Computers & Electrical Engineering最新文献

英文中文

A blockchain-based publicly verifiable data access control scheme without pairing 基于区块链的无配对公开可验证数据访问控制方案

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-03 DOI: 10.1016/j.compeleceng.2024.109724

The stability of Web 3.0 depends on the existence of a robust decentralized storage infrastructure. One challenge associated with decentralized storage is access control in the context of data outsourcing. Many solutions to this problem have been proposed, but there still exist limitations. For instance, the implementation of an access control algorithm may consume a considerable amount of computing resources, necessitate reliance on a centralized storage service provider, and fail to effectively arbitrate against malicious behaviors. To address the these problems, we innovatively propose a Publicly Verifiable Data Access Control (PVDAC) algorithm without bilinear pairing to reduce the computational overhead and provide an efficient arbitration method. We extend the blockchain with a layer two network to reduce the token consumption associated with on-chain operations. We analyzed the security of the scheme, proved that it meets the CCA security of the ciphertext, and conducted comprehensive experiments to evaluate its performance. The results show that the proposed PVDAC scheme achieves low computational consumption by costing only 1% to 10% of the token consumption for interaction with the main chain, and meanwhile, supports public verification.

Web 3.0 的稳定性取决于是否存在强大的分散式存储基础设施。与分散式存储相关的一个挑战是数据外包背景下的访问控制。针对这一问题已经提出了许多解决方案，但仍然存在局限性。例如，访问控制算法的实施可能会消耗大量计算资源，必须依赖集中式存储服务提供商，而且无法有效地对恶意行为进行仲裁。为了解决这些问题，我们创新性地提出了一种无双线性配对的公开可验证数据访问控制（PVDAC）算法，以减少计算开销并提供高效的仲裁方法。我们用第二层网络扩展了区块链，以减少与链上操作相关的代币消耗。我们分析了该方案的安全性，证明它符合密文的 CCA 安全性，并进行了全面的实验来评估其性能。结果表明，所提出的 PVDAC 方案实现了低计算消耗，与主链交互的代币消耗仅为 1%-10%，同时还支持公开验证。

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引用次数: 0

Exploiting self-evolutionary strategies of components for Dynamic Heterogeneous Redundancy 利用组件的自我进化策略实现动态异构冗余

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-03 DOI: 10.1016/j.compeleceng.2024.109756

The network security situation is increasingly serious. Traditional security equipment like firewalls and intrusion detection systems cannot prevent unknown risks due to their passive nature. Dynamic Heterogeneous Redundancy (DHR) actively defends by switching the attack surface and confusing attackers, becoming essential in modern network defense. However, existing DHR approaches based on scheduling algorithms struggle under high-intensity attacks due to resource limitations. To overcome this weakness, we propose a Genetic Algorithm and Particle Swarm Optimization (GAPSO), a novel DHR architecture. GAPSO provides real-time security awareness of host components, maps potential attacker paths, and calculates the risk probability of each component. High-risk components evolve into others in the pool during attacks. Experiments show that GAPSO significantly reduces system risk compared to scheduling-based DHR and effectively delays the hacker’s attack lifecycle. Additionally, we developed a prototype system and evaluated it in a real network environment, obtaining positive results.

网络安全形势日益严峻。防火墙和入侵检测系统等传统安全设备由于其被动性，无法防范未知风险。动态异构冗余（DHR）通过切换攻击面、迷惑攻击者来主动防御，成为现代网络防御的重要手段。然而，由于资源限制，现有的基于调度算法的 DHR 方法在高强度攻击下难以奏效。为了克服这一弱点，我们提出了遗传算法和粒子群优化（GAPSO）这一新型 DHR 架构。GAPSO 提供主机组件的实时安全意识，映射潜在的攻击路径，并计算每个组件的风险概率。在攻击过程中，高风险组件会演化成池中的其他组件。实验表明，与基于调度的 DHR 相比，GAPSO 能显著降低系统风险，并有效延缓黑客的攻击周期。此外，我们还开发了一个原型系统，并在真实网络环境中进行了评估，取得了积极的成果。

引用次数: 0

Weighted common spatial pattern based adaptation regularization for multi-source EEG time series 基于加权共同空间模式的多源脑电图时间序列适应正则化

