Computers & Electrical Engineering最新文献

Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems 针对生产调度问题的集合元启发式和强化学习综述

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

Computers & Electrical Engineering

Pub Date : 2024-10-18 DOI: 10.1016/j.compeleceng.2024.109780

With the development of Artificial Intelligence, Internet of Things and Big Data, intelligent manufacturing has become a new and popular trend in manufacturing industries. Manufacturing scheduling is one of the most critical components in intelligent manufacturing systems. It aims to optimize some specific objectives, e.g., production cost, customer satisfaction and energy efficiency, by making optimal decisions of processing routes, machine assignment, operation sequence, etc. Due to manufacturing scheduling problems featured with large scale, strong coupling and real-time optimization requirements, it is a huge challenge to effectively cope with them. As the extensive and successful applications of artificial intelligence in manufacturing areas, meta-heuristics and reinforcement learning methods achieve great breakthroughs in addressing manufacturing scheduling problems. It is noted that a hybridization of meta-heuristic and reinforcement learning algorithms has been recently proposed to solve such complicated problems. Firstly, this work summarizes the designs of meta-heuristics and reinforcement learning methods for dealing with manufacturing scheduling problems, respectively. Secondly, we review the hybridization of meta-heuristics and reinforcement learning methods in solving manufacturing scheduling problems, where the essential roles of reinforcement learning for meta-heuristics are analyzed and discussed from the views of ensemble methods, optimization criteria, scheduling models, performance evaluation metrics and stopping conditions. Finally, we conclude this work and sum up future research directions regarding the hybridization methods in handling manufacturing scheduling problems.

随着人工智能、物联网和大数据的发展，智能制造已成为制造业的一种新的流行趋势。生产调度是智能制造系统中最关键的组成部分之一。它旨在通过对加工路线、机器分配、操作顺序等做出最优决策，优化某些特定目标，如生产成本、客户满意度和能效。由于生产调度问题具有规模大、耦合性强、实时优化等特点，如何有效应对这些问题是一个巨大的挑战。随着人工智能在制造领域的广泛和成功应用，元启发式和强化学习方法在解决制造调度问题上取得了重大突破。最近，有人提出了元启发式算法和强化学习算法的混合算法来解决此类复杂问题。首先，本文总结了元启发式和强化学习方法分别用于处理生产调度问题的设计。其次，我们回顾了元启发式和强化学习方法在解决生产调度问题中的混合应用，从集合方法、优化准则、调度模型、性能评价指标和停止条件等方面分析和讨论了强化学习对元启发式的重要作用。最后，我们对这项工作进行了总结，并归纳了混合方法在处理生产调度问题方面的未来研究方向。

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引用次数: 0

LbPV: Lattice-based Privacy-preserving mutual authentication scheme for VANET LbPV：基于网格的 VANET 隐私保护相互认证方案

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

Computers & Electrical Engineering

Pub Date : 2024-10-17 DOI: 10.1016/j.compeleceng.2024.109765

This study addresses the critical challenge of achieving reliable message exchange among vehicles in Vehicular Ad-hoc Networks (VANETs). It is crucial to quickly share safety messages, traffic updates, available services, and road conditions among vehicles in VANETs. For security reasons, messages must originate exclusively from authenticated vehicles, ensuring secure message exchange and data privacy. Numerous schemes for privacy-preserving authentication have been proposed, yet they suffer from constant service provider involvement and the requirement for vehicles to generate parameters on the fly. To address these challenges, this paper introduces LbPV, a secure lattice-based privacy-preserving mutual authentication scheme emphasizing the reliability of messages, preventing spoofing and unauthorized access. LbPV eliminates service provider monitoring and allows vehicles to authenticate messages without generating parameters while on the move. This is achieved by exchanging a confidential token using lattice-based encryption and message signing. By verifying the signed message and using the shared token, receiving vehicles can confirm the authenticity of the messages. By using lattice-based cryptography, the proposed protocol is also designed with the potential to resist future attacks, including quantum attacks, enhancing its long-term security viability. The security analysis of LbPV includes formal and informal evaluations that demonstrate its robustness. Performance evaluations using the NTL library show that LbPV, with service provider parameters, outperforms existing approaches in the literature. Results of performance analysis indicate that when compared to the most efficient traditional non-lattice-based scheme discussed in this paper, the proposed protocol has increased computation cost, communication cost, and power consumption by 77.71%, 98.58%, and 77.71%, respectively. Conversely, when compared to the most efficient lattice-based scheme discussed in this paper, the proposed scheme demonstrates reductions in computation cost, communication cost, and power consumption by 71.95%, 2.16%, and 71.95%, respectively.

