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Multi-Material Topology Optimization of 2D Structures Using Convolutional Neural Networks

Computer Modeling in Engineering & sciences 2024年第8期140卷 1919-1947页

作者： Jiaxiang Luo Weien Zhou Bingxiao Du Daokui Li Wen Yao College of Aerospace Science and Engineering National University of Defense TechnologyChangsha410073China Defense Innovation Institute Chinese Academy of Military ScienceBeijing100071China Intelligent Game and Decision Laboratory Chinese Academy of Military ScienceBeijing100071China

In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural ***,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still ***,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO *** framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any *** performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction ***,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification *** the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most ***,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data ***,factors such as the domain size and the material property have minimal impact on the accuracy.

关键词： Multi-material topology optimization convolutional neural networks deep learning finite element analysis

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Performance Evaluation and Transmitter Optimization for AN-Aided FH Communication Against Time Misalignment

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China Communications 2025年第3期22卷 86-103页

作者： Song Changqing Lin Lang Qin Lizhi Xiao Fei Zhao Hongzhi Shao Shihai National Key Laboratory of Wireless Communications University of Electronic Science and Technology of ChinaChengdu 611731China The Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing 100089China The Thirtieth Research Institude of China Electronic Science and Technology Group Corporation Chengdu 610093China

To guarantee secure communication against eavesdropping and malicious attack,an artificial noise(AN)-aided frequency-hopping(FH)architecture is adopted in this *** the inevitable time misalignment between the received signal and locally reconstructed AN will deteriorate the AN cancellation performance,yielding significant secrecy degradation at the FH *** view of this,first,the AN cancellation performance under time misalignment is evaluated via signal to AN-plus-noise ratio,and the system secrecy is analyzed via secrecy ***,to mitigate the performance degradation raised by time misalignment,the transmitting power allocation scheme for AN and confidential signal(CS)is optimized,and the optimal hopping period is ***,the obtained conclusions in both the performance evaluation and transmitter optimization sections hold no matter whether the eavesdropper can realize FH synchronization or *** verify that time misalignment will raise non-negligible performance ***,the power ratio of AN to CS should decrease as time misalignment increases,and for perfect time synchronization,the transmitting power of AN and CS should be *** addition,a longer hopping period is preferred for secrecy enhancement when time misalignment gets exacerbated.

关键词： artificial noise(AN)cancellation hopping period design power allocation time misalignment

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Maximum ladle shell temperature prediction based on GABP neural network

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International Journal of Wireless and Mobile Computing 2023年第2期24卷 120-126页

作者： Sun, Ying Huang, Peng Tao, Bo Yun, Juntong Zhao, Guojun Liu, Xin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China

intelligent manufacturing is the main development trend of today's manufacturing industry, and talents are the first resource. Through the analysis of the current situation of cultivating talents in mechanical engineering in colleges and universities, it is found that most students of this major have difficulty in involving in knowledge of other fields outside their specialties, and their knowledge structure is relatively single. In response to the above problems, this paper proposes the training mode of multidisciplinary cross-integration of talents in mechanical engineering, which is analysed through the study of maximum temperature prediction of steel ladle shell. The BP neural network based on the improved genetic algorithm is trained on the experimental data samples to achieve the maximum temperature prediction of ladle shell under different thickness combinations of insulation layer, safety layer and working layer. By learning the knowledge of target prediction, students' overall development is promoted. © 2023 Inderscience Enterprises Ltd.

关键词： Forecasting

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A Novel Principal Component Analysis Algorithm for Fault Detection

A Novel Principal Component Analysis Algorithm for Fault Det...