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-03 DOI: 10.1016/j.compeleceng.2024.109680

Brain–computer interfaces (BCIs) have garnered significant attention due to their ability to actualize previously fantastical concepts through enabling direct communication between the brain and peripherals. However, electroencephalogram (EEG) time series are inherently vulnerable and subject-specific, necessitating a calibration process that is both intricate and time-consuming for different subjects. To address this issue, we present a feature fusion based adaptation regularization algorithm named as weighted common spatial pattern feature-based adaptation regularization (WCSPAR) to improve the classification performance for multi-source motor imagery EEG signals. Specifically, to leverage information from source domains, we refine the method for constructing covariance matrices within the common spatial pattern framework by incorporating information from source domains and introducing a classifier to predict pseudo labels in target domain. Furthermore, to fully exploit the inter-domain information, we present a similarity estimation approach utilizing Riemannian distance to quantify different contributions from different source domains. Additionally, we devise an uncertainty-free classifier based on adaptation regularization transfer learning to prevent negative transfer. To evaluate the performance of WCSPAR, we conduct comparative experiments involving eight benchmark algorithms. Experimental results demonstrate the effectiveness of WCSPAR, which achieved the highest average accuracy of 80.75% when compared with other state-of-the-art algorithms.

脑机接口（BCIs）通过实现大脑与外围设备之间的直接通信，能够将以前天马行空的概念变为现实，因此备受关注。然而，脑电图（EEG）时间序列本身具有脆弱性和特定对象性，因此需要针对不同对象进行复杂而耗时的校准过程。为了解决这个问题，我们提出了一种基于特征融合的适应正则化算法，称为基于加权共同空间模式特征的适应正则化（WCSPAR），以提高多源运动图像脑电信号的分类性能。具体来说，为了充分利用源域的信息，我们在共同空间模式框架内改进了构建协方差矩阵的方法，纳入了源域的信息，并引入了一个分类器来预测目标域的伪标签。此外，为了充分利用域间信息，我们提出了一种利用黎曼距离的相似性估计方法，以量化来自不同源域的不同贡献。此外，我们还设计了一种基于适应正则化迁移学习的无不确定性分类器，以防止负迁移。为了评估 WCSPAR 的性能，我们进行了八种基准算法的对比实验。实验结果证明了 WCSPAR 的有效性，与其他最先进的算法相比，WCSPAR 达到了最高的平均准确率 80.75%。

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引用次数: 0

An evaluation framework integrating sensing and transmission in cognitive radio networks 认知无线电网络中整合传感与传输的评估框架

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-02 DOI: 10.1016/j.compeleceng.2024.109708

Cognitive radio technology provides a potential solution to the growing demand for radio spectrum resources. However, evaluating the differences between channel theoretical models in various application scenarios is a complex and critical challenge in cognitive radio technology. To address this issue, the authors propose a framework for evaluating performance that integrates spectrum sensing, allocation, and data transmission, and a mathematical derivation of how the parts are connected is given. The framework is capable of analyzing the sensing and transmission performance of different fading channel models. It enables the assessment of transmission performance for each heterogeneous secondary user (SU) across various transmission environments and decision thresholds, including parameters such as throughput, queue length, and packet rejection rate. Additionally, a performance metric is proposed to measure the impact of fading channel models on the primary user (PU). The simulation results show that the quantitative performance difference between the Rayleigh channel model and its improved Nakagami channel model in different environments can be obtained using the proposed evaluation framework. The proposed framework can improve the reliability and effectiveness of implementing cognitive radio networks in cross-application scenarios.

