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7th International Conference on Electronics technology, ICET 2024

作者： Qing, Xian Qin, Kaiyu Li, Zhiqiang Shi, Mengji Lin, Boxian Dai, Yuehong University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu China National Laboratory on Adaptive Optics Chengdu610209 China

ISBN: (纸本)9798350363951

In this paper, the distributed optimization problem for networked multi-agent systems with unknown disturbances over signed graph is addressed. A novel robust fixed-time distributed optimization scheme is proposed. This scheme is divided into two components: a distributed observer-based optimization algorithm and a robust tracking controller. The distributed observer-based optimization algorithm makes the virtual state of each agent achieve bipartite consensus in fixed-time and obtain the optimal solution of the global convex objective function under the signed graph. The robust tracking controller is designed to eliminate the effect of external bounded disturbance, so that the actual state of the agent converges to the virtual state. The distributed optimization scheme can suppress the disturbances and obtain the optimal solution, and the convergence time is independent of the initial values of agents. Finally, some simulation results are given to verify the proposed fixed-time distributed optimization scheme. © 2024 IEEE.

关键词： Multi agent systems

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Research on dual-band fully tunable filter based on T-shaped dual-mode resonator 3

Research on dual-band fully tunable filter based on T-shaped...

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3rd International Conference on Electrical Engineering and control Science, IC2ECS 2023

作者： Wu, Yuanhang Feng, Lijun Zhang, Tianliang School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350382426

Combining two T-shaped resonators operating in different frequency bands, designing a dual-mode dual-band fully tunable filter indirectly couples parallel coupling lines into a feed structure, which makes the bandwidth independently tuning and central frequency and ensures the stability of the other passband in the process of tuning one passband. The spindle frequencies of the two passbands are 1.2 GHz and 1.7 GHz, and meanwhile, the bandwidth is 120 MHz and 100 MHz, respectively. The relative tuning range of the two bands is greater than 15%. The specific bandwidth tuning range is 60-180 MHz and 60-140 MHz. During the tuning process, the insertion loss is lower than 3 dB, and the return loss is clearly better than 10 dB. © 2023 IEEE.

关键词： Dual-mode dual-band tunable filter Odd-even mode analysis Tunable Filter

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Spatiotemporal-Enhanced Recurrent Neural Network for Network Traffic Prediction 28

Spatiotemporal-Enhanced Recurrent Neural Network for Network...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Chen, Zhiyong Lai, Junyu Zhu, Junhong Ma, Wanyi Gan, Lianqiang Xia, Tian School of Aeronautics Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350300482

Network traffic prediction can serve as a proactive approach for network resource planning, allocation, and management. Besides, it can also be applied for load generation in digital twin networks (DTNs). This paper focuses on background traffic prediction of typical local area networks (LANs), which is vital for synchronous traffic generation in DTN. Conventional traffic prediction models are firstly reviewed. The challenges of DTN traffic prediction are analyzed. On that basis, a spatiotemporal-enhanced recurrent neural network (RNN) based approach is elaborated to accurately predict the background traffic matrices of target LANs. Experiments compare this proposed model with four baseline models, including LSTM, CNN-LSTM, ConvLSTM, and PredRNN. The results turn out that the spatiotemporal-enhanced RNN model outperforms the baselines on accuracy. In particular, it can decrease the MSE of PredRNN more than 18%, with acceptable efficiency degradation. © 2023 IEEE.

