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Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Xinyu Liu Mingrui Hao Yu Fan Yan Zhen Yutao Zhuang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory

In this paper, a second-order fully actuated system model is derived for a six degree of freedom aircraft model with control coupling. Then a three channel joint optimal attitude control method of roll, yaw and pitch is designed on high-order fully actuated system theory. Through mathematical simulation of this controller and the traditional PID controller, it is shown that the PID control method has a great shortage in the aircraft with control coupling, and the fully actuated system theory control method can well solve the problem of aircraft coupling, achieve the stable tracking of aircraft attitude command, and has better rapidity.

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Random Coefficient-Moving Product Based Wireless Key Generation

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China Communications 2025年第2期22卷 199-212页

作者： Lu Xinjin Lei Jing Li Wei College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003China China South Industries Group Corporation Zhi Yuan Institute310012China

The physical layer key generation technique provides an efficient method,which utilizes the natural dynamics of wireless ***,there are some extremely challenging security scenarios such as static or quasi-static environment,which lead to the low randomness of generated ***,the coefficients of the static channel may be dropped into the guard space and discarded by the quantization approach,which causes low key generation *** tackle these issues,we propose a random coefficient-moving product based wireless key generation scheme(RCMP-WKG),where new random resources with remarkable fluctuations can be obtained by applying random coefficient and by moving product on the legitimate ***,appropriate quantization approaches are used to increase the key generation ***,the security of our proposed scheme is evaluated by analyzing different attacks and the eavesdropper’s mean square error(MSE).The simulation results reveal that the proposed scheme can achieve better performances in key capacity,key inconsistency rate(KIR)and key generation rate(KGR)compared with the prior works in static ***,the proposed scheme can deteriorate the MSE performance of the eavesdropper and improve the key generation performance of legitimate nodes by controlling the length of the moving product.

关键词： moving product random coefficient static environment wireless key generation

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Scribble-Supervised Video Object Segmentation

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 339-353页

作者： Peiliang Huang Junwei Han Nian Liu Jun Ren Dingwen Zhang Brain and Artificial Intelligence Laboratory School of AutomationNorthwestern Polytechnical UniversityXi’an 710072China IEEE Department of Engagement Services Mohamed Bin Zayed University of Artificial IntelligenceAbuDhabiUnited Arab Emirate Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory BeijingChina

Recently,video object segmentation has received great attention in the computer vision *** of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to *** tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual ***,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and *** address this issue,this paper introduces two novel elements to learn the video object segmentation *** first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and *** other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training *** evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-*** first generate the scribble annotations from the original per-pixel ***,we train our model and compare its test performance with the baseline models and other existing *** experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.

关键词： Convolutional neural networks(CNNs) scribble self-attention video object segmentation weakly supervised

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Exploration of the application of optimisation algorithm using stochastic gradient descent method in satellite resource allocation

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Applied Mathematics and Nonlinear sciences 2024年第1期9卷

作者： Zhao, Demin Xiong, Wei Shi, Jinjing Science and Technology on Complex Electronic System Simulation Laboratory University of Aerospace Engineering Beijing101400 China Dfh Satellite CO. Ltd Beijing100080 China North Automatic Control Technology Institute Shanxi Taiyuan030006 China

With the popularization and development of communication technology, the resource allocation problem of satellites has become a hot research topic. This study proposes a model for satellite resource allocation through the development of a beam mobilization system model and problem modeling. Then, on the basis of the linear stochastic gradient descent method, the classification accuracy of the algorithm is improved by adjusting the algorithm-solving method. Then its weight assignment method is improved to get the improved weighted linear stochastic gradient descent method. Using the optimized weighted linear stochastic gradient descent method to solve the satellite resource allocation problem from the perspective of the original problem, a model based on IWLSGD is designed and tested for performance. Through simulation experiments, the beam allocation service value of the satellite resource allocation model in this paper is 193, which is 4.32% and 3.21% higher than that of LSGD and WLSGD, respectively, and iterative convergence is faster, and its operation time and service value performs the best under different numbers of communication time slots. The system revenue, system access success rate, and system satisfaction under the interference environment between LEO and GEO are maintained at 15208, 0.8~1.0, and 85%, and keep around 7500, 1, and 75% under 5G base station interference. The satellite resource allocation model in this paper can effectively improve the utilization efficiency of communication resources and better adapt to dynamically changing interference scenarios. © 2024 Demin Zhao et al., published by Sciendo.