认知无线电技术为满足日益增长的无线电频谱资源需求提供了一种潜在的解决方案。然而，评估各种应用场景中信道理论模型之间的差异是认知无线电技术面临的一项复杂而严峻的挑战。为解决这一问题，作者提出了一个集成频谱感知、分配和数据传输的性能评估框架，并给出了各部分之间如何连接的数学推导。该框架能够分析不同衰减信道模型的传感和传输性能。它能评估每个异构二次用户（SU）在不同传输环境和决策阈值下的传输性能，包括吞吐量、队列长度和数据包拒绝率等参数。此外，还提出了一个性能指标来衡量衰减信道模型对主用户（PU）的影响。仿真结果表明，利用所提出的评估框架，可以在不同环境下获得瑞利信道模型与改进的中上信道模型之间的量化性能差异。所提出的框架可以提高认知无线电网络在跨应用场景中实施的可靠性和有效性。

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引用次数: 0

Medical image encryption algorithm based on Fresnel zone formula, differential neural networks, and pixel-guided perturbation techniques 基于菲涅尔区公式、微分神经网络和像素引导扰动技术的医学图像加密算法

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-02 DOI: 10.1016/j.compeleceng.2024.109722

This paper proposes an image encryption technique that integrates deep pixel substitution, data-dependent and chaotic pixel perturbation, and differential neural networks. The pixels of the image are manipulated using a deep pixel substitution operation that is based on the Fresnel Zone equation to eliminate the correlations to the input plain image. Additionally, a perturbation process based on pixel values and chaotic noise is applied to further scramble the image. The resulting image is then subjected to a second round of deep substitution. The differential neural network generates blurring codes by incorporating plain pixel blocks and an encryption key, which are subsequently added to the processed image to produce the final ciphered image. The proposed technique’s effectiveness was evaluated on a large dataset that included both medical and non-medical images. Simulation results indicated that the proposed technique was not only efficient but also effective for both medical and non-medical images, and it outperformed state-of-the-art encryption methods in both security properties and computational efficiency.

本文提出了一种集成了深度像素置换、数据依赖性和混沌像素扰动以及差分神经网络的图像加密技术。利用基于菲涅尔区方程的深度像素置换操作对图像像素进行处理，以消除与输入纯图像的相关性。此外，还采用基于像素值和混沌噪声的扰动过程来进一步扰乱图像。随后，生成的图像将进行第二轮深度置换。差分神经网络通过结合纯像素块和加密密钥生成模糊代码，然后将其添加到处理过的图像中，生成最终的加密图像。我们在一个包含医疗和非医疗图像的大型数据集上评估了拟议技术的有效性。仿真结果表明，所提出的技术不仅高效，而且对医学和非医学图像都很有效，在安全性能和计算效率方面都优于最先进的加密方法。

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引用次数: 0

Priority-driven harmonic power sharing strategy for interlinking converters based on distributed consensus protocol 基于分布式共识协议的互联变流器优先级驱动谐波功率共享策略

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-02 DOI: 10.1016/j.compeleceng.2024.109700

This paper investigates the issue of accurate harmonic power sharing in a microgrid clusters connected with interlinking converters. A distributed consensus protocol is developed for controller to adaptively regulate the virtual impedance at harmonic frequencies. Further, a simplified formula of the harmonic apparent power is adopted to reduce the calculation burden. With the proposed methods, harmonic power can be proportionally shared among ILCs and AC sub-grid according to sub-grid priority index,which improves the higher weight sub-grid voltage quality. Thus, the microgrid clusters system flexibility can be enhanced and the knowledge of he line impedance is not required. Finally, the effectiveness of the proposed strategy is verified by hardware-in-the-loop experiments.

本文研究了与互联变流器相连的微电网集群中的精确谐波功率共享问题。为控制器开发了一种分布式共识协议，用于自适应调节谐波频率下的虚拟阻抗。此外，还采用了简化的谐波视在功率公式，以减轻计算负担。采用所提出的方法，谐波功率可根据子网优先级指数在 ILC 和交流子网之间按比例分摊，从而改善高权重子网的电压质量。因此，可以提高微电网集群系统的灵活性，而且无需了解线路阻抗。最后，通过硬件在环实验验证了所提策略的有效性。

引用次数: 0

Margin-enhanced average precision optimization for visible-infrared person re-identification 可见红外人员再识别的边际增强平均精度优化

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-02 DOI: 10.1016/j.compeleceng.2024.109751

