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2022 29th International Conference on Nuclear Engineering, ICONE 2022

作者： Zhang, Pengbin Xing, Kunpeng Zou, Tao Yang, Zhijia Wang, Jingyang Wang, Hongrui State Key Laboratory of Robotics Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd Shenzhen China College of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9780791886472

Nuclear power is one of several significant clean energy and its production capacity is increasing rapidly. This paper proposed a novel PID self-tuning method based on closed-loop identification used in nuclear power plants to deal with the turbine's load tracking problem. A setpoint-shifting method is adopted to achieve the informativity of the closed-loop system, and the PID parameters are designed through an optimization method. For validating the proposed method, this paper modeled a pressurized-water reactor (PWR) nuclear power plant, a complex system consisting of three circuits, through the first principle. The primary circuit contains three main devices: a nuclear reactor, a pressurizer, and a steam generator. The models of these three devices are provided by previous literature separately. The whole model is generated through mass and energy balance. This mechanism model is used as the plant model, while the identified model is used as the control model. The initial PID parameters are set through open-loop identification using an external excitation. Then the proposed method works online to track the turbine's load in time. The effectiveness of the proposed method is illustrated by simulations. Copyright © 2022 by ASME.

关键词： Nuclear power plants

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Lunar Exploration Robot Mobility and Stability: A Comprehensive Analysis and Simulation Study

Lunar Exploration Robot Mobility and Stability: A Comprehens...

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2024 IEEE International Conference on Control Science and systems Engineering, ICCSSE 2024

作者： Xu, Yansong Li, Junlin Luo, Haitao Dong, Yuming Wang, Yaowei Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China University of Chinese Academy of Sciences Shenyang China School of Mechanical Engineering Shenyang University of Technology Shenyang China

ISBN: (纸本)9798331517199

This paper conducts an in-depth study on the mobility and stability of lunar exploration robots in the complex lunar environment. By defining the robot's primary geometric parameters and two typical motion configurations, the paper analyzes the distribution of wheel loads, mobility performance, and stability performance. Specifically, considering the characteristics of the soft lunar soil, a contact dynamics model between the wheels and the soft lunar soil is established. The discrete element method (DEM) was used to simulate the lunar soil surface, and multi-body dynamics and DEM coupled simulation experiments were conducted. The experimental results indicate that the robot's mobility and stability are significantly affected by the height of the center of mass and ground clearance. The analysis of traction torque, sinkage, and slip ratio further verifies the accuracy and practicality of the model. This research provides theoretical foundations and technical support for the design and operation of lunar exploration robots. © 2024 IEEE.

关键词： Wheels

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Modeling of dynamic-elastic deformation error for a collaborative robot 18

Modeling of dynamic-elastic deformation error for a collabor...

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

作者： Zhu, Shijing Pan, Xinan Wang, Hongguang Li, Dianqi School of Mechanical Engineering Shenyang University of Technology Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9798350312201

In the operation of a collaborative robot, the inertial force, gravity force, and other factors can cause elastic deformation of links and joints, and hence results in considerable deviations from the desired trajectory. However, the effects of inertial force, gravity force, and Jacobian matrix are rarely considered in common deformation analyses. This paper proposes a method for modeling dynamic elastic deformation of collaborative robots including joint torsional stiffness and complementary stiffness. Firstly, the dynamics equation was established based on the Newton-Euler method, and the driving torque of the joint was analyzed. Secondly, a dynamic-elastic deformation error model was established based on the joint torsional stiffness and complementary stiffness. Finally, the simulation results show that the established dynamic error model has good predictability, and the relative error is less than 1.63% compared with the ADAMS simulation results. © 2023 IEEE.