本研究探讨了在车载 Ad-hoc 网络 (VANET) 中实现车辆间可靠信息交换的关键挑战。在 VANET 中，车辆之间快速共享安全信息、交通更新、可用服务和路况至关重要。出于安全考虑，信息必须仅来自经过认证的车辆，以确保信息交换安全和数据隐私。目前已经提出了许多保护隐私的认证方案，但这些方案都存在服务提供商不断参与和车辆需要即时生成参数的问题。为了应对这些挑战，本文介绍了 LbPV，这是一种基于网格的安全隐私保护相互验证方案，强调信息的可靠性，防止欺骗和未经授权的访问。LbPV 消除了服务提供商的监控，允许车辆在移动过程中验证信息而无需生成参数。这是通过使用基于网格的加密和信息签名交换保密令牌来实现的。通过验证签名信息和使用共享令牌，接收车辆可以确认信息的真实性。通过使用基于网格的加密技术，所提出的协议还具有抵御未来攻击（包括量子攻击）的潜力，从而增强了其长期安全性。LbPV 的安全性分析包括正式和非正式评估，以证明其稳健性。使用 NTL 库进行的性能评估表明，LbPV 在带有服务提供商参数的情况下，性能优于文献中的现有方法。性能分析结果表明，与本文讨论的最有效的传统非网格方案相比，所提出的协议在计算成本、通信成本和功耗方面分别增加了 77.71%、98.58% 和 77.71%。相反，与本文讨论的最有效的基于网格的方案相比，拟议方案的计算成本、通信成本和功耗分别降低了 71.95%、2.16% 和 71.95%。

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引用次数: 0

A robust secret data sharing through coverless video steganography based on average DC coefficient on bit plane segmentation 基于位平面分割的平均 DC 系数，通过无掩码视频隐写术实现稳健的秘密数据共享

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

Computers & Electrical Engineering

Pub Date : 2024-10-17 DOI: 10.1016/j.compeleceng.2024.109766

Recently, coverless steganography has been gaining popularity in confidential information-hiding techniques due to its resistance to the detection of steganalysis. Whereas most coverless approaches are based on images as the transmission medium, few articles are based on video in coverless steganography. Whereas the hiding capacity per frame in the existing scheme is less, and to improve the capacity, a new secret data sharing through coverless video steganography is proposed based on the average Direct Current (DC) coefficient on the Bit plane. The bit planes are extracted from the video frame, generating multiple hash sequences from each bit plane. The bit plane is further partitioned into blocks. The average DC coefficient of consecutive subblocks within a block in a bit plane frame is compared to create the hash bit sequences. A video index database is created to store the generated hash sequences for easy retrieval. The receiver successfully recovered the secret information from the retrieval information and received videos. The observed findings show that the suggested method outperforms recent coverless video steganography techniques. The robustness is substantially stronger and superior to other existing schemes.

最近，无掩盖隐写术因其抗隐写分析检测的能力而在机密信息隐藏技术中越来越受欢迎。大多数无掩码方法都是以图像作为传输介质，而很少有文章以视频作为无掩码隐写术的传输介质。现有方案中每帧的隐藏能力较低，为了提高隐藏能力，提出了一种基于比特平面上平均直流（DC）系数的新的无掩盖视频隐写术秘密数据共享方法。从视频帧中提取位平面，从每个位平面生成多个哈希序列。位平面被进一步分割成块。比较位平面帧中一个块内连续子块的平均直流电系数，以创建哈希位序列。创建一个视频索引数据库来存储生成的哈希序列，以便于检索。接收器成功地从检索信息和接收到的视频中恢复了秘密信息。观察结果表明，所建议的方法优于最新的无掩码视频隐写技术。其稳健性大大强于其他现有方案。

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引用次数: 0

A novel DDoS detection method using multi-layer stacking in SDN environment 在 SDN 环境中使用多层堆叠的新型 DDoS 检测方法

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

Computers & Electrical Engineering

Pub Date : 2024-10-17 DOI: 10.1016/j.compeleceng.2024.109769

Software Defined Network (SDN) offers virtualized services compatible with infrastructure hosted computing, presenting a flexible, adaptive, and economical network architecture. Switches used in SDN prioritize packet matching in flow tables above packet processing, leaving them open to Denial of Service (DoS) attacks. These attacks, exemplified by Distributed Denial of Service Attacks (DDoS), target a victim while using many infected workstations at once. Due to its scalability and programmability, SDN is being used more and more for network management. However, it has specific security concerns, such as the controller’s susceptibility to cyberattacks, which might result in a single point of failure and network-wide risks. This study proposes a novel DDoS prediction model by developing stacking classifier model consisting of multiple base classifiers for an SDN environment. The proposed model is built on stacking several classifiers at the base level and the Meta level, which mixes varied or heterogeneous learners to provide reliable model results. The findings demonstrate that the proposed stacking model outperforms other existing models with respect to accuracy, sensitivity, specificity, precision, and F1 score. Finally, the stacking classifier model is evaluated in terms of binary classification. The evaluation shows the highest AUC of 0.9537 whereas Random Forest, Decision Tree, and Logistic Regression achieve AUC values around 0.93–0.95.

