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arXiv 2022年

作者： Xie, Liang Yu, Hongxiang Zhao, Yinghao Zhang, Haodong Zhou, Zhongxiang Wang, Minhang Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China The Application Innovate Lab Huawei Incorporated Company China

In the peg insertion task, human pays attention to the seam between the peg and the hole and tries to fill it continuously with visual feedback. By imitating the human's behavior, we design architectures with position and orientation estimators based on the seam representation for pose alignment, which proves to be general to the unseen peg geometries. By putting the estimators into the closed-loop control with reinforcement learning, we further achieve higher or comparable success rate, efficiency, and robustness compared with the baseline methods. The policy is trained totally in simulation without any manual intervention. To achieve simto-real, a learnable segmentation module with automatic data collecting and labeling can be easily trained to decouple the perception and the policy, which helps the model trained in simulation quickly adapting to the real world with negligible effort. Results are presented in simulation and on a physical robot. Code, videos, and supplemental material are available at https://***/xieliang555/***. © 2022, CC BY-NC-ND.

关键词： Visual communication

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Adaptive Decentralized Asymptotic Tracking control for Large-Scale Nonlinear systems With Unknown Strong Interconnections

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 173-186页

作者： Ben Niu Jidong Liu Ding Wang Xudong Zhao Huanqing Wang College of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Artificial Intelligence Nankai UniversityTianjin 300071China Faculty of Information Technology Beijing University of TechnologyBeijing 100124 State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Control Science and Engineering Dalian University of TechnologyDalian 116023China Department of Mathematical Science and Automatization Institute Bohai UniversityJinzhou 121000China

An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections,unknown time-varying parameters,and ***,by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time,all extra signals in the framework of decentralized control are filtered out,thereby removing all additional assumptions imposed on the interconnec-tions,such as upper bounding functions and matching ***,by introducing two integral bounded functions,asymptotic tracking control is ***,the nonlinear filters with the compensation terms are introduced to circumvent the issue of“explosion of complexity”.It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero *** the end,a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

关键词： Adaptive decentralized control asymptotic tracking control large-scale nonlinear systems unknown strong interconnections

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Oxidation Mechanism and Toxicity Evolution of Linalool,a Typical Indoor Volatile Chemical Product

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Environment & Health 2024年第7期2卷 486-498页

作者： Zihao Fu Song Guo Ying Yu Hong-Bin Xie Shiyu Li Daqi Lv Putian Zhou Kai Song Zheng Chen Rui Tan Kun Hu Ruizhe Shen Maosheng Yao Min Hu State Key Joint Laboratory of Environmental Simulation and Pollution Control International Joint Laboratory for Regional Pollution ControlMinistry of Education(IJRC)College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing 210044China Key Laboratory of Industrial Ecology and Environmental Engineering(Ministry of Education) School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024China Institute for Atmospheric and Earth System Research/Physics Faculty of ScienceUniversity of HelsinkiP.O.Box 64FIN-00014 HelsinkiFinland

Linalool,a high-reactivity volatile chemical product(VCP)commonly found in cleaning products and disinfectants,is increasingly recognized as an emerging contaminant,especially in indoor *** the gas-phase oxidation mechanism of linalool is crucial for assessing its impact on atmospheric chemistry and human *** quantum chemical calculations and computational toxicology simulations,we investigated the atmospheric transformation and toxicity evolution of linalool under low and high NO/HO_(2)·levels,representing indoor and outdoor *** findings reveal that linalool can undergo the novel mechanisms involving concerted peroxy(RO_(2)·)and alkoxy radical(RO·)modulated autoxidation,particularly emphasizing the importance of cyclization reactions *** expands the widely known RO_(2)·-dominated H-shift-driven autoxidation and proposes a generalized autoxidation mechanism that leads to the formation of low-volatility secondary organic aerosol(SOA)*** analysis shows that over half of transformation products(TPs)exhibited higher carcinogenicity and respiratory toxicity compared to *** also propose time-dependent toxic effects of TPs to assess their long-term *** results indicate that the strong indoor emission coupled with slow consumption rates lead to significant health risks under an indoor *** results highlight complex indoor air chemistry and health concerns regarding persistent toxic products during indoor cleaning,which involves the use of linalool or other VCPs.

