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检索条件"机构=Key Laboratory of Software Engineering for Complex System"
1921 条 记 录,以下是91-100 订阅
排序:
An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations
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Numerical Mathematics(Theory,Methods and Applications) 2023年 第2期16卷 298-322页
作者: Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China
Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a ... 详细信息
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An efficient adaptive space partitioning algorithm for electromagnetic scattering calculation of complex 3D models
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Journal of systems engineering and Electronics 2021年 第5期32卷 1071-1082页
作者: HUANG Minjie ZHOU Yaoming WANG Yongchao LIU Zhongtie School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China Key Laboratory of Complex Aviation System Simulation Beijing 100076China
The space partitioning algorithm based on the rounding and addressing operations has been proved to be an efficient space partitioning algorithm with the potential for real-time *** improvement on this kind of space p... 详细信息
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Non-convex Optimization Based On Deep Koopman Operator  38
Non-convex Optimization Based On Deep Koopman Operator
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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023
作者: Guo, Jun Jia, Yubin Yong, Panxiao Zhou, Jun Liu, Zhimin Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China
Non-convex optimization problems are prevalent in various fields, and finding the global optimal solution for such problems remains challenging. This paper proposes an optimization method based on deep Koopman operato... 详细信息
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Data-Driven Frequency Regulation Method for Multi-Terminal HVDC Grids  38
Data-Driven Frequency Regulation Method for Multi-Terminal H...
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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023
作者: Yong, Panxiao Jia, Yubin Guo, Jun Zhou, Jun Liu, Zhimin Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China
High voltage direct current (HVDC) lines allow low-loss transmission over long distances. The multi-terminal HVDC network consists of multiple terminals connected by HVDC lines, and each area shares the frequency rese... 详细信息
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Boosting Adversarial Training with Learnable Distribution
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Computers, Materials & Continua 2024年 第3期78卷 3247-3265页
作者: Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China
In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the fe... 详细信息
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Measurement-based Path Loss Prediction and Analysis in Water Scenarios
Measurement-based Path Loss Prediction and Analysis in Water...
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2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024
作者: Li, Hanpeng Chen, Xiaomin Li, Hangang Ma, Jian Ye, Xuchao Song, Lixin Zhang, Taotao Zhong, Weizhi Zhu, Qiuming Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Aviation Key Laboratory of Science and Technology On Aero Electromechanical System Integration Nanjing211106 China State Key Lab. of Complex Electromagnetic Environment Effects On Electronics and Information System Luoyang471003 China
Unmanned aerial vehicles (UAVs) have enabled the unmanned management of water areas. Path loss (PL) serves as a critical factor that affects the UAV communication quality. In this work, a PL model for UAV channel in w... 详细信息
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Formal Verification Based Synthesis for Behavior Trees  9th
Formal Verification Based Synthesis for Behavior Trees
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9th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2023
作者: Hong, Weijiang Chen, Zhenbang Li, Minglong Li, Yuhan Huang, Peishan Wang, Ji College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China
Behavior trees (BTs) have been extensively applied in the area of both computer games and robotics, as the control architectures. However, the construction of BTs is labor-expensive, time-consuming, and even impossibl... 详细信息
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Forming Circle Formations of Multi-agent systems Under Actuator Faults  5th
Forming Circle Formations of Multi-agent Systems Under Actu...
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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021
作者: Liu, Jiajun Ru, Xinfeng Xia, Weiguo Wang, Chen China National Aeronautical Radio Electronic Research Institute and National Key Laboratory of Science and Technology on Avionics System Integration Shanghai China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian China National Engineering Research Center of Software Engineering and the State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China
A distributed fault-tolerant formation control law is designed in this paper for multi-agent systems under external disturbances and actuator faults including time-varying loss of effectiveness faults. The initial pos... 详细信息
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Challenges of Using Pre-trained Models: The Practitioners' Perspective  31
Challenges of Using Pre-trained Models: The Practitioners' P...
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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024
作者: Tan, Xin Li, Taichuan Chen, Ruohe Liu, Fang Zhang, Li School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Economics and Management Beihang University Beijing China
The challenges associated with using pre-trained models (PTMs) have not been specifically investigated, which hampers their effective utilization. To address this knowledge gap, we collected and analyzed a dataset of ... 详细信息
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An Approach for Upper Limb Movement Intention Recognition Using EEG and sEMG Fusion based on the MCPSA-CIIM  5
An Approach for Upper Limb Movement Intention Recognition Us...
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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023
作者: Zhang, Weiming Zhang, Xiaodong Xu, Cheng Zhou, Guchuan Zhang, Teng Wang, Yu Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robot School of Mechanical Engineering Shaanxi Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China
The upper limb motion intention recognition method based on electroencephalography (EEG) and surface electromyography (sEMG) fusion has achieved significant results in fields such as prosthetic control. However, most ... 详细信息
来源: 评论