关键词： Errors

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Aspect-Ratio and Scale Aware Rotated Defect Detection for Fiber-reinforced Composites 5

Aspect-Ratio and Scale Aware Rotated Defect Detection for Fi...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Xu, Yuanye Liu, Yuanhao University of Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Institute of Automation Chinese Acad. of Sci. Institutes for Robotics and Intelligent Mfg. Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350373820

Detecting surface defects in carbon fiber-reinforced composites (FRCs) during prepreg stacking is crucial for ensuring product quality. Traditional methods using horizontal bounding boxes (HBBs) for defect detection often lead to overlapping boxes and inaccurate results. This paper proposes an Aspect-Ratio and Scale Aware Detector network (ARSADet) specifically designed for rotated defect detection in FRCs. The network incorporates a Gabor convolutional network for feature extraction, an aspect-ratio and scale-aware label assignment method, and a newly established FRCs Oriented Defect Detection dataset (FODD). Evaluation on the FODD dataset demonstrates superior performance compared to existing methods. © 2024 IEEE.

关键词： Aspect ratio

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Actuator Fault Estimation Design for DC Motor Using Unknown Input Observer 22

Actuator Fault Estimation Design for DC Motor Using Unknown ...

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2nd International Conference on Control and Intelligent robotics, ICCIR 2022

作者： Wu, Shenhua Yang, Shengxuan Xiao, Lei Wu, Minhui State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9781450397179

This study investigates the problem of robust fault estimation for direct current (DC) motor control systems subjected to actuator faults and unknown input disturbances. An augmented descriptor system is first built by creating an augmented state consisting of system states and actuator faults. Then, a novel unknown input observer (UIO) based on the augmented descriptor system is proposed to estimate actuator faults by decoupling the unknown input disturbances. Furthermore, the design conditions of the observer are transformed into linear matrix inequalities (LMIs). Finally, the effectiveness of the developed UIO is validated through simulations. © 2022 ACM.

关键词： Linear matrix inequalities

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An Opensim Based Verification Method for Lower Limb sEMG Muscle Synergy Decomposition 14

An Opensim Based Verification Method for Lower Limb sEMG Mus...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems, CYBER 2024

作者： Yu, Huiting Yang, Deguo Shi, Enming Liang, Jiawei Zhang, Bi Zhao, Xingang School of Software Shenyang University of Technology Liaoning Shenyang110870 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Liaoning Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798331506056

Complex musculoskeletal modeling of the lower extremity is critical for enhancing the understanding of the neural control of hip-knee-ankle joint movements, diagnosing lower extremity movement disorder, and making subsequent treatments. This paper proposes a strategy for analyzing the neuromuscular control of the lower limb based on muscle synergy extraction. We construct a new system suitable for the analysis of human motion characteristics by building a musculoskeletal model of multiple joints of the lower limb in Opensim. Then we realize the simulation of human walking simulation and obtain muscle activation simulation results. Meanwhile, according to the muscle synergy theory, sEMG signals are decomposed by NMF to extract real-time muscle activation components during the gait cycle. The simulation results can be used as a guide and corroboration of muscle synergy extraction to reveal the modes of modular muscle control in human lower limb. © 2024 IEEE.

关键词： Joints (anatomy)

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Smoke Attention Based Laparoscopic Image Desmoking Network with Hybrid Guided Embedding

Smoke Attention Based Laparoscopic Image Desmoking Network w...

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IEEE International Conference on robotics and Biomimetics

作者： Ziteng Liu Wenpeng Gao Jiahua Zhu Bainan Liu Yili Fu School of Life Science and Technology Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Surgical smoke can obscure the surgeon's line of sight during the laparoscopic procedure, potentially posing risk to the patients. Robot assisted-minimally invasive surgery (MIS) also faces such a challenge, which also hinders its autonomous operation. This paper proposes a desmoking method for laparoscopic surgery smoke removal. As the surgical smoke is hetergeneous, a two-stage network is developed to estimate the smoke distribution and reconstruct the corresponding smoke-free scene with the hybrid embedding guidance, including the estimated smoke mask and the initial image. The network is designed following the concept of the atmospheric scattering model, which makes the network more explainable. To qualitatively and quantitatively evaluate the proposed method, a synthetic laparoscopic dataset is generated and a real laparoscopic dataset is obtained. Experimental results show that the Peak Signal to Noise Ratio (PSNR) of the proposed method is 1.9% higher than that of the DehazeFormer, while the run-time is shorter more than 40.9%. The proposed method is comparable to or even outperforms the state-of-the-arts in desmoking quality and computation cost.