软件定义网络（SDN）提供与基础设施托管计算兼容的虚拟化服务，是一种灵活、自适应和经济的网络架构。SDN 中使用的交换机将流量表中的数据包匹配优先级置于数据包处理之上，从而使其容易受到拒绝服务 (DoS) 攻击。这些攻击以分布式拒绝服务攻击（DDoS）为例，目标是同时使用许多受感染工作站的受害者。由于具有可扩展性和可编程性，SDN 被越来越多地用于网络管理。然而，它也有一些特定的安全问题，如控制器易受网络攻击，可能导致单点故障和全网风险。本研究通过为 SDN 环境开发由多个基础分类器组成的堆叠分类器模型，提出了一种新型 DDoS 预测模型。所提出的模型是在基础层和元层堆叠多个分类器而建立的，它混合了不同或异构的学习者，以提供可靠的模型结果。研究结果表明，所提出的堆叠模型在准确度、灵敏度、特异性、精确度和 F1 分数方面都优于其他现有模型。最后，从二元分类的角度对堆积分类器模型进行了评估。评估结果显示，AUC 最高，为 0.9537，而随机森林、决策树和逻辑回归的 AUC 值约为 0.93-0.95。

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引用次数: 0

Multi-faceted sustainability improvement in low voltage power distribution network employing DG and capacitor bank 利用 DG 和电容器组多方面改善低压配电网络的可持续性

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

Computers & Electrical Engineering

Pub Date : 2024-10-17 DOI: 10.1016/j.compeleceng.2024.109789

Efficient distribution of active and reactive power in distribution networks is crucial for ensuring that consumer demand is met and that electrical power quality is maintained. This requires careful planning, particularly in terms of optimally placing and sizing distributed generator (DG) and capacitor bank (CB) units. These units play a key role in minimizing power losses and voltage fluctuations within the network. However, implementing DG and CB units in unbalanced distribution networks presents unique challenges due to inherent system unbalances. To address these challenges, this article proposes a method for allocating and sizing DG and CB units in unbalanced distribution networks. The approach accounts for the network's unbalance and targets multiple objectives, such as reducing power losses, improving multi-phase voltage stability, and minimizing phase-to-phase voltage unbalance. The fast and flexible radial power flow (FFRPF) technique is employed to model complex interactions and constraints within the network, leading to a multi-objective optimization problem. This optimization problem is solved using the weight aggregated particle swarm optimization (WA-PSO) method, a variant of particle swarm optimization tailored for multi-objective functions. WA-PSO simplifies the process by combining multiple objectives into a single function using weighted aggregation. The efficacy of this approach is validated on 19, 34, and 123-node unbalanced radial distribution networks (URDNs). The results show significant improvements in power delivery efficiency across all tested networks, especially when DG and CB units are operated simultaneously. Specifically, DG and CB integration led to a reduction in system losses by 89.90 %, 88.42 %, and 86.87 %, and a decrease in three-phase voltage unbalance indices by 88.75 %, 81.68 %, and 39.05 %, for the 19, 34, and 123-node systems, respectively, while maintaining voltage stability within acceptable limits. Additionally, CO₂ emissions were reduced by 52 %, 62 %, and 53 % when microturbines were utilized instead of coal-based thermal power plants, further highlighting the environmental benefits of the proposed approach.

