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Infrared, Millimeter-Wave, and Terahertz Technologies XI 2024

作者： Maa, Yehao Shen, Liran Cao, Yuqi Hou, Dibo Zhang, Guangxin State Key Laboratory of Industrial Control Technology Dept. of Control Science and Engineering Zhejiang University Hangzhou310027 China Robotics Institute Ningbo University of Technology Ningbo315211 China

ISBN: (数字)9781510682238

ISBN: (纸本)9781510682221

Aerogel is known as super insulation materials and has already been applied to manufacture thermal protection parts of aerospace equipment. Terahertz (THz) wave has good penetration to non-metallic and non-polar materials. To explore its potential in aerogel defect detection, the absorption coefficient and refractive index of aerogel were studied. As a result, the absorption spectrum of aerogel has not obvious absorption peak, and THz wave has great penetration for aerogel. Additionally, two aerogel samples with different sizes of defects were imaged with two time domain parameters (pulse peak and pulse valley) and two frequency domain parameters (absorption coefficient and refractive index). In contrast to other three parameters, the refractive index presents the greatest performance in defect detection, defects in the aerogel can be observed more clearly through the refractive index. The study is of key importance for THz wave in nondestructive testing for aerogel, offering a rapid and non-destructive detection method for aerogel defects. © 2024 SPIE.

关键词： Refractive index

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A new active rehabilitation training mode for upper limbs based on Tai Chi Pushing Hands

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Biomimetic Intelligence & robotics 2024年第3期4卷 82-92页

作者： Xiangpan Li Liaoyuan Li Jianhai Han Bingjing Guo Ganqin Du School of Mechatronics Engineering Henan University of Science and TechnologyLuoyang 471003China Henan Provincial Key Laboratory of Robotics and Intelligent Systems Luoyang 471003China School of Automotive Engineering Hubei University of Automotive TechnologyShiyan 442002China Hubei Key Laboratory of Automotive Power Train and Electronic Control Shiyan 442002China The First Affiliated Hospital of Henan University of Science and Technology Luoyang 471003China

Robot-assisted rehabilitation is a crucial approach to restoring motor function in the ***,the current training trajectory lacks sufficient theoretical or practical support,and the monotony of single-mode training is a *** Chi Pushing Hands,a beneficial and effective daily exercise,has been shown to improve balance function,psychological state,and motor function of the upper extremities in patients recovering from *** address these issues,we propose a new active rehabilitation training that incorporates Tai Chi Pushing Hands movements and yin-yang balance *** training trajectory and direction are encoded by the velocity field and consist of two processes:yang(push)and yin(return).During yang,the limb actively pushes the robot to move,while during yin,the limb actively follows the robot’s *** provide necessary assistance,an admittance controller with self-adaptive parameters is *** addition,we introduce two indexes,the‘Intention Angle’(ϖ)and the time ratio(Γ),to evaluate motion perception *** experiment was conducted on a 4-degree-of-freedom upper limb rehabilitation robot platform,and the subjects were separated into a familiar group and an unfamiliar *** experiment results show that the training could be completed well no matter whether the subject is familiar with Tai Chi Pushing Hands or *** parameters and the movement of the robot can be adjusted based on the interactive force to adapt to the ability of the subject.

关键词： Tai Chi Push Hands Velocity field Self-adaptive admittance Upper limb rehabilitation Active training

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Small Photoresist Defect Samples Augmentation Based on Generative Adversarial Network 6

Small Photoresist Defect Samples Augmentation Based on Gener...

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6th IEEE Information Technology, Networking, Electronic and Automation control Conference, ITNEC 2023

作者： Yang, Guang Li, Zhihang Yang, Zhijia Cui, Shuping State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9781665460033

Photoresist coating technology is an important part of the surface treatment of semiconductor wafers. The presence of bubbles in photoresist can seriously affect the quality of wafers, however, the lack of sufficient bubble samples makes intelligent automatic detection techniques impossible. To solve such a problem, we propose B-GAN based on adversarial generative network, and design a mapping network for potential encoding mapping and a synthetic network for high-quality bubble image generation, so that defective bubble samples can be automatically generated. Experiments prove that our method achieves excellent results. © 2023 IEEE.

关键词： III-V semiconductors

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Fault Diagnostic Opportunities for Robot Servo Motors Using the Physics of Failure Analysis 14

Fault Diagnostic Opportunities for Robot Servo Motors Using ...

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14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023

作者： Zhang, Bo Liang, Shan Wang, Kai Key Laboratory of Networked Control Systems Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China College of Computer Science and Technology University of Chemical Technology Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350329988

Joint robots are widely used in various industries, such as aviation, aerospace, and automotive manufacturing. The performance degradation of servo motors can significantly affect the overall performance of the robots because of the core component of joint robots, and therefore, it is critical to detect and address servo motor failures promptly. In this study, we analyze some common causes and mechanisms of performance degradation in robot servo motors using the physics of failure analysis. We also discuss strategies for detecting and diagnosing faults using these mechanisms before the servo motors fail completely. © 2023 IEEE.