关键词： Long short-term memory

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A Joint Longitudinal and Lateral Guidance Scheme for Reentry Gliding Phase of Hypersonic Vehicles

A Joint Longitudinal and Lateral Guidance Scheme for Reentry...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Wang, Ziyu Wu, Yunjie Duan, Jingqi State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Science and Technology on Aircraft Control Laboratory Beijing100191 China Large Aircraft Advanced Training Center Beihang University Beijing100191 China

ISBN: (纸本)9789811966125

A joint longitudinal and lateral guidance law design scheme is proposed for the reference trajectory tracking problem of the reentry gliding phase of hypersonic vehicles. With respect to the longitudinal and lateral motion model of the hypersonic vehicle, a longitudinal guidance law based on the Global Fast Terminal Sliding Mode (GFTSM) algorithm and a lateral guidance law based on the Extended State Observer (ESO) is designed respectively, which are combined to get the bank angle for the hypersonic vehicle and obtain the final guidance scheme. From theoretical analysis, it shows that the guidance scheme has the characteristics of global rapidity and finite-time convergence, and both longitudinal and lateral guidance law are robust to system uncertainties which are caused by certain parameter perturbation and external interference. The effectiveness and superiority of this joint guidance scheme compared to traditional guidance method are verified by simulation experiments using the specific gliding reentry model and reference trajectory of a hypersonic vehicle. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hypersonic vehicles

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Multi-Agent Deep Reinforcement Learning Based Computation Offloading Approach for LEO Satellite Broadband Networks 28

Multi-Agent Deep Reinforcement Learning Based Computation Of...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Lai, Junyu Liu, Huashuo Sun, Yusong Zhu, Junhong Ma, Wanyi Gan, Lianqiang School of Aeronautics & Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350300482

Conventional computation offloading approaches are originally designed for ground networks, and are not effective for low earth orbit (LEO) satellite networks. This paper proposes a multi-agent deep reinforcement learning (MADRL) algorithm for making multi-level offloading decisions in LEO satellite networks. Offloading is formulated as a partially observable Markov decision process based multi-agent decision problem. Each satellite as an agent either conducts a received task, forwards it to neighbors, or sends it to ground clouds based on its own policy. These agents are independent and their deep neural networks to make offloading decisions share identical parameter values and are trained by using the same replay buffer. A centralized training and distributed executing mechanism is adopted to ensure that agents can make globally optimized offloading decisions. Comparative experiments demonstrate that the proposed MADRL algorithm outperforms the five baselines in terms of task processing delay and bandwidth consumption with acceptable computational complexity. © 2023 IEEE.

关键词： Reinforcement learning

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Distributed Robust Fixed-Time Dynamic Average Consensus Protocol for Networked Agents 42

Distributed Robust Fixed-Time Dynamic Average Consensus Prot...

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42nd Chinese control Conference, CCC 2023

作者： Li, Weihao Shi, Mengji Ren, Zili Yue, Jiangfeng Lin, Boxian Qin, Kaiyu School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China The Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9789887581543

This paper seeks a robust dynamic average consensus protocol that makes the networked agents reach a common time-varying average state of multiple reference signals within an advanced preset time. It can be applied in fields such as multi-robot map merging and DC microgrid secondary control. Finite-time dynamic average consensus algorithms are studied in existing papers, while the convergence time is dependent on the initial states of the agents, so larger differences among their initial states may result in a slower convergence rate. Hence, a robust fixed-time dynamic average consensus scheme is presented in this paper. The settling time of the proposed scheme only relies on the parameters of the protocol and is independent of the initial values of agents. Specifically, the theoretical fixed-time convergence analysis is achieved by adopting the Lyapunov function, which indicates that dynamic average consensus can be reached within a fixed-time interval. Moreover, a damping term is added to the internal dynamics of agents to enhance the robustness of the protocol once the network interaction is changed. Finally, some numerical simulations are shown to verify the effectiveness of the proposed strategy. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Robust control

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An improved yield criterion characterizing the anisotropic and tension-compression asymmetric behavior of magnesium alloy

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Journal of Magnesium and Alloys 2022年第2期10卷 612-628页

作者： Zhigang Li Haifeng Yang Jianguang Liu Fu Liu School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijing100044PR China Automotive Research Institute of Beijing Automotive Co. Ltd.Beijing 101300China Beijing Key Laboratory of Civil Aircraft Structures and Composite Materials COMAC Beijing Aircraft Technology Research InstituteBeijing102211PR China Shanghai Aircraft Design and Research Institute Shanghai 201210PR China

