Computers & Electrical Engineering最新文献_第6页

A secure expert system framework for private function evaluation using functional encryption and multi-party computation 基于功能加密和多方计算的私有功能评估安全专家系统框架

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.2025.110930

Rahat Naz , Jaydeep Howlader , Shahnawaz Ahmad

Cloud systems and edge-based systems have an increasing appetite for privacy-preserving computation over distributed sensitive data. Most existing cryptographic solutions don't perform well when executing complex inference tasks while hiding both the input data and the logic of the functions. This can be a serious shortcoming in particular areas, such as healthcare analytics and financial modeling, where data privacy and model protections are paramount. Although secure multiparty computation (SMPC) and functional encryption (FE) hold promise individually, current implementations are often either not scalable or not auditable from end to end in adversarial models. This work presents a hybrid framework that fuses FE with SMPC to enable private function evaluation (PFE) in decentralized environments. The architecture supports encrypted expert inference, leveraging a trust-weighted cryptographic consensus layer, dynamic key management, and function-specific policy enforcement. An adaptive fusion of secure execution and traceable audit logging ensures both privacy and compliance without sacrificing computational tractability. Experimental validation demonstrates that our system reduces decision latency by up to 18 % over standard FE baselines and improves leakage resistance under semi-honest and collusion-based attacks by 23 %, with auditability scores reaching 87 % in real-world simulation settings. By enabling the execution of confidential functions with built-in explainability and regulatory transparency, the proposed system lays the foundation for secure AI-as-a-service platforms. Its compatibility with edge deployments and extensibility toward zero-knowledge and post-quantum cryptography position it as a robust candidate for the next generation of trust-aware decentralized computation.

云系统和基于边缘的系统对分布式敏感数据的隐私保护计算的需求越来越大。大多数现有的加密解决方案在执行复杂的推理任务时都不能很好地执行，同时隐藏输入数据和函数的逻辑。在某些领域，这可能是一个严重的缺点，例如医疗保健分析和财务建模，在这些领域，数据隐私和模型保护至关重要。尽管安全多方计算（SMPC）和功能加密（FE）各自都有希望，但在对抗性模型中，当前的实现通常要么不可扩展，要么不可从端到端进行审计。这项工作提出了一个混合框架，将FE与SMPC融合在一起，在分散的环境中实现私有功能评估（PFE）。该体系结构支持加密专家推理，利用信任加权的加密共识层、动态密钥管理和特定于功能的策略实施。安全执行和可跟踪审计日志的自适应融合在不牺牲计算可跟踪性的情况下确保了隐私和遵从性。实验验证表明，我们的系统比标准FE基线减少了18%的决策延迟，并在半诚实和基于串通的攻击下提高了23%的泄漏阻力，在真实世界的模拟设置中可审计性得分达到87%。通过使机密功能的执行具有内置的可解释性和监管透明度，拟议的系统为安全的ai即服务平台奠定了基础。它与边缘部署的兼容性以及对零知识和后量子密码学的可扩展性使其成为下一代信任感知分散计算的健壮候选者。

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引用次数: 0

A hybrid reinforcement learning framework for adaptive multi-horizon electricity load forecasting: The DWRNet approach 自适应多视界电力负荷预测的混合强化学习框架：dwnet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: 2026-01-02 DOI: 10.1016/j.compeleceng.2025.110926

Muhammad Farhan Khan , Sile Hu , Yuan Gao , Yu Guo , Yuan Wang , Maryam Saeed , Yucan Zhao , Jiaqiang Yang

Accurate and adaptive multi-horizon electricity load forecasting is essential for secure operation of modern power systems and for the integration of variable renewable generation. This paper proposes DWRNet, a Dynamic Weighted Residual Network that combines statistical decomposition, deep residual learning, and reinforcement learning (RL)-based adaptive fusion. A Fruit Fly Optimization-tuned Holt-Winters model first extracts the dominant seasonal-trend component, while a Long Short-Term Memory (LSTM) network learns the nonlinear residual structure. A continuous-action policy-gradient controller then produces horizon-dependent convex weights that balance the statistical and neural forecasts, enabling the ensemble to adapt to changing load regimes while remaining lightweight enough for EMS/SCADA deployment. DWRNet is evaluated on four years of hourly load data from two structurally different power systems (Inner Mongolia, China and Germany) over 24 h, 168 h, and 720 h horizons, and compared against strong baselines including SVR, LSTM, GRU, CNN, CNN-LSTM, and recent Transformer-based models (Informer, FEDformer) under a common rolling-origin protocol. Across both regions and all horizons, DWRNet consistently achieves the best or near-best MAE, RMSE, sMAPE and R² values, with particularly notable gains on weekly and monthly forecasts. Robustness is assessed through cross-validation with varying training fractions, bootstrap-based confidence intervals, ablation studies, and residual diagnostics, which collectively indicate that the improvements are stable and not attributable to overfitting. A complexity analysis and runtime benchmarks further show that the RL-based blending stage adds only modest offline training cost and negligible inference overhead. DWRNet offers a practical and scalable solution for real-time energy forecasting, with strong potential for use in energy management systems, dispatch operations, and smart grid planning.

