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11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024

作者： Wang, Chong Liu, Yajie Song, Yuanming Wang, Yu College of System Engineering National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha410073 China

ISBN: (纸本)9789819788231

During the operation of lithium-ion battery packs, there often exhibit certain abnormalities due to cell faults such as internal short circuit or unavoidable inconsistencies among cells, which affects the operation safety of the battery packs. Therefore, it is of great significance to carry out abnormality detection for the battery packs. Accordingly, this paper proposes a feature selection method based on Kullback-Leibler (K-L) test and an improved Greenwald-Khanna (GK) clustering algorithm. Initially, faults are introduced into the battery pack using the Simulink simulation platform to obtain simulation data. Subsequently, parameter identification is identified based on the Rint equivalent circuit model and the Recursive Least Squares (RLS) algorithm. Finally, better consistency features are selected using the K-L test as inputs to the GK clustering algorithm, and the K-Means++ method is used to optimize the GK algorithm for clustering features. The results demonstrate that this method can effectively distinguish between normal and abnormal batteries, achieving abnormality detection in battery packs. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Clustering algorithms

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Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids

arXiv

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arXiv 2025年

作者： Liu, Wei Zhang, Tao Lin, Chenhui Li, Kaiwen Wang, Rui College of System Engineering the National University of Defense Technology the Xiangjiang Laboratory the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology HKL-MSI2T Changsha410073 China State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China

Independent microgrids are crucial for supplying electricity by combining distributed energy resources and loads in scenarios like isolated islands and field combat. Fast and accurate assessments of microgrid vulnerability against intentional attacks or natural disasters are essential for effective risk prevention and design optimization. However, conventional Monte Carlo simulation (MCS) methods are computationally expensive and time-consuming, while existing machine learning-based approaches often lack accuracy and explainability. To address these challenges, this study proposes a fast and explainable vulnerability assessment framework that integrates MCS with a graph attention network enhanced by self-attention pooling (GAT-S). MCS generates training data, while the GAT-S model learns the structural and electrical characteristics of the microgrid and further assesses its vulnerability intelligently. The GAT-S improves explainability and computational efficiency by dynamically assigning attention weights to critical nodes. Comprehensive experimental evaluations across various microgrid configurations demonstrate that the proposed framework provides accurate vulnerability assessments, achieving a mean squared error as low as 0.001, real-time responsiveness within 1 second, and delivering explainable results. Copyright © 2025, The Authors. All rights reserved.

关键词： Microgrids

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Synergistically regulated PCL scaffold bioactivity and antibacterial activity by wollastonite and in situ zinc oxide

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Ceramics International 2025年第14期51卷 18573-18586页

作者： Li, Dongying Du, Haocheng Ding, Wenhao Guo, Xiaoping Yuan, Hai Jiang, Jinglu Li, Mengqi Long, Nanbiao Xu, Yong Key Laboratory of Hunan Province for Efficient Power System and Intelligent Manufacturing College of Mechanical and Energy Engineering Shaoyang University Shaoyang422000 China School of Automation Hangzhou Dianzi University Hangzhou310018 China Department of Medical Technology Shaoyang University Shaoyang422000 China Shaoyang Industry Polytechnic College Shaoyang422000 China

Synergistically improving bioactivity and imparting antibacterial function are the problems that need to be solved urgently in the application of polycaprolactone (PCL) scaffolds in the field of tissue engineering. To this end, PCL/wollastonite/zinc oxide (PCL/15Wol + ZnO) composite scaffolds were fabricated by combining triply periodic minimal surfaces (TPMS) design, selective laser sintering (SLS) and in-situ deposition technology. First, the rapid degradation of Wol sustainably released Ca2+ ions, inducing the deposition of osteoid apatite on scaffolds and significantly enhancing bioactivity. Second, ZnO grown in situ on the scaffold provided antibacterial functionality by disrupting bacterial structures through oxidative stress. The results indicated that the composite scaffold immersed in SBF quickly induced the formation of apatite layer, possibly due to the substitution of H+ in solution with Ca2+ in Wol, forming a negatively charged silicon-rich layer that subsequently attracted the deposition of HPO42− via electrostatic interactions. In the degradation experiments, Wol played a crucial role in neutralizing acidic byproducts released during PCL degradation and raising the pH of the solution. Furthermore, in-situ zinc oxide endowed the scaffold with antibacterial activity against Escherichia coli and the antibacterial test showed that the 15Wol composite scaffold had an inhibition zone with a diameter of 17 mm. Moreover, the composite scaffold provided a favorable environment for cell survival, which promoted the adhesion, extension and survival viability of MC3T3-E1 cells. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Laser heating