配电网络中有功和无功功率的有效分配对于确保满足用户需求和保持电能质量至关重要。这需要精心规划，特别是在优化分布式发电机 (DG) 和电容器组 (CB) 的布置和大小方面。这些设备在最大限度减少电网内的电力损耗和电压波动方面发挥着关键作用。然而，由于固有的系统不平衡，在不平衡配电网络中实施分布式发电机和电容器组单元面临着独特的挑战。为了应对这些挑战，本文提出了一种在不平衡配电网络中分配和确定 DG 和 CB 单元大小的方法。该方法考虑了配电网的不平衡性，并以降低功率损耗、改善多相电压稳定性和最大限度减少相间电压不平衡等多个目标为目标。该方法采用快速灵活的径向功率流（FFRPF）技术来模拟网络内复杂的相互作用和约束条件，从而产生一个多目标优化问题。该优化问题采用权重聚合粒子群优化（WA-PSO）方法解决，该方法是粒子群优化的一种变体，专为多目标函数量身定制。WA-PSO 利用加权聚合法将多个目标合并为一个函数，从而简化了过程。这种方法的有效性在 19、34 和 123 节点的不平衡径向配电网络 (URDN) 上得到了验证。结果表明，所有测试网络的电力输送效率都有明显改善，尤其是当 DG 和 CB 设备同时运行时。具体而言，在 19 节点、34 节点和 123 节点系统中，DG 和 CB 的集成使系统损耗分别降低了 89.90 %、88.42 % 和 86.87 %，三相电压不平衡指数分别降低了 88.75 %、81.68 % 和 39.05 %，同时将电压稳定性保持在可接受的范围内。此外，当使用微型涡轮机替代燃煤火力发电厂时，二氧化碳排放量分别减少了 52%、62% 和 53%，进一步凸显了拟议方法的环境效益。

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引用次数: 0

Proposal automatic water purging system for machinery in high humidity environments controlled by an ECU 由 ECU 控制的用于高湿度环境中机械设备的自动水净化系统提案

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

Computers & Electrical Engineering

Pub Date : 2024-10-16 DOI: 10.1016/j.compeleceng.2024.109775

This paper presents an advanced automatic water discharge system tailored for diesel-powered machinery, particularly for vehicles operating in diverse climates, including humid coastal regions. The system is designed to protect fuel circuits in tanks and diesel engines of boats, offering either automatic or semi-automatic protection against oxidation and corrosion. This enhancement extends the lifespan of diesel components and pipelines while simultaneously reducing maintenance costs and intervention times. Additionally, the system improves fuel purity, resulting in increased engine power and efficiency. The proposed solution was experimentally validated using an Arduino board coupled with a water-sensitive detector in diesel fuel, suitable for installation in diesel filters or tanks. Experimental findings demonstrated a remarkably swift separation time of 150 microseconds for diesel and water at a pressure of 1 bar, achieving an impressive efficiency of 99.84%. The system's rapid and effective separation capability highlights its practical value, particularly in scenarios where efficient water removal is critical to sustaining engine performance and longevity.

本文介绍了一种先进的自动排水系统，该系统专为柴油动力机械量身定制，尤其适用于在不同气候条件下运行的车辆，包括潮湿的沿海地区。该系统设计用于保护船舶油箱和柴油发动机中的燃料回路，提供自动或半自动保护，防止氧化和腐蚀。这种改进可延长柴油元件和管道的使用寿命，同时降低维护成本和干预时间。此外，该系统还能提高燃油纯度，从而增强发动机的动力和效率。我们使用 Arduino 电路板和柴油中的水敏探测器对所提出的解决方案进行了实验验证，该探测器适合安装在柴油过滤器或油箱中。实验结果表明，在 1 巴压力下，柴油和水的快速分离时间为 150 微秒，效率高达 99.84%。该系统快速有效的分离能力凸显了其实用价值，特别是在高效除水对维持发动机性能和使用寿命至关重要的情况下。

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引用次数: 0

Corrigendum to “Intelligent hybrid energy system and grid integration using microcontrollers” [Computers and Electrical Engineering, Volume 110, September 2023, 108873] 使用微控制器的智能混合能源系统和电网集成》更正[《计算机与电气工程》，第 110 卷，2023 年 9 月，108873]

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

Computers & Electrical Engineering

Pub Date : 2024-10-15 DOI: 10.1016/j.compeleceng.2024.109772

引用次数: 0

Steering authority allocation strategy for human-machine shared control based on driver take-over feasibility 基于驾驶员接管可行性的人机共享控制转向权限分配策略

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

Computers & Electrical Engineering

Pub Date : 2024-10-15 DOI: 10.1016/j.compeleceng.2024.109753

Misoperation when a driver is distracted, e.g., turning the steering wheel unconsciously, preventing the machine from turning the steering wheel, etc., can lead to a false take-over of the vehicle, which poses a danger to shared control. Therefore, it is essential to evaluate the feasibility of driver take-over in distraction status. This paper proposes a steering authority allocation strategy based on driver take-over feasibility to reduce the interference of distracted driver behavior on shared control. First, a driver take-over feasibility framework is established based on quantified driver distraction levels and take-over intention. Then the shared control system is established based on a linear parameter-varying vehicle model and non-cooperative game theory to suppress the interference of time-varying speed and parameter uncertainty on shared control. Subsequently, the steering authority allocation strategy is constructed in conjunction with driver take-over feasibility and environmental risk evaluation. The proposed sigmoid-based mapping function ensures fast and smooth authority allocation. Finally, a series of driver-in-the-loop experiments are conducted to validate the effectiveness of our proposed strategy across various driving scenarios, and the results demonstrate that our strategy has superior performance than other advanced shared control schemes in reducing steering burden, improving driving safety, and enhancing vehicle stability, etc.