准确、自适应的多水平负荷预测对于现代电力系统的安全运行和可变可再生能源发电的整合至关重要。本文提出了一种动态加权残差网络DWRNet，它结合了统计分解、深度残差学习和基于强化学习（RL）的自适应融合。果蝇优化的Holt-Winters模型首先提取主要的季节趋势成分，而长短期记忆（LSTM）网络学习非线性剩余结构。然后，连续动作策略梯度控制器产生与水平相关的凸权值，以平衡统计和神经预测，使集成能够适应不断变化的负载状态，同时保持足够轻量的EMS/SCADA部署。DWRNet基于两个结构不同的电力系统（内蒙古、中国和德国）在24小时、168小时和720小时期间的4年每小时负荷数据进行评估，并与强大的基线进行比较，包括SVR、LSTM、GRU、CNN、CNN-LSTM和最近基于变压器的模型（Informer、FEDformer）。在这两个地区和所有范围内，dwnet始终能够实现最佳或接近最佳的MAE、RMSE、sMAPE和R²值，特别是在每周和每月的预测中获得显著的收益。鲁棒性通过不同训练分数、基于自启动的置信区间、消融研究和剩余诊断的交叉验证来评估，这些共同表明改进是稳定的，而不是归因于过拟合。复杂度分析和运行时基准进一步表明，基于强化学习的混合阶段只增加了适度的离线训练成本和可忽略的推理开销。DWRNet为实时能源预测提供了实用且可扩展的解决方案，在能源管理系统、调度操作和智能电网规划中具有强大的应用潜力。

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引用次数: 0

Hybrid deep learning based load forecasting and AI-driven energy management for grid-connected multi-microgrids 基于深度学习的并网多微电网负荷预测与人工智能驱动的混合能源管理

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: 2025-12-23 DOI: 10.1016/j.compeleceng.2025.110915

Adil Zohaib , Faraz Akram , Sohail Khalid , Hamid Nawaz , Mujeeb Ur Rehman

Microgrids offer a promising paradigm for sustainable and decentralized energy management; however, they face operational challenges due to fluctuating load profiles and the intermittency of renewable energy sources. This paper proposes a two-phase framework to address these challenges through accurate short-term load forecasting (STLF) and an advanced energy management system (EMS) for grid-connected multi-microgrids. In Phase I, STLF was performed using residential metering infrastructure data from the PRECON dataset. A hybrid deep learning model, Prophet-Long Short-Term Memory (PLSTM), was developed and outperformed benchmarks, including LSTM, XGBoost, SARIMA, and Prophet, reducing the error by 12%–18%. In Phase II, an AI-enhanced EMS is introduced, integrating PLSTM-based load forecasting, ANN-based photovoltaic generation prediction, adaptive self-learning weights, and deep Q-learning for forecast margin tuning. This robust hierarchical model predictive control strategy eliminates reliance on demand-side management and preserves user comfort. The simulation results demonstrate that the proposed framework outperforms conventional baseline EMS methods in terms of energy efficiency, reducing grid imports by 28%, adaptability with average SoC tracking improvement of 15%, and resilience indicated by a 22% increase in battery cycle longevity under uncertainties in load consumption and solar energy generation, offering a scalable solution for microgrid deployment in dynamic environments.