Average precision-based loss functions are effective for training deep neural networks in ranking tasks and single-modality person re-identification. However, they have difficulty in uniformly ranking cross-modality samples due to high intra-modality similarity. To address this limitation, this paper proposes a Margin-Enhanced Average Precision (MEAP) optimization approach for cross-modality person re-identification. MEAP integrates average precision optimization with inter-class and cross-modality margin parameters. The inter-class margin improves performance by increasing class separation, while the cross-modality margin enhances performance by prioritizing cross-modality positive samples. Additionally, we propose an innovative algorithm to augment visible and infrared person images using horizontal stripes, aiming to bridge the gap between the two modalities by creating diverse and enriched training data. Experiments on two public datasets demonstrate the effectiveness of our approach in comparison to state-of-the-art methods. MEAP and horizontal stripe augmentation significantly improve accuracy and robustness in matching individuals across different modalities. The code is available at: https://github.com/NihatTekeli/meapnet

基于平均精度的损失函数对于训练排序任务和单模态人物再识别中的深度神经网络非常有效。然而，由于模态内相似性较高，它们难以对跨模态样本进行统一排序。针对这一局限性，本文提出了一种用于跨模态人物再识别的保证金增强平均精度（MEAP）优化方法。MEAP 将平均精度优化与跨类和跨模态保证金参数相结合。类间余量通过增加类间分离来提高性能，而跨模态余量则通过优先处理跨模态阳性样本来提高性能。此外，我们还提出了一种利用水平条纹增强可见光和红外人物图像的创新算法，旨在通过创建多样化和丰富的训练数据来弥合两种模态之间的差距。在两个公共数据集上进行的实验表明，与最先进的方法相比，我们的方法非常有效。MEAP 和水平条纹增强显著提高了不同模态个体匹配的准确性和稳健性。代码见：https://github.com/NihatTekeli/meapnet

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引用次数: 0

Enhancing solar irradiance forecasting for hydrogen production: The MEMD-ALO-BiLSTM hybrid machine learning model 加强制氢过程中的太阳辐照度预测：MEMD-ALO-BiLSTM 混合机器学习模型

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-02 DOI: 10.1016/j.compeleceng.2024.109747

This study focuses on an innovative hybrid machine-learning model for solar irradiance forecasting, targeting the integration of solar power into hydrogen production systems. Addressing the urgent need for sustainable energy transitions, the paper introduces the MEMD-ALO-BiLSTM model, designed to enhance solar irradiance prediction accuracy. This model uniquely combines Multivariate Empirical Mode Decomposition (MEMD), Ant Lion Optimizer (ALO), and Bidirectional Long Short-Term Memory (BiLSTM) techniques, setting a new benchmark in forecast precision across various seasonal datasets from Jiangsu Province, China. Demonstrating superior performance to traditional models, it achieves an exceptional coefficient of determination, averaging 0.99 for all seasons. Additionally, to prove the efficiency of the model three statistical tests were used, namely Wilcoxon, Friedman, and P-value. The research highlights the model's potential in optimizing photovoltaic systems and hydrogen production, thus contributing to carbon dioxide emission mitigation. Through comprehensive simulations of a residential system encompassing photovoltaic cells, compressors, and electrolyzers, the study underscores the practical feasibility and significant advancements the MEMD-ALO-BiLSTM model offers in the renewable energy sector, promoting a shift toward more reliable and efficient solar-powered hydrogen generation systems. Accordingly, the day-ahead values of photovoltaic-generated power and hydrogen production through the electrolyzer reached peak values at 1:00PM with approximately 75 kW and 1.4 kg, respectively.

本研究的重点是针对太阳能与制氢系统集成的太阳辐照度预测的创新型混合机器学习模型。针对可持续能源转型的迫切需求，本文介绍了 MEMD-ALO-BiLSTM 模型，旨在提高太阳辐照度预测的准确性。该模型独特地结合了多变量经验模式分解（MEMD）、蚁狮优化器（ALO）和双向长短期记忆（BiLSTM）技术，为中国江苏省各种季节数据集的预测精度设定了新基准。与传统模型相比，该模型表现出更优越的性能，所有季节的平均决定系数均达到 0.99。此外，为了证明该模型的效率，还使用了三种统计检验方法，即 Wilcoxon、Friedman 和 P 值。研究强调了该模型在优化光伏系统和制氢方面的潜力，从而有助于减少二氧化碳排放。通过对包括光伏电池、压缩机和电解器在内的住宅系统进行全面模拟，该研究强调了 MEMD-ALO-BiLSTM 模型在可再生能源领域的实际可行性和显著进步，促进了向更可靠、更高效的太阳能制氢系统的转变。因此，光伏发电和通过电解槽制氢的前一天值在下午 1 点达到峰值，分别约为 75 千瓦和 1.4 千克。