关键词： Laparoscopes Training PSNR Minimally invasive surgery Atmospheric modeling Scattering Real-time systems Robots Videos Testing

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A review of structures,verification,and calibration technologies of space robotic systems for on-orbit servicing

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Science China(Technological Sciences) 2021年第3期64卷 462-480页

作者： DING XiLun WANG YeCong WANG YaoBing XU Kun Robotics Institute Beihang UniversityBeijing 100191China Beijing Institute of Spacecraft System Engineering Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and ApplicationsBeijing 100094China

Recently,with the rapid development of aerospace technology,an increasing number of spacecraft is being launched into ***,the demands for on-orbit servicing(OOS)missions are rapidly *** robotics is one of the most promising approaches for various OOS missions;thus,research on space robotics technologies for OOS has attracted increased attention from space agencies and universities *** this paper,we review the structures,ground verification,and onorbit kinematics calibration technologies of space robotic systems for ***,we systematically summarize the development of space robotic systems and OOS programs based on space ***,according to the structures and applications,these systems are divided into three categories:large space manipulators,humanoid space robots,and small space *** to the capture mechanisms adopted,the end-effectors are systematically ***,the ground verification facilities used to simulate a microgravity environment are summarized and ***,the on-orbit kinematics calibration technologies are discussed and analyzed compared with the kinematics calibration technologies of industrial manipulators with regard to four ***,the development trends of the structures,verification,and calibration technologies are discussed to extend this review work.

关键词： space robotic system on-orbit servicing large space manipulator humanoid space robot verification kinematics calibration

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Shrinkage estimator based-DGLRSQ for motor imagery classification 38

Shrinkage estimator based-DGLRSQ for motor imagery classific...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Fan, MengLing Tang, Fengzhen Zhao, Xingang Chinese Academy of Sciences The State Key Laboratory of Robotics Shenyang Institute of Automation Liaoning Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Liaoning Shenyang110169 China University of Chinese Academy of Sciences Beijing Beijing100049 China

ISBN: (纸本)9798350303636

Motor imagery classification is a crucial task in the field of brain-computer interfaces (BCI)which can benefit individuals with disabilities in their movements of their limbs. However, accurately classifying such signals poses a significant challenge owing to the complexity of the signal dynamics. This paper proposes a approach for motor imagery classification utilizing shrinkage estimator-based dynamic generalized learning Riemannian space quantization (DGLRSQ). Our proposed approach aims to provide a more accurate estimated covariance matrix by utilizing a shrinkage estimator, which avoids overfitting through data regularization. Additionally, the impact of the sliding window size on the classification performance is investigated, where shorter shifting size delivers better classification performance but with lower computational efficiency. Experimental results demonstrate the efficacy of our method and its superior performance on the publicly available BCIIV 2a motor imagery EEG dataset. © 2023 IEEE.

关键词： Dynamic Learning Vector Quantization Learning Vector Quantization Riemannian Manifold Shrinkage Estimator

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Learning Representation for Clustering via Dual Correlation 6

Learning Representation for Clustering via Dual Correlation

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

作者： Zhang, Tao Hu, Mingming Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China University of Chinese Academy of Sciences Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Shenyang China Center for Clinical Studies Data Management Department Beijing China

ISBN: (纸本)9781665460033

Contrastive learning is a self-supervised learning method used to learn the general features of a dataset without labels by letting the model learn which data points are similar or different. It constructs labeled samples through data augmentation. Therefore, the core and difficulty of self-supervised contrastive learning are to construct high-quality contrastive samples, especially negative sample pairs. In this paper, a cluster-based contrast sample selection method is proposed, which selects the positive contrast samples from the same cluster and the negative contrast samples from different clusters. By minimizing the forward contrast sample loss and maximizing the reverse sample loss, the deep representation clustering result of the image is obtained. The experimental results show the excellence and adaptability of the algorithm. © 2023 IEEE.

关键词： Deep learning

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