微电网为可持续和分散的能源管理提供了一个有希望的范例；然而，由于负荷波动和可再生能源的间歇性，它们面临着运营挑战。本文提出了一个两阶段框架，通过准确的短期负荷预测（STLF）和先进的并网多微电网能源管理系统（EMS）来解决这些挑战。在第一阶段，STLF使用来自PRECON数据集的住宅计量基础设施数据进行。开发了一种混合深度学习模型，Prophet- long - Short-Term Memory (PLSTM)，并优于LSTM、XGBoost、SARIMA和Prophet等基准，将误差降低了12%-18%。在第二阶段，引入了人工智能增强的EMS，集成了基于plstm的负荷预测、基于人工神经网络的光伏发电预测、自适应自学习权值以及用于预测裕度调整的深度q学习。这种鲁棒的分层模型预测控制策略消除了对需求侧管理的依赖，并保持了用户的舒适性。仿真结果表明，该框架在能效方面优于传统的基线EMS方法，减少了28%的电网进口，平均SoC跟踪提高了15%的适应性，在负载消耗和太阳能发电不确定的情况下，电池循环寿命增加了22%，为动态环境下的微电网部署提供了可扩展的解决方案。

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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 : 2026-03-01 Epub 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

Hierarchical mobile-dense convolutional architecture for tampered image detection using focal optimization with quantized edge TPU deployment 采用量化边缘TPU部署的焦点优化的分层移动密集卷积结构篡改图像检测

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: 2026-01-16 DOI: 10.1016/j.compeleceng.2026.110979

Badam Shanmukha Venkata Vinayak , Rama Muni Reddy Yanamala , Rayappa David Amar Raj , Archana Pallakonda

The availability of powerful digital editing tools has made image tampering increasingly sophisticated, posing significant challenges to journalism, forensics, and social media authenticity. To address the limitations of conventional and transformer-based forgery detection approaches – which often suffer from feature redundancy, compressibility instability, and high computational demands – this study introduces a deep learning architecture for tampered image detection. The model integrates a MobileNetV2-based encoder for compact spatial feature extraction, multi-scale hierarchical feature reuse blocks inspired by DenseNet, and a U-Net-type decoder for precise forgery localization. Class imbalance is mitigated using an enhanced binary classifier with focal loss. The entire model is quantized and deployed on a Google Coral Edge TPU, achieving real-time classification performance (approximately 135 ms per image) in low-power, resource-limited environments. The model is trained and tested on four benchmark forgery datasets – CASIA v1, Columbia, MICC-F2000, and Defacto-Splicing – and demonstrates excellent results: AUC = 1.00 and accuracy = 99% on Defacto, AUC = 0.967 and F1-score = 0.915 on Columbia, and strong performance on both high-resolution (MICC-F2000) and compressed (CASIA v1) datasets. Comparative analyses show that the proposed approach outperforms recent CNN- and Transformer-based methods while using only 5.7 million parameters, confirming its efficacy, scalability, and suitability for embedded AI systems. Thus, the proposed method represents a lightweight, hardware-deployable, and interpretable solution for robust image forgery detection.

强大的数字编辑工具的可用性使得图像篡改变得越来越复杂，对新闻业、法医学和社交媒体的真实性构成了重大挑战。为了解决传统的和基于变压器的伪造检测方法的局限性——通常存在特征冗余、可压缩性不稳定和高计算需求——本研究引入了一种用于篡改图像检测的深度学习架构。该模型集成了基于mobilenetv2的编码器，用于紧凑的空间特征提取，受DenseNet启发的多尺度分层特征重用块，以及u - net类型的解码器，用于精确的伪造定位。类失衡是减轻使用增强的二元分类器与焦点损失。整个模型被量化并部署在谷歌Coral Edge TPU上，在低功耗、资源有限的环境中实现实时分类性能（每张图像约135毫秒）。该模型在四个基准伪造数据集（CASIA v1、Columbia、mic - f2000和Defacto- splicing）上进行了训练和测试，并展示了出色的结果：Defacto上的AUC = 1.00，准确率= 99%，Columbia上的AUC = 0.967, F1-score = 0.915，在高分辨率（mic - f2000）和压缩（CASIA v1）数据集上都表现出色。对比分析表明，所提出的方法在仅使用570万个参数的情况下优于最近基于CNN和transformer的方法，证实了其有效性、可扩展性和对嵌入式人工智能系统的适用性。因此，所提出的方法代表了一种轻量级、硬件可部署和可解释的鲁棒图像伪造检测解决方案。

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引用次数: 0

Single-phase switched-capacitor based common ground five-level inverter for grid-tied PV systems with double gain 双增益并网光伏系统单相开关电容共地五电平逆变器

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: 2026-01-06 DOI: 10.1016/j.compeleceng.2025.110928

