑战。该框架集成了用于潜在特征压缩的变分自编码器（VAE）、用于无监督异常检测的隔离森林（IF）和用于关系建模和多类分类的图注意网络（GAT）。CIC IoT-DIAD 2024数据集用于评估多个攻击类别的性能。VAE提取紧凑的潜在表示，通过中频实现有效的异常检测。然后将检测到的异常结构成图拓扑，并根据节点级特征和节点间关系使用GAT进行分类。实验结果表明，该方法具有优异的检测性能，总体准确率为99.08%，f1分数为98.03%，优于传统和深度学习基线。该系统具有很强的可扩展性、通用性和对动态物联网云威胁环境的适应性。此外，其基于图的推理增强了可解释性，并支持实时威胁响应的可操作见解。总体而言，该框架为下一代物联网云生态系统中的智能、自适应和可解释入侵诊断建立了一条实用途径。

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