关键词： Resource allocation

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基于晶体塑性的辐照金属材料局部化颈缩和吕德斯带的模拟研究

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Acta Mechanica Sinica 2023年第4期39卷 132-146页

作者：付家琪刘文斌隋昊男程杨洋段慧玲 State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceThe Beijing Innovation Center for Engineering Science and Advanced TechnologyCollege of EngineeringPeking UniversityBeijing100871China Key Laboratory of High Energy Density Physics Simulation Center for Applied Physics and Technologyand Inertial Fusion Sciences and ApplicationsCollaborative Innovation Center of Ministry of EducationPeking UniversityBeijing100871China

金属材料中,颈缩失稳通常由表面缺陷或材料不均匀性等潜在扰动引起并随着微结构的演化进一步发展.本文将构型扰动法与考虑辐照效应的晶体塑性本构关系相结合,模拟了辐照前后局部化颈缩和吕德斯带等现象.通过考虑模拟局部化变形中扰动的... 详细信息

金属材料中,颈缩失稳通常由表面缺陷或材料不均匀性等潜在扰动引起并随着微结构的演化进一步发展.本文将构型扰动法与考虑辐照效应的晶体塑性本构关系相结合,模拟了辐照前后局部化颈缩和吕德斯带等现象.通过考虑模拟局部化变形中扰动的必要性,发现了外在的几何缺陷和内禀的微结构演化在颈缩过程中的竞争关系.此外,以纯金属铜和奥氏体不锈钢为例研究了辐照硬化效应对颈缩失稳的影响.结果表明,流动应力的增加和功硬化能力的降低是均匀延伸率下降的原因,辐照效应导致的织构软化作用可以忽略.为了解释辐照后低碳钢下屈服点和吕德斯应变同时增加的现象,一种位错脱钉扎和位错与辐照缺陷的交互作用相耦合的塑性变形机制被提出并结合修正的流动方程,很好地模拟出包含屈服下降、应力平台、应变扩展和应变硬化等特征的吕德斯带现象.其中,位错与辐照缺陷的相互作用也可以解释辐照后纯金属材料中发现的扩散颈缩现象.我们的模拟结果既阐明了几何缺陷在颈缩失稳中的重要作用,也为辐照金属中常见的宏观塑性失稳现象提供了清晰的物理图像.

关键词：吕德斯带金属材料奥氏体不锈钢晶体塑性下屈服点流动应力几何缺陷均匀延伸率

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Research and Implementation of Model Engineering Environment Integration Based on OpenMBEE 21st

Research and Implementation of Model Engineering Environment...

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21st Asia simulation Conference, AsiaSim 2022

作者： Xue, Junjie Zhou, Junhua Shi, Guoqiang Feng, Chaoqun Xu, Lin Liu, Penghua Quan, Hongyan Engineering Research Center Beijing Simulation Center Beijing100854 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing100854 China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing100854 China Yunnan Chensheng Tendering Consulting Co. Ltd. Kuming650102 China School of Computer Science and Technology East China Normal University Shanghai200062 China

ISBN: (纸本)9789811991974

Aiming at the problem of integrating open tools in a user development environment in complex product design, an integration strategy of model engineering environment based on OpenMBEE is studied. It is based on the technologies of user identity unified authentication and user interaction data collaboration. Specifically, in the solution of unified identity authentication on cross-platform, the extended controller method is provided based on the message queue information flow mechanism. Then, the separation of business logic, data information, and interactive view of user unified authentication are realized, on the basis, that the function of unified identity authentication is realized by using the Software Development Kit (SDK) of OpenMBEE. Besides, in the study of user interaction data collaboration technology, three-layer collaboration mechanisms are proposed, they are the presentation layer collaboration, the business layer collaboration, and the persistence layer collaboration. When the jQuery programming framework is integrated into the bootstrap front-end framework, combing with the OpenMBEE back-end server interface, the collaboration of user interaction data is realized. Through experiments and project practice analysis, the user unified authentication strategy and user interactive collaboration strategy are tested respectively for proving the effectiveness and feasibility of the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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A Multimodal Image Matching Algorithm Based on Structure Feature 1