Katroth Kalyan Singh, Kirubakaran Annamalai

This article proposes a single-phase transformerless inverter for grid-tied PV installations. At the output stage, the proposed inverter can produce five levels of voltage. It features two electrolytic switching capacitors (SCs), six power switches, and two power diodes. This architecture is lighter and less expensive due to the usage of fewer power electronic components. Because the negative DC line of the suggested inverter is directly connected to the grid neutral in PV applications, leakage current is completely minimized. Another advantage of this design is that it may easily double the output voltage without the need for a transformer or inductor. Self-balancing is achieved by symmetrically charging and discharging the SCs in parallel and in series with the input voltage over time. Therefore, a complex control technique to balance the SCs is no longer necessary with the proposed inverter. The design specifications of the proposed inverter are provided. To illustrate the benefits of the proposed inverter, including the reduction of total standing voltage and cost function, a quantitative comparison analysis with similar five-level topologies is also presented. An experimental prototype of a 1 kW grid-tied system is used to validate the topology and demonstrate the capabilities of the proposed inverter with a closed-loop PR controller. Moreover, the system dynamics are tested under different loading conditions and input voltage variations.

本文提出了一种用于并网光伏装置的单相无变压器逆变器。在输出阶段，所提出的逆变器可以产生五个等级的电压。它具有两个电解开关电容器（SCs），六个功率开关和两个功率二极管。由于使用更少的电力电子元件，这种架构更轻，更便宜。由于建议的逆变器的负直流线路直接连接到光伏应用中的电网中性点，因此泄漏电流完全最小化。这种设计的另一个优点是，它可以很容易地加倍输出电压，而不需要变压器或电感。自平衡是通过与输入电压随时间平行或串联对称充电和放电来实现的。因此，对于所提出的逆变器，不再需要复杂的控制技术来平衡sc。给出了逆变器的设计参数。为了说明所提出的逆变器的优点，包括降低总驻电压和成本函数，还提供了与类似五级拓扑的定量比较分析。一个1千瓦并网系统的实验原型被用来验证拓扑结构，并展示了带闭环PR控制器的逆变器的能力。并对系统在不同负载条件和输入电压变化下的动力学特性进行了测试。

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引用次数: 0

Cyber risk quantification for adversarial machine learning attacks 对抗性机器学习攻击的网络风险量化

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: 2026-01-09 DOI: 10.1016/j.compeleceng.2026.110964

Jasmita Malik, Raja Muthalagu, Pranav M. Pawar, Mithun Mukherjee

Adversarial machine learning (AML) attacks including evasion, poisoning, and privacy-targeting techniques represent a new class of evolving threats to AI systems. However, traditional cyber risk quantification approaches struggle to capture the uncertainty and impact of such dynamic threats. This study introduces a novel framework to quantify cyber risk exposure and business impact stemming from new-age AML attacks. Leveraging Monte Carlo simulations, the framework models probabilistic loss distributions based on attack likelihoods and impact ranges. Applied to a ransomware attack scenario on a machine learning system, the framework estimates an Annualized Loss Expectancy of approximately $1.6 million to an organization, revealing the potential for unexpected heavy-tail, high-cost outcomes. The framework is further validated across diverse adversarial scenarios, including evasion, poisoning, and privacy attacks. The results provide decision-makers with a structured way to assess control effectiveness and prioritize cybersecurity investments using quantitative metrics. This work bridges the gap between technical threat intelligence and strategic cybersecurity investment financial planning, offering a practical path toward resilient and secure deployment of AI systems in organizations.

对抗性机器学习（AML）攻击，包括逃避、中毒和隐私定位技术，代表了人工智能系统面临的一类不断发展的新威胁。然而，传统的网络风险量化方法难以捕捉这种动态威胁的不确定性和影响。本研究引入了一个新的框架来量化新时代“反洗钱”攻击所带来的网络风险暴露和业务影响。利用蒙特卡罗模拟，该框架基于攻击可能性和影响范围对概率损失分布进行建模。应用于机器学习系统上的勒索软件攻击场景，该框架估计一个组织的年预期损失约为160万美元，揭示了意想不到的重尾、高成本结果的可能性。该框架在不同的对抗性场景中得到进一步验证，包括逃避、中毒和隐私攻击。研究结果为决策者提供了一种结构化的方法来评估控制效果，并使用定量指标确定网络安全投资的优先级。这项工作弥合了技术威胁情报和战略网络安全投资财务规划之间的差距，为组织中弹性和安全部署人工智能系统提供了切实可行的途径。

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引用次数: 0

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 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

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

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.

