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arXiv 2024年

作者： Ma, Xinyue Wang, Chenxing The School of Automation The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

3D single-pixel imaging (SPI) is a promising imaging technique that can be flexibly applied to various wavebands. The main challenge in 3D SPI is that the calibration usually requires a large number of standard points as references, which are tricky to capture using single-pixel detectors. Conventional solutions involve sophisticated device deployment and cumbersome operations, resulting in hundreds of images needed for calibration. In our work, we construct a Calibration Field (CaliF) to efficiently generate the standard points from one single image. A high accuracy of the CaliF is guaranteed by the technique of deep learning and digital twin. We perform experiments with our new method to verify its validity and accuracy. We believe our work holds great potential in 3D SPI systems or even general imaging systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Calibration

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A Data-Driven Modeling and Motion control of Heavy-Load Hydraulic Manipulators via Reversible Transformation

arXiv

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arXiv 2024年

作者： Ma, Dexian Liu, Yirong Liu, Wenbo Zhou, Bo Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education Nanjing210096 China

This work proposes a data-driven modeling and the corresponding hybrid motion control framework for unmanned and automated operation of industrial heavy-load hydraulic manipulator. Rather than the direct use of a neural network black box, we construct a reversible nonlinear model by using multilayer perceptron to approximate dynamics in the physical integrator chain system after reversible transformations. The reversible nonlinear model is trained offline using supervised learning techniques, and the data are obtained from simulations or experiments. Entire hybrid motion control framework consists of the model inversion controller that compensates for the nonlinear dynamics and proportional-derivative controller that enhances the robustness. The stability is proved with Lyapunov theory. Co-simulation and Experiments show the effectiveness of proposed modeling and hybrid control framework. With a commercial 39-ton class hydraulic excavator for motion control tasks, the root mean square error of trajectory tracking error decreases by at least 50% compared to traditional control methods. In addition, by analyzing the system model, the proposed framework can be rapidly applied to different control plants. Copyright © 2024, The Authors. All rights reserved.

关键词： Industrial manipulators

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Study on Self-Organizing Aggregation Method for Massive Flexible Resources and Its Simulation 4

Study on Self-Organizing Aggregation Method for Massive Flex...

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4th International Conference on Advanced Electrical and Energy systems, AEES 2023

作者： Yin, Changwei Ji, Wenlu Ye, Ting Mao, Tingfeng Zhang, Kaifeng Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (纸本)9798350342659

Distributed flexible resources are developing in the trend of sea quantization. In this situation, the existing resource aggregation methods are facing bottlenecks, and it is difficult to fully exploit the regulation potential of massive flexible resources. In order to achieve efficient and flexible aggregation in the scenario of massive resources, this paper proposes a self-organizing aggregation method for flexible resources based on the social network and simulates the aggregation process under the proposed method based on the multi-agent simulation platform NetLogo. Firstly, this paper outlines the idea of the self-organizing aggregation method for flexible resources, which projects flexible resources from the physical space to the social network space, and realizes flexible aggregation through the interactive attraction between resources in the social network. Secondly, this paper establishes a simulation model for flexible resource self-organizing aggregation based on the NetLogo platform. The model defines four types of agents involved in the self-organizing aggregation process and provides detailed specifications for their attributes and interaction rules. Finally, simulation experiments are designed to verify the rationality of the self-organizing aggregation method and simulation model proposed in this paper. The simulation results show that, following the idea of self-organizing aggregation, massive flexible resources can autonomously and flexibly aggregate through the interaction attraction in the social network due to the incentive of task gain, demonstrating the effectiveness and rationality of the self-organizing aggregation method and the simulation model proposed in this paper. © 2023 IEEE.

关键词： Multi agent systems

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Pager Explosion:Cybersecurity Insights and Afterthoughts

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2359-2362页

作者： Chuan Sheng Wanlun Ma Qing-Long Han Wei Zhou Xiaogang Zhu Sheng Wen Yang Xiang Fei-Yue Wang Swinburne University of Technology Hawthorn VIC 3122Australia IEEE the School of Computer and Mathematical Sciences the University of AdelaideAdelaideSouth Australia 5005Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

PAGER EXPLOSION:THE *** 2024 Lebanon pager explosions represent one of the most unexpected and devastating technological incidents in recent *** September 17 and 18,2024,thousands of pagers and walkie-talkies exploded simultaneously across Lebanon and parts of Syria,resulting in 42 deaths and more than 3500 *** handheld communication devices,previously regarded as secure and low-profile,were rigged with concealed explosives and remotely triggered by attackers.

