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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Liu, Jinyue Gao, Xiang Yang, Haowei Zhang, Jihao Liu, Yuhan Gong, Peng National Key Laboratory of Mechatronic Engineering and Control School of Mechatronical Engineering Beijing Institute of Technology Beijing China Institute of Computer Application Technology Norinco Group Beijing China

ISBN: (纸本)9791188428137

In modern battlefields, the stability of wireless communications is crucial for intelligence transmission, command coordination, and maintaining strategic advantage. However, with the rapid advancement of communication technologies, the electromagnetic environment has become increasingly complex, making intelligent decision-making in communication countermeasures a prominent research focus. However, most existing studies adopt deep learning and reinforcement learning methods, which require extensive data samples and computational resources. To address these limitations, this paper proposes a game-theoretic model for intelligent radio communication countermeasures under incomplete information. It quantitatively evaluates common attack and defense tactcis used in communication countermeasures and explores the mixed-strategy equilibrium of the game when both the attackers and defenders consist of multiple types. Furthermore, simulation-based comparative analyses are conducted to assess the factors influencing the utility of both the attacker and defender. The findings offer theoretical insights and decision-making support for the deployment of communication countermeasure strategies in practical military operations, contributing to significant strategic value. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Deep reinforcement learning

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A Registration and Fusion Method of 3D Cross-source Point Cloud Data for Modeling Accurate Models of Small Celestial Bodies

A Registration and Fusion Method of 3D Cross-source Point Cl...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： He, Xuan Zhao, Qingjie Lv, Xingchen Wang, Lei Liu, Wangwang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Control Engineering Beijing100190 China

ISBN: (纸本)9781665481090

High-precision three-dimensional models of small celestial bodies are important for deep space exploration tasks. The homologous point cloud obtained from a single sensor has limitations in terms of accuracy and density. For example, the point cloud obtained by RGB camera has rich information and high density, but low precision. The point cloud obtained by laser scanning has high precision but low density. Therefore, we hope to register and fuse the point clouds obtained from different sensors, synthesize the advantages of cross-source point clouds obtained from different source domains, and improve the density, accuracy and other aspects of small celestial point clouds. To solve the common problems of cross-source point clouds, we adopt an end-to-end feature measurement point cloud registration framework, which does not need to search the corresponding points between two point clouds, and reduces the difficulty of processing cross-source point cloud data. Aiming at the problem that some stray points on the registered point cloud surface affect the accuracy of the point cloud, a point cloud fusion optimization method based on Euclidean distance is proposed. The stray points around the point cloud are removed to reduce the loss of normal points. The accuracy of point cloud is improved by the fusion after registration. Experiments show that the proposed method can effectively improve the density and accuracy of small celestial body models. © 2024 IEEE.

关键词： Laser applications

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State of health estimation of lithium-ion batteries based on TiDE

State of health estimation of lithium-ion batteries based on...

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2024 China Automation Congress, CAC 2024

作者： Li, Hui Cui, Fangshu Shi, Yuanhao Wang, Bohui School of Electrical and Control Engineering North University of China Taiyuan China School of Computer Science and Technology North University of China Taiyuan China School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350368604

Given the widespread use of lithium-ion batteries, accurately forecasting their State of Health (SOH) is crucial for ensuring the secure and reliable operation of equipment. The local capacity regeneration during battery degradation can undermine prediction accuracy. This research introduces an MDT-FOA+TiDE approach for predicting SOH in lithium-ion batteries using multidimensional time series data. Initially, capacity and various feature data from the degradation process were collected and health indicators were derived. Features with high correlation to capacity were integrated into multidimensional time series. Subsequently, the Time-series Dense Encoder (TiDE) model was employed for training and prediction, while the Fox Optimization Algorithm (FOA) was used for model optimization. Experimental results using the NASA dataset demonstrate that the proposed approach outperforms SVR and LSTM models that utilize univariate data and MDT-TiDE model without FOA optimization. © 2024 IEEE.