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An anomaly detection method for spacecraft solar arrays based on the ILS-SVM model

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Journal of systems Engineering and Electronics 2023年第2期34卷 515-529页

作者： WANG Yu ZHANG Tao HUI Jianjiang LIU Yajie College of Systems Engineering National University of Defense TechnologyChangsha 410073China Hunan Key Laboratory of Multi-energy System Intelligent Interconnection Technology Changsha 410073China Beijing Institute of Tracking and Telecommunication Technology Beijing 100094China

Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit *** a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnormal damage *** damage makes solar array power generation unable to fully meet the energy demand of a ***,timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of *** this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine(ILS-SVM)model:it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine(LS-SVM)submodels by training on these training subsets,respectively;after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on;finally,based on the obtained ILS-SVM model,a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar *** use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.

关键词： spacecraft solar array anomaly detection integrated least squares support vector machine(ILS-SVM) induced ordered weighted average(IOWA)operator integrated model

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UAV Path Planning Based on Variable Neighborhood Search Genetic Algorithm 12th

UAV Path Planning Based on Variable Neighborhood Search Gene...

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12th International Conference on Advances in Swarm Intelligence, ICSI 2021

作者： Zhang, Guo Wang, Rui Lei, Hongtao Zhang, Tao Li, Wenhua Song, Yuanming College of Systems Engineering National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha410073 China

ISBN: (纸本)9783030788100

This study proposed a new genetic algorithm with variable neighbourhood search (GAVNS) for UAV path planning in three-dimensional space. First, an 0–1 integer programming mathematical model is established by inspired from the vehicle routing planning model with time window (VRPTW), and then a heuristic rule based on space vector projection is designed to quickly initialize high-quality solutions that meet constraints of upper error limit and minimum turning radius. Second, it improves mutation operator with a reselected mutation strategy, and incorporates Variable Neighborhood Search strategy based on adding and deleting route during the search process;Finally, GAVNS is compared with general Genetic Algorithm on a set of experiments. It is demonstrated that GAVNS algorithm is both effective and efficient. Moreover, the introduction of variable neighborhood search strategy enhances the local search ability of Genetic Algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Motion planning

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Coevolutionary Framework for Generalized multimodal multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1544-1556页

作者： Wenhua Li Xingyi Yao Kaiwen Li Rui Wang Tao Zhang Ling Wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Xiangjiang Laboratory and also with the College of Systems EngineeringNational University of Defense TechnologyChangsha 410073China College of Systems Engineering National University of Defense Technologyand also with the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection TechnologyChangsha 410073China Department of Automation Tsinghua UniversityBeijing 100084China

Most multimodal multi-objective evolutionary algorithms(MMEAs)aim to find all global Pareto optimal sets(PSs)for a multimodal multi-objective optimization problem(MMOP).However,in real-world problems,decision makers(DMs)may be also interested in local ***,searching for both global and local PSs is more general in view of dealing with MMOPs,which can be seen as generalized ***,most state-of-theart MMEAs exhibit poor convergence on high-dimension MMOPs and are unable to deal with constrained *** address the above issues,we present a novel multimodal multiobjective coevolutionary algorithm(Co MMEA)to better produce both global and local PSs,and simultaneously,to improve the convergence performance in dealing with high-dimension ***,the Co MMEA introduces two archives to the search process,and coevolves them simultaneously through effective knowledge *** convergence archive assists the Co MMEA to quickly approach the Pareto optimal *** knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the-dominance-based method to obtain global and local PSs *** results show that Co MMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.