本文提出了一个多智能体深度强化学习（MARL）框架，用于包括光伏、风力涡轮机和储能系统在内的直流微电网（DC MG）的分布式能源管理，其主要目标是保持直流链路电压稳定。分散控制体系结构采用本地电压测量作为代理状态输入，并使用Deep Q-Networks来估计单个动作值函数。研究了三种算法方法：独立DQN (IDQN)，价值分解网络（VDN）和QMIX，每种算法都使用多层感知器（MLP）和递归神经网络（RNN）架构进行评估。自定义奖励功能集成了电压偏差惩罚、功率平衡约束和电池循环成本，以实现高可再生能源渗透率和高效的储能调度。案例研究验证了框架在不同条件下的性能，包括变量生成和需求、网络延迟、虚假数据注入攻击、接地故障和即插即用拓扑变化。结果揭示了场景相关的性能特征：基于RNN的VDN在正常运行下实现了卓越的电压调节，IDQN在网络攻击中表现出强大的奖励优化，而基于RNN的QMIX在虚假数据注入的对抗场景中表现出色，在即插即用事件中表现出最快的瞬态响应。计算分析确定了与体系结构相关的扩展权衡，QMIX需要更多的计算需求和集中的协调开销，而IDQN的分布式体系结构和较低的资源消耗为多代理扩展提供了更好的可伸缩性。该框架通过场景化算法的选择，证明了基于marl的分布式控制在直流电网弹性能量管理中的实际可行性。

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引用次数: 0

An unsupervised domain adaptation approach for remote sensing scene classification using adaptive incremental density-based clustering and multi-objective optimization 基于自适应增量密度聚类和多目标优化的遥感场景无监督域自适应分类方法

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: 2025-12-23 DOI: 10.1016/j.compeleceng.2025.110908

Binu Jose A. , Pranesh Das , Ebrahim Ghaderpour , Paolo Mazzanti

Unsupervised Domain Adaptation (UDA) is the process of learning knowledge from a labelled source domain to an unlabelled target domain, particularly in the context of remote sensing scene classification. The primary challenge in this process is the substantial cost associated with labelling and the significant discrepancies between domains. However, existing UDA methods degrade under severe domain shift and scene diversity, yielding noisy pseudo-labels and unstable target structure discovery. To address these issues, a novel UDA framework is proposed. The main focus of the framework is to develop a mapping using clustering-based pseudo-labelling that can provide a reliable and interpretable pseudo-labels to the target dataset. A deep learning-based Pareto font-driven feature-selection module is also added to fine-tune the source and target features, thereby significantly improving the performance of the scene classification model. An adaptive density-based clustering method with a two-step neural network in the clustering module is utilized to determine whether adjacent clusters should be merged, thereby maintaining clear class boundaries. To reduce the pseudo-label noise, an uncertainty-aware soft pseudo-labelling approach is implemented, based on a dynamic confidence threshold. The framework is evaluated on four remote-sensing datasets namely AID (A), NWPU (N), RSSCN7 (R), and UC Merced (U) across various domain-adaptation tasks (A

\to

R, R

\to

A, A

\to

U, U

\to

A, R

\to

U, U

\to

R, N

\to

R, and R

\to

N). The proposed approach achieves accuracy improvements of 5.80%, 1.8%, 2.79%, 7.56%, 3.50%, 7.39%, 5.35%, and 3.12% over some of the baseline methods. These results show the superiority of the proposed approach in managing domain shifts, reducing pseudo-label noise, and improving target recognition without the need for labelled target data. The source code is available at https://github.com/BinuJoseA/UDA.

无监督域自适应（UDA）是一种将知识从已标记的源域学习到未标记的目标域的过程，特别是在遥感场景分类的背景下。这一过程的主要挑战是与标签相关的大量成本和域之间的显著差异。然而，现有的UDA方法在严重的域漂移和场景多样性下会退化，产生有噪声的伪标签和不稳定的目标结构发现。为了解决这些问题，提出了一种新的UDA框架。该框架的主要重点是使用基于聚类的伪标签开发映射，该映射可以为目标数据集提供可靠且可解释的伪标签。此外，还增加了基于深度学习的Pareto字体驱动特征选择模块，对源特征和目标特征进行微调，从而显著提高了场景分类模型的性能。采用基于自适应密度的聚类方法，在聚类模块中引入两步神经网络来确定相邻聚类是否合并，从而保持清晰的类边界。为了降低伪标签噪声，实现了一种基于动态置信度阈值的不确定性感知软伪标签方法。在AID (A)、NWPU (N)、RSSCN7 (R)和UC Merced (U) 4个遥感数据集上，对该框架进行了不同领域自适应任务（A→R、R→A、A→U、U→A、R→U、U→R、N→R和R→N）的评估。与一些基线方法相比，该方法的准确率分别提高了5.80%、1.8%、2.79%、7.56%、3.50%、7.39%、5.35%和3.12%。这些结果表明，该方法在不需要标记目标数据的情况下，在管理域偏移、减少伪标签噪声和提高目标识别方面具有优越性。源代码可从https://github.com/BinuJoseA/UDA获得。