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第43届中国控制会议

作者： Wang Shenhang Tian Kun Zheng Wei Liu Mengyu National Key Laboratory of Science and Technology on Aerospace Intelligent Control

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In modern industry,fault detection is very important in high-cost and safety-critical *** component analysis(PCA) is an effective data-driven approach which is widely used in this *** PCA is time-invariant,which often lead to false *** order to overcome the false alarms,recursive PCA algorithms have been proposed for adaptive ***,the amount of calculation of the algorithm is very great because of frequently updated *** this paper,a novel recursive PCA method is proposed for solving the question by introducing the standard residual *** standard residual method serves as an indicator of the level of consistency among data,which is used to decide whether or not to update the PCA *** proposed method is applied to fault detection and experiment results demonstrate the effectiveness of the method.

关键词： Principal component analysis fault detection standard residual method

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Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays:Optimized Algorithm Design and Boundedness Analysis

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Journal of Systems science & Complexity 2023年第4期36卷 1423-1442页

作者： HU Jun HU Zhibin DONG Hongli LIU Hongjian School of Automation Harbin University of Science and TechnologyHarbin 150080China Department of Mathematics Harbin University of Science and TechnologyHarbin 150080China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and TechnologyHarbin 150080China Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318China School of Mathematics-Physics and Finance Anhui Polytechnic UniversityWuhu 241000China

This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RSDs is modeled by a set of random variables with certain statistical *** addition,the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering *** aim of the addressed issue is to propose a DRFF scheme for MNSSs such that,for both RSDs and estimator gain perturbations,certain upper bounds of estimation error covariance(EEC)are given and locally minimized at every sample *** the light of the obtained local filters,a new DRFF algorithm is developed via the matrix-weighted fusion ***,a sufficient condition is presented,which can guarantee that the local upper bound of the EEC is ***,a numerical example is provided,which can show the usefulness of the developed DRFF approach.

关键词： Distributed resilient fusion filtering matrix-weighted fusion nonlinear time-varying systems random sensor delays

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Dynamic Event-Triggered Consensus control for Input Constrained Multi-Agent Systems With a Designable Minimum Inter-Event Time

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 649-660页

作者： Meilin Li Yue Long Tieshan Li Hongjing Liang C.L.Philip Chen the School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China IEEE the Yangtze Delta Region Institute University of Electronic Science and Technology of ChinaHuzhou 313000 the Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing 100089China the School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006

This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first ***,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale *** is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event *** addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable ***,the results are extended to regional consensus of the MASs with the bounded control *** simulations show the effectiveness of the proposed approach.

关键词： Constrained input designable minimum inter-event time directed communication topology dynamic event-triggered mechanism MASs consensus control

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Mathematical Modeling of GM (1, 1) Polymorphic Management Based on Dormant System

Mathematical Modeling of GM (1, 1) Polymorphic Management Ba...

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2022 International Conference on Applied Mathematics and Digital Simulation, AMDS 2022

作者： Zhang, Ying Peng, Di Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing100854 China China Academy of Launch Vehicle Technology Beijing100076 China

In this paper, a GM(1, 1) polymorphic management prediction algorithm based on a dormant system is proposed, which first obtains the optimal data quantity m through simulation, and then the data used is forecasted only once, and the input data will be updated to the latest data immediately after the prediction is completed, and then the parameter estimates will be recalculated for the next prediction. The simulation results show that the MRE value of the optimized GM (1, 1) is 0.090, which has much better MRE values for GM (1, 1) algorithms that are not optimized. At the same time, other commonly used algorithms in the industry are compared horizontally. In contrast, the optimized GM (1, 1) algorithm responds quickly, has high prediction accuracy, short prediction time, and consumes fewer resources. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Cooperative Advantage Actor-Critic Reinforcement Learning for Multiagent Pursuit-Evasion Games on Communication Graphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6509-6523页

作者： Meng, Yizhen Liu, Chun Wang, Qiang Tan, Longyu Shanghai Aerospace Control Technology Institute Shanghai201109 China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai201109 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Shanghai University Institute of Artificial Intelligence Shanghai200444 China Northwestern Polytechnical University College of Astronautics Xi'an710072 China