A novel yield criterion based on CPB06 considering anisotropic and tension-compression asymmetric behaviors of magnesium alloys was derived and proposed(called M_CPB06).This yield criterion can simultaneously predict the yield stresses and the Lankford ratios at different angles(if any)under uniaxial tension,compression,equal-biaxial and equal-compression ***,in order to further describe the anisotropic strain-hardening characteristics of magnesium alloy,the proposed M_CPB06 criterion was further evolved to the M_CPB06ev model by expressing the parameters of the M_CPB06 model as functions of the plastic *** the model was developed,the stresses and Lankford ratios of AZ31B and ZK61M magnesium alloys at different angles under tensile,compressive and through-thickness compressive conditions were used to calibrate the M_CPB06/M_CPB06ev and the existing CPB06ex2 *** results reveal that compared with the CPB06ex2 yield criterion with equal quantity of coefficients,the M_CPB06 criterion exhibits certain advancement,and meanwhile the M_CPB06ev model can relatively accurately predict the change of the yield locus with increase of the plastic ***,the M_CPB06ev model was developed through UMAT in *** element simulations using the subroutine were conducted on the specimens of different angles to the rolling direction under tension and *** results were highly consistent with the experimental results,demonstrating a good reliability and accuracy of the developed subroutine.

关键词： Magnesium alloy Anisotropy Tension-compression asymmetry Improved yield criterion UMAT

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Research on Modeling Methods for General Characteristics of Cyber-Physical Systems

Research on Modeling Methods for General Characteristics of ...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Zelin Li Yazhou Yue Yong Wang Fangfang Wu Dantao Zhang Xiangqiao Li National Key Laboratory of Science and Technology on Aircraft Control Avic Xi’An Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

As a complex system that integrates computer science, network communication, and physical processes, it has shown huge potential in many fields. To effectively design, analyze and optimize CPS (Cyber-Physical Systems), establishing appropriate unified methods for modeling, simulation, and analysis of CPS is of crucial importance. In this paper, the concepts and characteristics of CPS are introduced, emphasizing the nature of its interdisciplinary and heterogeneous, as well as the extensive application of aviation, aerospace, vehicle, and other fields. And then existing CPS modeling methods are systematically reviewed and summarized from three perspectives: discrete modeling, continuous modeling, and advanced language modeling methods. The challenges and technical difficulties in CPS modeling process are identified, and three feasible solutions are proposed. The proposed solutions could contribute to the further study of CPS modeling.

关键词： Computer science Analytical models Automation Computational modeling Cyber-physical systems Complex systems

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Robust Multi-agent Federated Reinforcement Learning for Task Offloading 7th

Robust Multi-agent Federated Reinforcement Learning for Task...

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7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Yan, Dibao Wang, Yongfeng Hou, Wenjing Song, Huanhuan Wen, Hong Ma, Wendi Sun, Fan School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. This algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. This paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node. © The Author(s) 2025.

关键词： Federated learning

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Adaptive Communication Spectrum Sensing Algorithm Based on Energy Detection 7th

Adaptive Communication Spectrum Sensing Algorithm Based on E...

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7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Shi, Weihong Tang, Tao Zhao, Runhui Wen, Hong Feng, Xuewei School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent loT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

Based on the traditional energy detection algorithm, an adaptive communication spectrum sensing algorithm based on energy detection is proposed for signals whose noise power is difficult to be estimated accurately. The algorithm takes into account the problem of large error in calculating the energy detection judgment threshold by noise power when the estimated value of noise power is inaccurate, and is able to perform adaptive communication spectrum sensing for signals with less a priori information and noise power that cannot be estimated accurately. Simulation results show that the adaptive communication spectrum sensing algorithm based on energy detection can effectively improve the performance of spectrum sensing for signals, and the algorithm can effectively detect and sense signals under the condition of reasonable setting of judgment coefficients. © The Author(s) 2025.

关键词： Adaptive Cognitive radio Energy detection Spectrum sensing

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