关键词： lithium-ion batteries local capacity regeneration multidimensional time series state of health

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Embedded Multi-Port Converters for Synthesis and Enhancement of Hybrid-Clamped Multilevel Converters

Embedded Multi-Port Converters for Synthesis and Enhancement...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Li, Yuzhuo Gunawardena, Pasan Tian, Hao Li, Yunwei University of Alberta Department of Electrical and Computer Engineering Edmonton Canada Shandong University School of Control Science and Engineering Jinan China

ISBN: (纸本)9798350316445

Leveraging a number of inner capacitors/inductors, hybrid-clamped multilevel converters (MLCs) normally face great challenges among good performance (proper charge/discharge of these devices), high efficiency (maintaining low losses) and high power density (compact profile). On the other hand, these multiple-device energy-processing requirements have been addressed well in some promising multi-port converters (MPCs), and, therefore, inspire us to implement well-developed compact MPCs to facilitate the voltage/current level generation process in hybrid-clamped MLCs. Though recently, some researchers started to integrate active cells into hybrid-clamped MLCs and improve capacitor voltage control and generate extra output levels, the systematic synthesis method is still unclear and rarely discussed in the literature. To address this gap, we propose a systematic synthesis method for those hybrid clamped MLCs that can benefit from embedding well-developed MPCs. The approach can be applied for both voltage-source and current-source hybrid-clamped MLCs, covering emerging MLCs. In particular, we also derived and verified a new mixed hybrid MLC family through an emerging current-fed dual-input isolated multi-port converter. This topology features both current-source and voltage-source benefits and is ideal for future renewable generation integration. © 2023 IEEE.

关键词： Converters Synthesis Hybrid-Clamped Multi-level Converters Multi-port Converters

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Some Remarks on Fault-Tolerant Dynamic control Allocation for Weakly Redundant Systems 32

Some Remarks on Fault-Tolerant Dynamic Control Allocation fo...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Cristofaro, Andrea Faleschini, Michelangelo Sapienza University of Rome Department of Computer Control and Management Engineering Italy School of Science and Technology Mathematics Division University of Camerino Italy

ISBN: (纸本)9798350395440

We consider the problem of control allocation for weakly redundant systems subject to actuator faults. In particular, the design of a suitable allocator will be devised with the aim of compensating for the fault effects while, at the same time, keeping the control burden as low as possible. To this goal, two approaches can be followed for the synthesis of the allocation servomechanism: direct allocation using orthogonal projection and optimization-based allocation. Some numerical examples illustrate and highlight advantages, disadvantages and limitations of both strategies. © 2024 IEEE.

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Transit search optimized least squares support vector machine for the state of health estimation of lithium-ion batteries

Transit search optimized least squares support vector machin...

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2024 China Automation Congress, CAC 2024

作者： Wang, Kangjie Cui, Fangshu Shi, Yuanhao Wang, Bohui School of Electrical and Control Engineering North University of China Taiyuan China School of Computer Science and Technology North University of China Taiyuan China School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350368604

The State of Health (SOH) of lithium-ion batteries is a critical parameter that mirrors the performance of their health monitoring systems. To achieve accurate prediction of SOH, we propose a novel prediction model. The model combines the advantages of variational mode decomposition (VMD) and transit search optimization- least squares support vector machine (TSOA-LSSVM). The constant-current charging time (CCCT), constant-current discharging time (CCDT), and Constant voltage charging time(CVCT) with Internal resistance(IR) are extracted as health indicators (HIs), and the correlations between HIs and SOH are evaluated by using Spearman correlation coefficient (spearman) respectively. With the VMD method, the most correlated HI is decomposed into multiple intrinsic modal functions (IMFs), and each IMF is fitted by the LSSVM. TSOA is used to obtain the most appropriate hyperparameters. The efficacy of the newly developed VMD-TSOA-LSSVM model is assessed by utilizing battery aging data sourced from the Center for Advanced Life Cycle engineering (CALCE). Its performance is then compared to that of the solo LSSVM, the VMD-LSSVM, and the VMD-TSOA-SVM methods. The laboratory outcomes demonstrate that the VMD-TSOA-LSSVM model outperforms other models in prediction and has better robustness and generalization ability. © 2024 IEEE.