{"title":"An unsupervised domain adaptation approach for remote sensing scene classification using adaptive incremental density-based clustering and multi-objective optimization","authors":"Binu Jose A. , Pranesh Das , Ebrahim Ghaderpour , Paolo Mazzanti","doi":"10.1016/j.compeleceng.2025.110908","DOIUrl":"10.1016/j.compeleceng.2025.110908","url":null,"abstract":"<div><div>Unsupervised Domain Adaptation (UDA) is the process of learning knowledge from a labelled source domain to an unlabelled target domain, particularly in the context of remote sensing scene classification. The primary challenge in this process is the substantial cost associated with labelling and the significant discrepancies between domains. However, existing UDA methods degrade under severe domain shift and scene diversity, yielding noisy pseudo-labels and unstable target structure discovery. To address these issues, a novel UDA framework is proposed. The main focus of the framework is to develop a mapping using clustering-based pseudo-labelling that can provide a reliable and interpretable pseudo-labels to the target dataset. A deep learning-based Pareto font-driven feature-selection module is also added to fine-tune the source and target features, thereby significantly improving the performance of the scene classification model. An adaptive density-based clustering method with a two-step neural network in the clustering module is utilized to determine whether adjacent clusters should be merged, thereby maintaining clear class boundaries. To reduce the pseudo-label noise, an uncertainty-aware soft pseudo-labelling approach is implemented, based on a dynamic confidence threshold. The framework is evaluated on four remote-sensing datasets namely AID (A), NWPU (N), RSSCN7 (R), and UC Merced (U) across various domain-adaptation tasks (A <span><math><mo>→</mo></math></span> R, R <span><math><mo>→</mo></math></span> A, A <span><math><mo>→</mo></math></span> U, U <span><math><mo>→</mo></math></span> A, R <span><math><mo>→</mo></math></span> U, U <span><math><mo>→</mo></math></span> R, N <span><math><mo>→</mo></math></span> R, and R <span><math><mo>→</mo></math></span> N). The proposed approach achieves accuracy improvements of 5.80%, 1.8%, 2.79%, 7.56%, 3.50%, 7.39%, 5.35%, and 3.12% over some of the baseline methods. These results show the superiority of the proposed approach in managing domain shifts, reducing pseudo-label noise, and improving target recognition without the need for labelled target data. The source code is available at <span><span>https://github.com/BinuJoseA/UDA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110908"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842706","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 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

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

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.

在本研究中，提出了一种新的用于二维FIR滤波器的滤波器组（FB）架构，并利用对称处理、并行性和分布式算法（DA）思想在VLSI设计中实现。这项工作的动机是需要硬件高效的2D FIR滤波器架构，以降低实时图像处理应用中的计算复杂性、功耗（PC）和资源使用。为了提高吞吐量和减少乘法器的数量，设计中引入了并行处理，并在滤波器系数中引入了对称性。采用双端口查找表（Dual port - lookup Table, DP-LUT）代替剩余的乘法器来减少面积和功耗。考虑了四种类型的对称性，并使用所提出的数据处理方法探索和实现了每种体系结构。最后，通过考虑公共存储模块和控制逻辑，将所有这些滤波器结构集成在一起。FB设计使得内存重用和共享成为可能，它还允许并行处理。建议的FB设计在内存和处理能力方面具有较低的资源需求。对现场可编程门阵列（FPGA）目标器件的硬件利用率进行了综合评价。之后，使用Cadence的Genus工具进行ASIC设计，在45 nm CMOS技术中进行设计合成。从面积、延迟和PC报告等方面分析了现有的二维FIR滤波器设计和传统的基于乘法器的滤波器架构。与现有设计相比，所提出的FB架构实现了高达98.04%的ADP降低和高达64.51%的PDP降低，突出了其在面积和功耗优化方面的效率。然后提供建议的工作布局，包括用于确定地点和路线的Innovus工具。

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引用次数: 0