关键词： volatile chemical products(VCPs) peroxy radicals(RO_(2)·) atmospheric autoxidation secondary organic aerosol(SOA) computational toxicology

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Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

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Petroleum Science 2023年第2期20卷 1142-1154页

作者： Yun-Peng He Chuan-Zhi Zang Peng Zeng Ming-Xin Wang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyangLiaoning 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyangLiaoning 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyangLiaoning 110169China University of Chinese Academy of Sciences Beijing100049China Shenyang University of Technology ShenyangLiaoning 110870China School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyangLiaoning 110159China

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

关键词： Few-shot learning Indicator diagram Meta-learning Soft thresholding Sucker-rod pumping system Time–frequency signature Working condition recognition

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Dynamic Multi-Robot Path Planning in Narrow-Lane Environments with One-Way Constraint 6

Dynamic Multi-Robot Path Planning in Narrow-Lane Environment...

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6th International Conference on Intelligent Autonomous systems, ICoIAS 2023

作者： Song, Zhenyu Zheng, Ronghao Zhang, Senlin Liu, Meiqin College of Electrical Engineering Zhejiang University Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xian710049 China

ISBN: (纸本)9798350371253

With the rapid development of the logistics industry, the path planning problem for automated guided vehicles (AGVs) in warehousing systems has become a prominent research topic. Traditional multi-robot path planning algorithms often perform poorly in narrow-lane environments found in warehouses. This paper proposes an improved dynamic multi-robot path planning algorithm specifically tailored for narrow-lane environments. By constraining the traversal directions within narrow lanes and dynamically determining the optimal directions, this algorithm achieves efficient multi-robot path planning and enhances the quality of robot paths. The effectiveness of the algorithm is theoretically proven in this paper. Extensive simulation data demonstrates that our algorithm efficiently generates high-quality paths in high-density narrow-lane warehouse environments. © 2023 IEEE.

关键词： Multipurpose robots

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Leveraging BEV Representation for 360-degree Visual Place Recognition

arXiv

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arXiv 2023年

作者： Xu, Xuecheng Jiao, Yanmei Lu, Sha Ding, Xiaqing Xiong, Rong Wang, Yue The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310058 China The School of Information Science and Engineering Hangzhou Normal University Hangzhou311121 China Alibaba Group Hangzhou310052 China

This paper investigates the advantages of using Bird’s Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual cues and spatial awareness. Our method extracts image features using standard convolutional networks and combines the features according to pre-defined 3D grid spatial points. To alleviate the mechanical and time misalignments between cameras, we further introduce deformable attention to learn the compensation. Upon the BEV feature representation, we then employ the polar transform and the Discrete Fourier transform for aggregation, which is shown to be rotation-invariant. In addition, the image and point cloud cues can be easily stated in the same coordinates, which benefits sensor fusion for place recognition. The proposed BEV-based method is evaluated in ablation and comparative studies on two datasets, including on-the-road and off-the-road scenarios. The experimental results verify the hypothesis that BEV can benefit VPR by its superior performance compared to baseline methods. To the best of our knowledge, this is the first trial of employing BEV representation in this task. © 2023, CC BY-SA.

关键词： Discrete Fourier transforms

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Current Problems and Solutions of industrial control Network Intrusion Detection: A Brief Survey

Current Problems and Solutions of Industrial Control Network...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Jiang, Tianyu Zhang, Chen Wang, Rui Chai, Senchun Institute of Automation Chinese Academy of Sciences and University of Science and Technology Beijing China Beijing Institute of Technology China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems China Institute of Intelligent Information Processing and Control Beijing Institute of Technology China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

With the advancement of industrial intelligence, the industrial Internet has been widely used in energy, manufacturing and other important industries. In recent years, security incidents have occurred frequently in industrial control network, which means that maintaining industrial control network security has become more and more important. Intrusion detection technology can actively detect abnormal behavior in the network, and is an important means to ensure the security of industrial control networks. Presently, the intrusion detection technology of industrial control network has problems such as extreme imbalance of classes and redundant interference of traffic characteristics. This paper analyzes these problems and clarifies the existing solutions for different problems in principle. © 2022 IEEE.

关键词： Network security

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Few-shot Underwater Acoustic Target Recognition Based on Siamese Network 42

Few-shot Underwater Acoustic Target Recognition Based on Sia...