关键词： Variational mode decomposition

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Multiple Robots Autonomous Task Planning Based on Improved Genetic Algorithm

Multiple Robots Autonomous Task Planning Based on Improved G...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Yang, Zhiyuan Zhao, Qingjie Lv, Xingchen Wang, Lei Liu, Wangwang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Control Engineering Beijing100190 China

ISBN: (纸本)9781665481090

This paper presents an autonomous task planning method for multiple collecting robots on improved genetic algorithm. Considering the limited capacity of collecting robots and the suitability of hopping robots in the microgravity environment of small celestial bodies, we developed a task planning model based on the Capacitated Vehicle Routing Problem (CVRP) and proposed an improved genetic algorithm to simultaneously allocate tasks and plan paths for multiple collecting robots. During robot movement, the A ∗ algorithm is employed for active obstacle avoidance when unknown obstacles are encountered. Experimental results demonstrated that our algorithm, can efficiently allocate tasks for the robot group, and each robot actively avoiding obstacles during movement. © 2024 IEEE.

关键词： Microrobots

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A fatigue assessment method based on attention mechanism and surface electromyography

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 112-120页

作者： Dang, Yukun Liu, Zitong Yang, Xixin Ge, Linqiang Miao, Sheng College of Computer Science & Technology Qingdao University Qingdao266071 China School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China School of Computer Science Columbus State University GA31907 United States Faculty of Environmental Engineering The University of Kitakyushu Kitakyushu808-0135 Japan

Surface electromyography (sEMG) signals can be used to quantitatively assess muscle fatigue, thereby directly and objectively reflecting the functional state of neuromuscular activity. Effective fatigue diagnosis can prevent muscle damage, thereby improving the safety of rehabilitation exercise. Traditional fatigue diagnosis has certain limitations, including strong subjectivity and poor accuracy. This paper designs a sEMG signals acquisition circuit and collects the sEMG signals of the upper limb biceps brachii and triceps brachii in the force-relaxation state in a dual-channel form. Muscle fatigue classification assessment using Dynamic Time Warping-K Nearest Neighbor (DTW-KNN) and three deep learning algorithms. The experimental results show that compared with traditional machine learning algorithms, deep learning algorithm can achieve higher accuracy and time efficiency. In addition, this study introduces an attention mechanism to dynamically and reasonably assign network weights to achieve high level feature learning. The Attention-Long Short-Term Memory (Attention Based LSTM) neural network achieves 93.5% assessment accuracy with a time overhead of only 3.73s, allowing for real-time assessment of muscle fatigue. © 2023 The Authors

关键词： Long short-term memory

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Toward quantum unmanned systems

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Science China(Technological Sciences) 2024年第7期67卷 2277-2280页

作者： CHENG ShuMing QI HongSheng SUN Yue LIU LiJun XIE ShaoRong REN XiaoQiang Department of Control Science and Engineering Tongji UniversityShanghai 200092China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Cipher Cloud Core Ltd. Beijing 100085China College of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041081China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444China

1 Quantum information technology Quantum information technology utilizes physical systems at the microscopic level, such as photon, atom, ion, and superconducting, to accomplish information-processing tasks that are impossible for the classical macroscopic world. During the past decade, significant process has been achieved in the pursuit of quantum technology into practical applications,generating great research interest from various domains, with the potential to radically change our information infrastructure [1–3].

关键词： quantum utilize generating

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Intelligent Maintenance Scheduling for 5G Networks with ECA-DenseNet-DQN 3

Intelligent Maintenance Scheduling for 5G Networks with ECA-...

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3rd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Yang, Mingye Li, Zhen Xu, Chuang Li, Chao International School Beijing University of Posts and Telecommunications Beijing China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798331529222

This paper presents an intelligent maintenance scheduling model for 5G networks using a deep reinforcement learning approach, specifically ECA-DenseNet-DQN. The proposed model optimizes the allocation of maintenance personnel by considering both travel and task completion times, aiming to minimize total task duration. Unlike traditional scheduling methods that often disregard skill levels and workloads of personnel, the model integrates Efficient Channel Attention (ECA) mechanisms into DenseNet to enhance feature extraction capabilities, thereby improving scheduling decisions. Simulation experiments demonstrate the model’s superior performance in reducing task completion time and achieving faster convergence compared to existing algorithms. ©2024 IEEE.

关键词： Scheduled maintenance

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