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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Sun, Yubo Cheng, Yuanyuan Zhu, Zhizhong Liu, Jinrui Guo, Zhenhui Yu, Ningbo College of Artificial Intelligence Nankai University Tianjin300350 China Tianjin Huanhu Hospital Department of Rehabilitation Medicine Tianjin300350 China Nankai University Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Tianjin300350 China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

ISBN: (纸本)9798350300178

Parkinson's disease (PD) is a progressive neurode-generative disorder that requires accurate severity assessment to facilitate proper medication and interventions. This study proposes a novel method for PD severity assessment by fusing bilateral surface electromyography (sEMG) signals and features using Fisher vector. The sEMG signals were measured from the tibialis anterior (TA) and lateral gastrocnemius (GL) muscles in 28 PD patients. Preprocessing was performed to remove industrial frequency noise and low-frequency trend, followed by feature extraction using a customized feature generator that included time-domain and frequency-domain features. The extracted features and preprocessed sEMG signals were then vectorized using the Fisher vector and fused as input to the classifier. Our results demonstrated that considering bilateral features is more favorable than unilateral features for the PD severity assessment. Moreover, the proposed method significantly enhanced the classification accuracy, with a remarkable classification accuracy of 96.00% achieved through the combination of our proposed method with a decision tree classifier. © 2023 IEEE.

关键词： Time domain analysis

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Trajectory Tracking Error Optimization based on Iterative Learning 4

Trajectory Tracking Error Optimization based on Iterative Le...

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4th IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2022

作者： Huang, Haoran Zhang, Tao Jia, Danping Zhang, Hualiang Yan, Hongyu Pei, Quanshen Ding, Fan Shenyang University of Technology School of Information Science and Engineering Shenyang China 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 Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

The X-Y precision plane motion platform is widely used in various precision motion control fields. In this paper, to improve the tracking accuracy of the X-Y precision plane motion table, a high-level iterative learning control with forgetting factor is proposed, and it is combined with a kind of PD-type iterative learning control with low-pass filter, and the method is applied to the Simulink model of the linear motor of the X-Y precision plane platform, thereby improving the trajectory accuracy of the X-Y precision plane motion platform. The traditional ILC has the disadvantage that the more number of iterations, the more divergent tracking error is. The new ILC proposed in this paper can not only effectively reduce the noise, reduce the tracking error, but also eliminate the problem of error divergence caused by too many iterations. At last, the experimental results of high-order PD iterative learning with forgetting factor and low-pass filter, PD iterative learning with low-pass filter, ordinary PD iterative learning are compared. The results show that the high-order PD iterative learning with forgetting factor and low-pass filter has better tracking effect than the first-order iterative learning and other several algorithms. It can make the movement trajectory of the X-Y precision plane motion platform more stable and improve the accuracy. © 2022 IEEE.

关键词： Low pass filters

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Fast Realization of Robot 3D Simulation Based on WebGL

Fast Realization of Robot 3D Simulation Based on WebGL

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第35届中国控制与决策会议

作者： Tao Zhang Xintong Wang Yun Teng Jingzhe Fang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Shenyang University of Technology

As science and technology have advanced,an increasing number of people are focusing their study on 3 D robot *** 3 D quick simulation of the mechanical arm can be achieved using 3 D modeling software and *** the third-party WebGL library *** to construct the simulation scenario,then interact with and render the 3 D scene and 3 D robot model directly on the Web,which is easier and faster than downloading *** essay briefly explains the benefits and applicability of *** in the creation of 3 D virtual simulations,then goes on to discuss the quick implementation process of 3 D robot simulations before summarizing the building technique.

关键词： webgl three.js 3D simulation robot

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Path-tracking of Mobile Robot Using PD-type Iterative Learning Control with Forgetting Factor

Path-tracking of Mobile Robot Using PD-type Iterative Learni...

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第35届中国控制与决策会议

作者： Tao Zhang Jingzhe Fang Yun Teng Xintong Wang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Shenyang University of Technology

In this paper,a discrete iterative learning control strategy is proposed for the path-tracking problem of mobile robot trajectory tracking with repeated *** algorithm adds the forgetting factor to the PD-type discrete iterative learning control and considers the correction of the initial state of the *** convergence of the algorithm is proved rigorously by using the paradigm theory,and the sufficient conditions of the paradigm form to make the algorithm converge are ***,the effectiveness of the proposed algorithm is verified by simulation.

关键词： Mobile robot discrete iterative learning control forgetting factor path-tracking

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Research on prefabricated component production line mold platform combination method

Research on prefabricated component production line mold pla...