驾驶员分心时的错误操作，如无意识转动方向盘、阻止机器转动方向盘等，会导致错误接管车辆，从而给共享控制带来危险。因此，评估分心状态下驾驶员接管的可行性至关重要。本文提出了一种基于驾驶员接管可行性的转向权限分配策略，以减少驾驶员分心行为对共享控制的干扰。首先，根据量化的驾驶员分心程度和接管意图，建立驾驶员接管可行性框架。然后，基于线性参数变化车辆模型和非合作博弈论建立共享控制系统，以抑制时变速度和参数不确定性对共享控制的干扰。随后，结合驾驶员接管可行性和环境风险评估，构建了转向权限分配策略。所提出的基于 sigmoid 的映射函数确保了快速平稳的权限分配。最后，我们进行了一系列驾驶员在环实验，以验证我们提出的策略在各种驾驶场景下的有效性，结果表明，与其他先进的共享控制方案相比，我们的策略在减轻转向负担、提高驾驶安全性和增强车辆稳定性等方面具有更优越的性能。

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引用次数: 0

Corrigendum to “Maximum power point tracking for grid-connected photovoltaic system using Adaptive Fuzzy Logic Controller” [Computers and Electrical Engineering, Volume 110, September 2023, 108879] 利用自适应模糊逻辑控制器实现并网光伏系统的最大功率点跟踪"[《计算机与电气工程》，第 110 卷，2023 年 9 月，108879] 更正

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

Computers & Electrical Engineering

Pub Date : 2024-10-15 DOI: 10.1016/j.compeleceng.2024.109773

引用次数: 0

Landscape of learning techniques for intrusion detection system in IoT: A systematic literature review 物联网入侵检测系统学习技术的前景：系统性文献综述

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

Computers & Electrical Engineering

Pub Date : 2024-10-15 DOI: 10.1016/j.compeleceng.2024.109725

The IoT has interconnected devices that collaborate via the Internet. Yet, its widespread connectivity and data generation pose cybersecurity risks. Integrating robust intrusion detection systems (IDSs) into the architecture has become crucial. IDSs safeguard data, detect attacks, and ensure network security and privacy. Constructing anomaly-based intrusion detection systems using artificial intelligence methods, often termed learning techniques, has gained significant traction lately. In this context, this study undertakes a systematic literature review to comprehensively analyze the current landscape of research concerning IoT security, explicitly employing learning techniques. These techniques fall under four primary categories: machine learning, deep learning, transfer learning, and federated learning. From a pool of 646 papers published between 2018 and 2023, we have selected 36 papers encompassing all these techniques based on the keywords of the study. These chosen studies were then categorized based on their respective learning techniques, with an additional hybrid classification that combines federated learning and transfer learning. Moreover, the paper provides a comparative analysis of the studied articles across different dimensions. The research outcomes demonstrate the effectiveness of each learning technique, shed light on the datasets and metrics employed, and conclude with a discussion on open challenges and future recommendations in this domain

物联网通过互联网将设备相互连接并进行协作。然而，其广泛的连接性和数据生成带来了网络安全风险。在架构中集成强大的入侵检测系统（IDS）已变得至关重要。IDS 可保护数据、检测攻击并确保网络安全和隐私。最近，利用人工智能方法（通常称为学习技术）构建基于异常的入侵检测系统受到了广泛关注。在此背景下，本研究通过系统的文献综述，全面分析了当前明确采用学习技术的物联网安全研究的现状。这些技术主要分为四类：机器学习、深度学习、迁移学习和联合学习。从 2018 年至 2023 年间发表的 646 篇论文中，我们根据研究关键词选出了 36 篇包含所有这些技术的论文。然后，我们根据各自的学习技术对这些被选中的研究进行了分类，并结合联合学习和迁移学习进行了额外的混合分类。此外，本文还对所研究的文章进行了不同维度的比较分析。研究成果证明了每种学习技术的有效性，阐明了所使用的数据集和衡量标准，最后还讨论了该领域的挑战和未来建议。

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引用次数: 0