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Wang, Xing Zhao, Jiajia su, Ang Guan, Banglei Dong, Jing Han, Songlai Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China Research Institution of Aerospace Technology Central South University Changsha410083 China

ISBN: (数字)9789811966132

ISBN: (纸本)9789811966125

Multimodal image matching is a crucial technique for aircraft navigation in GPS denied environment. However, the nonlinear radiometric difference between multimodal images and significant image noise make this task considerably challenging. A multimodal image registration algorithm build on structural features is presented. The method can also accurately extract structural features in the case of severe noise distortion. Besides, A similarity measure method based on parallelism is presented for adapting nonlinear grayscale distortion. The evaluation show that the algorithm has significant superiority over other state-of-the-art algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

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Energy Engineering 2024年第6期121卷 1671-1695页

作者： Guanfu Wang Yudie Sun Jinling Li Yu Jiang Chunhui Li Huanan Yu He Wang Shiqiang Li State Grid Liaoning Electric Power Co. Ltd.Liaoyang Power Supply CompanyLiaoyang111000China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin132012China Jilin Northeast Electric Power University Science and Technology Development Co. Ltd.Jilin132012China

Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network ***, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.

关键词： Self-adaptive the uncertainty of sources and load deep reinforcement learning dynamic economic scheduling

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Visual Explanation of Object Detectors via Saliency Maps 18

Visual Explanation of Object Detectors via Saliency Maps

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Xiao, Jin Liu, Wenrui Hu, Xiaoguang Jiang, Hao Wang, Weipeng Yahang, Li Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Beihang University Beijing China The Third Academy of Casic Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350312201

In recent years, rapid progress of deep learning has resulted in object detection algorithms based on deep neural networks nearly completely replacing traditional methods. This shift has led to significant improvements in both accuracy and speed of object detection. However, deep neural networks that support decision-making inobject detection are complex black boxes with hidden internal logic and operations. This lack of transparency results in deep learning models being unexplainable, which limits their practical deployment. In this paper, we analyze the current state and future trends of interpretable technology in object detection and propose an effective object detection interpretable algorithm. This algorithm aims to explain the underlying logic of AI decision-making and provide support for the high performance and credibility of object detection algorithms based on deep neural networks through quantitative evaluation indicators. © 2023 IEEE.

关键词： Object detection

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Agent Optimization Decision-Making Method Based on Self-Play

Agent Optimization Decision-Making Method Based on Self-Play

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IEEE International Conference on Unmanned systems (ICUS)

作者： Chunyi Duan Rongling Xiong Luyou Zhang Huaming Ran Qing Yang National Key Laboratory of Complex Aviation System Simulation China Electronics Technology Group Corporation Tenth Research Institute Chengdu China National Key Laboratory of Complex Aviation System Simulation China Electronic Technology Group Corporation Tenth Research Institute Chengdu China Unit 93216 People's Liberation Army of China Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

Aiming at the problem that the traditional deep reinforcement learning algorithm has poor generalization ability of generating strategies when solving complex game problems such as real-time strategy games, an agent strategy optimization method based on the combination of expert knowledge and self-play is proposed. Firstly, the opponent strategy of the agent is designed based on expert knowledge, and the corresponding strategy is generated by the proximal policy optimization algorithm (PPO), and the generated strategy is continuously put into the opponent strategy pool to provide a variety of opponent strategies for the agent in the training process. Through continuous iterative confrontation, the agent's autonomous decision-making ability in the face of different opponent strategies is improved. The simulation results show that compared with the traditional deep reinforcement learning algorithm, the introduction of the self-play training framework can significantly improve the average confrontation rate of the agent strategy, improve the robustness and generalization ability of the agent confrontation strategy, guide the agent to learn more effective tactical strategy, and achieve the adversarial advantage of “Priority first enemy discovery, Priority enemy launch”.

关键词： Training simulation Decision making Optimization methods Games Deep reinforcement learning Market research Robustness Real-time systems Iterative methods

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