关键词： Coevolution ∈-dominance generalized multimodal multi-objective optimization(MMO) local convergence two archives

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Knowledge-Guided Evolutionary Optimization for Large-Scale Air Defense Resource Allocation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6267-6279页

作者： Li, Wenhua Wang, Rui Heng, Yong Zhang, Tao Wang, Ling National University of Defense Technology College of Systems Engineering Changsha410073 China Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha410073 China Xiangjiang Laboratory Changsha410073 China Beijing Institute of Electronic System Engineering Beijing100854 China Tsinghua University Department of Automation Beijing100084 China

In the high-stakes domain of air defense, resource allocation is a challenging optimization problem that involves many constraints and requires fast solutions. Previous work mainly addressed the single-intercept sensor-weapon-target assignment problem, which assigns missiles and radars to targets without optimizing the launch time. However, this problem can become large-scale and difficult to solve when saturation attack tactics are employed. Therefore, we propose a more realistic model that allows multiple missiles to intercept the same target and considers the launch time as a decision variable. We also incorporate radar relay guidance to maximize the interception probability and minimize the missile cost. To solve this model efficiently, we present a novel evolutionary algorithm with linear population size reduction (LPSR) and knowledge-guided strategies, called LKEA. Specifically, we design a heuristic method to quickly find the optimal launch time for each missile and a knowledge-guided strategy to use the radar channel collision information to guide the evolution. Experimental results show that LKEA is competitive in solving small/medium/large air defense resource allocation problems (ADRAPs). © 2020 IEEE.

关键词： Combinatorial optimization

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Solving Orienteering Problems by Hybridizing Evolutionary Algorithm and Deep Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5493-5508页

作者： Wang, Rui Liu, Wei Li, Kaiwen Zhang, Tao Wang, Ling Xu, Xin National University of Defense Technology College of System Engineering Changsha410073 China Xiangjiang Laboratory Changsha410205 China Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology HKL-MSI2T Changsha410073 China Tsinghua University Department of Automation Beijing100084 China National University of Defense Technology College of Intelligence Science and Technology Changsha410073 China

The orienteering problem (OP) is widely applied in real life. However, as the scale of real-world problem scenarios grows quickly, traditional exact, heuristics, and learning-based methods have difficulty balancing optimization accuracy and efficiency. This study proposes a problem decomposition-based double-layer optimization framework named DEA-DYPN to solve OPs. Using a diversity evolutionary algorithm (DEA) as the external optimizer and a dynamic pointer network (DYPN) as the inner optimizer, we significantly reduce the difficulty of solving large-scale OPs. Several targeted optimization operators are innovatively designed for stronger search ability, including a greedy population initialization heuristic, an elite strategy, a population restart mechanism, and a fitness-sharing selection strategy. Moreover, a dynamic embedding mechanism is introduced to DYPN to improve its characteristic learning ability. Extensive comparative experiments on OP instances with sizes from 20 to 500 are conducted for algorithmic performance validation. More experiments and analyses, including the significance test, stability analysis, complexity analysis, sensitivity analysis, and ablation experiments, are also conducted for comprehensive algorithmic evaluation. Experimental results show that our proposed DEA-DYPN ranks first according to the Friedman test and outperforms the competitor algorithms by 69%. © 2024 IEEE.

关键词： Evolutionary algorithms

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Defence technology（防务技术） 2021年第2期17卷 531-544页

作者： Gui-ju Zhang Cai-yuan Xiao Olatunj Oladimeji Ojo Key Laboratory of Hunan Province for Efficient Power System and Intelligent Manufacturing Shaoyang422000HunanChina College of Mechanical and Energy Engineering Shaoyang UniversityShaoyang422000HunanChina Department of Industrial and Production Engineering Federal University of Technology AkureAkureNigeria

This paper studies the friction stir spot welding of AA2024-T3/AA7075-T6 Al alloys in the ambient and underwater environments by clarifying the nugget features,microstructure,fracture and mechanical properties of the *** results show that the water-cooling medium exhibits a significant heat absorption capacity in the AA2024-T3/AA7075-T6 welded *** features such as stir zone width,circular imprints,average grain sizes,and angular inter-material hooking are reduced by the watercooling effect in the *** whitish(intercalated structures)bands are formed in the underwater joints while Mg2Si and Al2CuMg precipitates are formed in the ambient and the underwater welded joints *** increase in tool rotational speed(600e1400 rpm)and plunge depth(0.1 e0.5 mm)increases the tensile-shear force of the welded AA2024-T3/AA7075-T6 joints in both the ambient and underwater *** maximum tensile-shear forces of 5900 N and 6700 N were obtained in the ambient and the underwater welds respectively.

关键词： Friction stir spot welding Underwater friction stir spot welding Nugget characterization Microstructure Mechanical properties Fracture

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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