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42nd Chinese control Conference, CCC 2023

作者： Yang, Haizhou Liu, Meiqin Zhang, Senlin Zheng, Ronghao Dong, Shanling Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9789887581543

Underwater acoustic target recognition is a widely investigated issue in the field of underwater acoustics. Many good results have been reported for underwater acoustic target recognition. However, in practical applications, the strong demand for labeled data for underwater acoustic target recognition is a big obstacle. In order to solve this problem, researchers have explored few-shot learning and unsupervised methods in various papers. A Siamese network is proposed which is composed of one-dimensional convolution and Long Short-Term Memory (LSTM) neural networks, called 1DCLSN. A structure for 1DCLSN is designed which combines contrastive information with label information and obtains satisfactory recognition results. In addition, the contrastive loss function with a different clustering term is modified to improve the performance. With only few labeled training samples, the performance of the proposed approach is better than those of other deep learning methods. The experiment shows the great potential of our method. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Underwater acoustics

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Universal Trajectory Optimization Framework for Differential-Driven Robot Class

arXiv

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arXiv 2024年

作者： Zhang, Mengke Chen, Nanhe Wang, Hu Qiu, Jianxiong Han, Zhichao Ren, Qiuyu Xu, Chao Gao, Fei Cao, Yanjun The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China

Differential-driven robots are widely used in various scenarios thanks to their straightforward principle, from household service robots to disaster response field robots. There are several different types of deriving mechanisms considering the real-world applications, including two-wheeled, four-wheeled skid-steering, tracked robots, etc. The differences in the driving mechanism usually require specific kinematic modeling when precise controlling is desired. Furthermore, the nonholonomic dynamics and possible lateral slip lead to different degrees of difficulty in getting feasible and high-quality trajectories. Therefore, a comprehensive trajectory optimization framework to compute trajectories efficiently for various kinds of differential-driven robots is highly desirable. In this paper, we propose a universal trajectory optimization framework that can be applied to differential-driven robot class, enabling the generation of high-quality trajectories within a restricted computational time-frame. We introduce a novel trajectory representation based on polynomial parameterization of motion states or their integrals, such as angular and linear velocities, that inherently matching robots’ motion to the control principle for differential-driven robot class. The trajectory optimization problem is formulated to minimize complexity while prioritizing safety and operational efficiency. We then build a full-stack autonomous planning and control system to show the feasibility and robustness. We conduct extensive simulations and real-world testing in crowded environments with three kinds of differential-driven robots to validate the effectiveness of our approach. We will release our method as an open-source package Copyright © 2024, The Authors. All rights reserved.

关键词： Four wheel steering

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Eigenvalue-Oriented Data-Driven Small-Signal Stability Assessment for DC Microgrids

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IEEE Transactions on Power systems 2024年

作者： Deng, Ruilong Jiang, Qiliang Zhou, Xu Wang, Yuhong Sun, Mingyang Zhejiang University State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China Sichuan University College of Electrical Engineering Chengdu610065 China Peking University Department of Industrial Engineering and Management College of Engineering Beijing100091 China

The stability issues of DC microgrids (DCmGs) are becoming increasingly important due to the widespread deployment of renewable distributed energy resources (DERs), which has led to a growing demand for DCmGs. Existing smallsignal white-box and gray-box models of DCmGs require system parameters and are severely limited in practical applicable scenarios, while black-box models cannot effectively assess DCmGs' stability. To this end, this paper proposes a novel eigenvalueoriented small-signal stability assessment approach for DCmGs, which achieves small-signal modeling and stability assessment of DCmGs through measurements of converters' interface. The contributions of this paper are as follows: 1) The Fed-Koopman network is proposed, mapping a multi-scenario DCmG to highdimensional linear space via a federated learning algorithm, and establishing the black-box small-signal model. 2) An improved elastic net regression (ENR) algorithm, named NeuENR is proposed, identifying the system matrix Asys. 3) Considering measurement errors during Asys identification, the small-signal stability criterion and stability margin are defined. The approach is implemented on the commercial electromagnetic transient simulation platform, CloudPSS, demonstrating its applicability to different operation conditions of DCmGs and superior assessment performance. © 1969-2012 IEEE.

关键词： Microgrids

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