关键词： injuries thoughts explosion

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Consensus control of second-order stochastic discrete-time multi-agent systems without velocity transmission

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control Theory and Technology 2023年第4期21卷 591-601页

作者： Zi-Xuan Wang Xiaofeng Zong Ling-Jie Gai School of Automation China University of GeosciencesWuhan430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems WuhanHubeiChina Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhanHubeiChina

This article studies the almost-sure and the mean-square consensus control problems of second-order stochastic discrete-time multi-agent systems with multiplicative ***,a control law based on the absolute velocity and relative position information is ***,considering the existence of multiplicative noises and nonlinear terms with Lipschitz constants,the consensus control problem is solved through the use of a degenerated Lyapunov ***,for the linear second-order multi-agent systems,some explicit consensus conditions are ***,two sets of numerical simulations are performed.

关键词： Consensus control Multiplicative noises Second-order Discrete-time Multi-agent system

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Nonconvex Noise-Tolerant Neural Model for Repetitive Motion of Omnidirectional Mobile Manipulators

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1766-1768页

作者： Zhongbo Sun Shijun Tang Jiliang Zhang Junzhi Yu the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE College of Information Science and Engineering Northeastern UniversityShenyang 110819China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

Dear Editor,Quadratic programming problems(QPs)receive a lot of attention in various fields of science computing and engineering applications,such as manipulator control[1].Recursive neural network(RNN)is considered t... 详细信息

关键词： directional programming hardware

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GraspDiff: Grasping Generation for Hand-Object Interaction With Multimodal Guided Diffusion

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IEEE Transactions on Visualization and Computer Graphics 2024年 PP卷 PP-PP页

作者： Zuo, Binghui Zhao, Zimeng Sun, Wenqian Yuan, Xiaohan Yu, Zhipeng Wang, Yangang Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

Grasping generation holds significant importance in both robotics and AI-generated content. While pure network paradigms based on VAEs or GANs ensure diversity in outcomes, they often fall short of achieving plausibility. Additionally, although those two-step paradigms that first predict contact and then optimize distance yield plausible results, they are always known to be time-consuming. This paper introduces a novel paradigm powered by DDPM, accommodating diverse modalities with varying interaction granularities as its generating conditions, including 3D object, contact affordance, and image content. Our key idea is that the iterative steps inherent to diffusion models can supplant the iterative optimization routines in existing optimization methods, thereby endowing the generated results from our method with both diversity and plausibility. Using the same training data, our paradigm achieves superior generation performance and competitive generation speed compared to optimization-based paradigms. Extensive experiments on both in-domain and out-of-domain objects demonstrate that our method receives significant improvement over the SOTA method. We will release the code for research purposes. © 1995-2012 IEEE.

关键词： 3D modeling

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Mining Better Samples and Semantic Consistency for Contrast Learning in Forest Semantic Segmentation 41

Mining Better Samples and Semantic Consistency for Contrast ...

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第41届中国控制会议

作者： Chongjin Chen Yichao Cao Qingfei Tang Xiaobo Lu School of Software Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing Enbo Technology Co. Ltd.

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Image segmentation has impressive progress in the past several *** good segmentation usually follows pixelwise well-annotated labels which is ***,the robustness would not be guaranteed due to lack-ofdiversity *** work usually focuses on pixels individually and pay less attention to the neighbor *** local context would be scarce and the global context is not utilized following these *** proposal a method,named Forest Semantic Segmentation Network(FSSNet) to address these *** organizes original version and augmented version of images,as two inputs into student branch and teacher branch,and force the two outputs being consistent to strengthen the robustness of our ***,we not only consider pixel itself and also the neighbor pixels because the context of neighbor pixels helps understanding the *** utilizes contrastive loss with memory bank to involve global context in training which will make pixels closer to others in same category and far away from pixels of different categories.A bank filter is suggested to improve the quality of features in the memory *** also proposal a new sample strategy to improve the effect of contrastive loss and reduce the *** method can improve accuracy and strengthen the robustness with affordable extra computation during training process,and no additional computation during inference toward *** to benchmark,the proposed approach can improve the mIoU by 3.1% on our challenging dataset.

关键词： Semantic segmentation Contrastive learning Deep learning

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Composite-mask GAN based on refined optical flow and disparity map for SLAM visual odometry

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Optoelectronics Letters 2025年

作者： JI Yuehui JIANG Jingwei LIU Junjie SONG Yu GAO Qiang School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory of Control Theory and Application for Complex Systems

Although deep learning methods have been widely applied in slam visual odometry over the past decade with impressive improvements， the accuracy remains limited in complex dynamic environments. In this paper， a composite mask-based generative adversarial network is introduced to predict camera motion and binocular depth maps. Specifically， a perceptual generator is constructed to obtain the corresponding parallax map and optical flow from between two neighboring frames. Then， an iterative pose improvement strategy is proposed to improve the accuracy of pose estimation. Finally， a composite mask is embedded in the discriminator to sense structural deformation in the synthesized virtual image， thereby increasing the overall structural constraints of the network model， improving the accuracy of camera pose estimation， and reducing drift issues in the Visual Odometer. Detailed quantitative and qualitative evaluations on the KITTI dataset show that the proposed framework outperforms existing conventional， supervised learning and unsupervised depth VO methods， providing better results in both pose estimation and depth estimation.

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