This article investigates the distributed optimal strategy problem in multiagent pursuit-evasion (MPE) games, striving for Nash equilibrium through the optimization of individual benefit matrices based on observations. To this end, a novel collaborative control scheme for MPE games using communication graphs is proposed. This scheme employs cooperative advantage actor-critic (A2C) reinforcement learning to facilitate collaborative capture by pursuers in a distributed manner while maintaining bounded system signals. The strategy orchestrates the actions of pursuers through adaptive neural network learning, ensuring proximity-based collaboration for effective captures. Meanwhile, evaders aim to evade collectively by converging toward each other. Through extensive simulations involving five pursuers and two evaders, the efficacy of the proposed approach is demonstrated, and pursuers seamlessly organize into pursuit units and capture evaders, validating the collaborative capture objective. This article represents a promising step toward effective and cooperative control strategies in MPE game scenarios. © 2024 IEEE.

关键词： Reinforcement learning

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Infrared Dim Small Target Detection Algorithm Based on Adaptive Attention Transformer Network

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Journal of Shanghai Jiaotong University (science) 2025年 1-13页

作者： Ding, Leqi Xia, Yilin Wang, Biyun Cai, Yunze Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China National Key Laboratory of Air-based Information Perception and Fusion Henan Luoyang471009 China Shanghai Institute of Aerospace Control Technology Infrared Detection Technology R & D Center of China Aerospace Science and Technology Corporation Shanghai201109 China

This paper presents a novel algorithm for detecting infrared dim small targets in complex backgrounds by integrating both global and local feature information. The proposed method employs an adaptive attention transformer network which addresses challenges associated with computational complexity and information redundancy in traditional self-attention mechanisms. A global context aggregation module is designed to address the loss of small target features in high-level feature maps. This module compensates for spatial distribution information during the multi-scale feature fusion process, ensuring the preservation of target features. Comparison experiments were conducted on two datasets, NUAA-SIRST and IRSTD-1k. The proposed algorithm achieved 0.784 for mIoU and 0.878 for F-measure on NUAA-SIRST, 0.632 for mIoU and 0.807 for F-measure on IRST-1k, all of which are the highest values. The proposed algorithm shows a significant improvement over other existing detection algorithms, demonstrating its capability to accurately extract both local and global features of targets with high recall rates and low false alarm rates across various scenarios. The ablation study results highlight the significant improvements contributed by the key modules to the proposed algorithm. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： A adaptive attention global context aggregation module infrared dim small target detection TP391.4 Transformer network

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Identification and compensation of suspension disturbance for magnetically levitated rotary joints 18

Identification and compensation of suspension disturbance fo...

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

作者： Wang, Yingguang Zhang, Jiyang Fan, Yahong Lu, Ming Luo, Ruizhi Beijing Institute of Control Engineering Science and Technology on Space Intelligent Control Laboratory Beijing China

ISBN: (纸本)9798350312201

For the multi-degree-of-freedom harmonic disturbance force/moment of the magnetically levitated rotor, a harmonic disturbance/disturbance force suppression method based on the eigenfrequency disturbance observer is proposed in this paper. First, a multi-freedom harmonic disturbance force/moment mechanism analysis is performed and its source is located. Then, a multi-degree-of-freedom eigenfrequency disturbance observer based on an adaptive discriminator is designed for closed-loop identification of the eigenfrequency disturbance forces of the magnetically levitated rotor by controlling its steady speed rotation. Then, the open-loop compensation current of each channel magnetic bearing is solved according to the multi-degree-of-freedom eigenfrequency perturbation observation results, so that the open-loop compensation electromagnetic force/moment and the perturbation force/moment form a balanced force/moment. Finally, tests were conducted on the rotating joint products, and the test results showed that the method can reduce the amount of magnetic levitation rotor levitation control fluctuation by more than 90% and improve the levitation stability by one order of magnitude. © 2023 IEEE.

关键词： Magnetic levitation

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