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引用次数: 0

DMPC-based control solution for mobile robots platoon based on ZigBee communication 基于 ZigBee 通信的移动机器人排基于 DMPC 的控制解决方案

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-02 DOI: 10.1016/j.compeleceng.2024.109755

The integration of mobile robots into various applications, operating collaboratively in groups known as platoons, presents challenges in communication, control, and synchronization. This paper introduces a control solution for mobile robot platoons based on a DMPC algorithm and the ZigBee protocol for V2V communication. This communication standard was selected for its low power consumption, cost-effectiveness, and reliable data transmission over short distances. Furthermore, a testbed and methodology for the experimental evaluation of the proposed control solution were developed. The results demonstrate the efficacy and feasibility of the proposed control and communication solutions.

将移动机器人集成到各种应用中，以被称为 "排 "的形式分组协作运行，给通信、控制和同步带来了挑战。本文介绍了一种基于 DMPC 算法和用于 V2V 通信的 ZigBee 协议的移动机器人排控制解决方案。之所以选择这一通信标准，是因为它功耗低、成本效益高、短距离数据传输可靠。此外，还开发了一个测试平台和方法，用于对所提出的控制解决方案进行实验评估。结果证明了所提出的控制和通信解决方案的有效性和可行性。

引用次数: 0

Data-driven control framework using fractional order singular optimal control and optimized metaheuristic algorithms 使用分数阶奇异优化控制和优化元搜索算法的数据驱动控制框架

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2024-10-01 DOI: 10.1016/j.compeleceng.2024.109728

As the demand for advanced healthcare systems increases with the aging population, this paper introduces a novel data-driven control framework for constrained systems. The framework integrates signal processing algorithms with the optimal control of fractional order singular systems. Data collection was performed using a master-slave structure, while the classification process included preprocessing, window selection, feature extraction, and feature selection conducted through a genetic algorithm. We used machine learning algorithms, fuzzy wavelet neural networks using optimized metaheuristic algorithm, and convolutional neural network-long short-term memory (CNN-LSTM) for classification. We first decomposed both time-invariant and time-varying systems for the controller design to simplify the control process. This was followed by eliminating infinite modes, allowing for more efficient system control. We developed a novel linear method based on orthogonal functions to address the presence of both left and right fractional-order derivatives. The proposed framework's practicality was validated through its application in a rehabilitation system. Results indicated that electromyography (EMG) signals effectively classified movement states when combined with machine learning algorithms. In contrast, electroencephalogram (EEG) signals were better suited for classifying mental states. For movement classification using EEG signals, the fuzzy wavelet neural network and optimized CNN-LSTM emerged as the most effective methods. Among the orthogonal functions, the Chebyshev polynomial delivered the best performance, further confirming the robustness of our approach.

随着人口老龄化的加剧，对先进医疗保健系统的需求也随之增加，本文介绍了一种针对受限系统的新型数据驱动控制框架。该框架将信号处理算法与分数阶奇异系统的优化控制相结合。数据收集采用主从结构，分类过程包括预处理、窗口选择、特征提取，以及通过遗传算法进行特征选择。我们使用机器学习算法、使用优化元搜索算法的模糊小波神经网络和卷积神经网络-长短期记忆（CNN-LSTM）进行分类。在控制器设计中，我们首先分解了时变系统和时变系统，以简化控制过程。随后，我们消除了无限模式，从而实现了更高效的系统控制。我们开发了一种基于正交函数的新型线性方法，以解决存在左右分数阶导数的问题。通过在康复系统中的应用，验证了所提出框架的实用性。结果表明，肌电图（EMG）信号与机器学习算法相结合，能有效地对运动状态进行分类。相比之下，脑电图（EEG）信号更适合对精神状态进行分类。在使用脑电图信号进行运动分类时，模糊小波神经网络和优化 CNN-LSTM 成为最有效的方法。在正交函数中，切比雪夫多项式的性能最好，进一步证实了我们方法的鲁棒性。

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