关键词： Servomotors

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Data-Driven Tracking control of a Cushion Robot with Safe Autonomous Motion Considering Human-Machine Interaction Environment

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Journal of Dynamic systems, Measurement and control 2025年第4期147卷 041003页

作者： Sun, Ping Shan, Rui Chang, Hongbin Wang, Shuoyu School of Artificial Intelligence Shenyang University of Technology Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Intelligent Mechanical Systems Engineering Kochi University of Technology Kochi7828502 Japan

This study proposes a data-driven safety controller with velocity constraints for a cushion robot. We constructed a mathematical description of the human-machine interaction environment by decomposing the generalized input force and coefficient matrix in the dynamic model. We established a new equivalent data model, which considered various human-machine interaction environments using a pseudo-Jacobian matrix. A stochastic configuration network (SCN) estimation method for variations in the human-machine interaction environment was proposed, with hidden layer nodes added at random. We designed a safe autonomous navigation path and proposed a data-driven control method that limited the actual velocity while stabilizing the tracking error system. In addition, the desired motion velocity of the robot was designed. This approach has the advantage of ensuring safety at a specified velocity. We also demonstrated and validated the effectiveness of the proposed data-driven algorithm using simulation-based comparative analysis and an experimental study. Copyright © 2024 by ASME.

关键词： Stochastic systems

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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A Novel Sensor Scheduling Algorithm Based on Deep Reinforcement Learning for Bearing-Only Target Tracking in UWSNs

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1077-1079页

作者： Linyao Zheng Meiqin Liu Senlin Zhang Jian Lan Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’an 710049 State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China

Dear Editor,This letter is concerned with the energy-aware multiple sensor coscheduling for bearing-only target tracking in the underwater wireless sensor networks(UWSNs).Considering the traditional methods facing with the problems of strong environment dependence and lack flexibility,a novel sensor scheduling algorithm based on the deep reinforcement learning is ***,the sensors’co-scheduling strategy in UWSNs is formulated as Markov decision process(MDP).

关键词： Deep networks underwater

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Weld Defects Detection by Neural Networks Based on Attention Mechanism 7

Weld Defects Detection by Neural Networks Based on Attention...

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7th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Yang, Guang Liu, Zhifeng Wang, Jianjun Cui, Shuping State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9798350334197

Welding technology plays an indispensable role in ship construction, and its maturity has a direct impact on the quality of shipbuilding. The weld seam, being the weakest part of a welded structure, significantly affects the structural strength, service life, and safety reliability of ships. However, the current identification of weld defects in the ship industry relies on manual methods, which are subjective and prone to error. To address this issue, this paper presents a novel approach by collecting weld images from actual ship scenes, preprocessing them to eliminate noise and highlight defect features, and proposing a Transformer-based improved target detection model (T-OBJDETC) to enhance the recognition accuracy of the model. © 2023 IEEE.

关键词： Ships

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Research on Knowledge-based Assembly Process Planning Method Based on Representation Learning Reasoning

Research on Knowledge-based Assembly Process Planning Method...

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Chinese control and Decision Conference, CCDC

作者： Yu-kai Fu Le-jun Fu Yi-yang Liu Peng Zeng Rui-fei Peng Key Laboratory of Networked Control Systems Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Science Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

For multi-variety and small-batch assembly parts, the assembly process updates and iterations are very frequent. The assembly process planning method based on manual experience cannot effectively utilize the large amount of hidden assembly process knowledge generated during product iteration. To solve this problem, this paper proposes an effective assembly process planning method based on knowledge graph. Fristly, the entire assembly is constructed from each layer of assembly components to reduce the computational complexity of assembly process planning. Then, assembly process planning starts from the last layer of assembly components. The entire assembly process planning is obtained by concatenating the results of each layer’s assembly process planning from bottom to top. Assembly operations are determined based on the features of the base component and neighboring assembly components. If there is no corresponding assembly operation for the assembly features, the assembly operation can be inferred through representation learning. After that, arrange and combine all possible assembly solutions to form a feasible solution space. Inferring the optimal assembly process sequence through rules. Combining the optimal assembly sequence with specific assembly operations to complete assembly process planning. Finally, Macro and micro adjustments are made to the assembly process planning of different products in the same series by comparing the differences between current product and classic product. In this paper, the upper door of elevator is used as an example to verify the proposed method. The results show that the proposed method can effectively realize assembly process planning.

关键词： Representation learning Process planning Knowledge based systems Process control Knowledge graphs Manuals Predictive models

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Fault Detection Based on Hierarchical Mode Analysis with ICA-KICA 41

Fault Detection Based on Hierarchical Mode Analysis with ICA...

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第41届中国控制会议

作者： Shuai Li Xiaofeng Zhou Haibo Shi Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

For the problem of fault detection in non-Gaussian and nonlinear multimode processes,a fault detection method based on hierarchical mode analysis with independent component analysis(ICA)-kernel independent component analysis(KICA) is *** stable and transition modes coexist in multimode processes and the mode information has uncertainty,hierarchical mode analysis is used for obtaining the hierarchical mode ***,the ICA-KICA is presented for modeling the stable and transition modes obtained from the hierarchical mode ***,a mode identification method based on discriminant function is given for fault *** penicillin fermentation process is used for the experiments of fault detection and the analyses indicate the performance of the fault detection based on hierarchical mode analysis with ICA-KICA.

关键词： Fault detection multimode process hierarchical mode analysis ICA-KICA

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