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2021 International Conference on Advanced Manufacturing Technology and Electronic Information, AMTEI 2021

作者： Han, Zhonghua Yu, Lingfeng Li, Bo Qi, Yuanwei School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

In traditional prefabricated component manufacturing enterprises, there are often problems such as low utilization rate of the platform surface due to poor production plans of the enterprise, resulting in waste of production resources and low production capacity of the enterprise. In order to solve the problem of mold platform combination allocation in the production process of prefabricated component manufacturers, a mold platform combination allocation method based on the combination of machine learning and backtracking is proposed. The backtracking method is used to search for the theoretical best fit combination result, and the improved BL is used. The positioning algorithm simulates the placement process of the mold on the platform, and uses the Apriori algorithm to train the obtained data set to obtain the association rules contained in the frequent item set. When the enterprise re-produces, the prefabricated components are combined according to the association rules for production, and the utilization rate of the platform is improved. The simulation test is carried out with the example data of the prefabricated component manufacturing enterprise, which verifies the effectiveness of the combination allocation method of mold and platform combined with the backtracking method and the Apriori algorithm to solve the problem. © Published under licence by IOP Publishing Ltd.

关键词： Production control

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Research on optimization method of routing buffer linkage based on Q-learning

Research on optimization method of routing buffer linkage ba...

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2021 International Conference on Advanced Manufacturing Technology and Electronic Information, AMTEI 2021

作者： Han, Zhonghua Qi, Hongfeng Chang, Daliang Gong, Wei Dai, Shilong School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

According to the characteristics of the painting process of passenger car manufacturing enterprises, by formulating the routing buffer linkage rules based on the total renewal cost, the linkage process of the bus in the routing buffer is controlled, and an improved Q-learning (Q- The routing buffer of learning) algorithm quickly finds the optimal path method. According to the actual production situation, this method improves the dynamic parameters of the algorithm on the basis of the traditional Q-learning algorithm, and improves the optimization speed and accuracy of the algorithm by establishing the correlation between the work-in-process and its neighboring work-in-process in the current state. Through multiple sets of example simulation tests, the effectiveness of the Q-learning algorithm in solving the optimization problem of routing buffer linkage is verified. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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AI Enabled Automatic Mobile Robot Intelligent Navigation in Construction with Obstacle Awareness

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IEEE Transactions on Automation Science and engineering 2025年

作者： Zhang, Yinlong Liu, Yuanhao Cui, Yunge Zeng, Ziming Liang, Wei Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China University of Chinese Academy of Sciences Beijing101408 China Zeekr Group Zeekr Intelligent Driving Department Shanghai110078 China Shenzhen Polytechnic University School of Automotive and Transportation Engineering Shenzhen518055 China

In the construction industry, the integration of artificial intelligence (AI) and robotics has led to significant advancements in automating various tasks. One critical aspect is the intelligent navigation of automatic mobile robot (AMR) within construction sites, where dynamic environments pose challenges such as inaccurate robot state estimation, irregular and textureless obstacle detection. To solve these issues, this paper designs an AI enabled obstacle-aware AMR intelligent navigation approach while ensuring robot safety and efficiency. Specifically, the robot is equipped with the complementary RGB camera, inertial measurement unit (IMU) and wheel encoder to estimate the states (i.e., positions, orientations and velocities), which have been tightly fused in the optimization framework. Furthermore, the RGB-Depth images are jointly fed into the AI model. It combines of Mask-RCNN and multi-scale attention network, to detect and segment the obstacles, and estimate the corresponding relative depth. It should be noted that the system incorporates obstacle awareness mechanisms to dynamically switch the robot's velocities in response to obstacles in the environment, ensuring smooth and collision-free movement. Experimental results demonstrate the effectiveness and robustness of the proposed AI-enabled navigation system in real-world construction scenarios, showcasing its potential to enhance productivity and safety in construction automation. © 2004-2012 IEEE.

关键词： Construction industry

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Learning to Synthesize and Remove Rain Unsupervisedly 18th

Learning to Synthesize and Remove Rain Unsupervisedly

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Qi, Yinhe Pan, Meng Jin, Zhi School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Guangdong Provincial Key Laboratory of Robotics and Digital Intelligent Manufacturing Technology Guangzhou510535 China

ISBN: (纸本)9783030893699

Most existing single image deraining networks are trained in a supervised way, which relies on paired images including one clean image and one rain image. In most cases, the rain images are synthesized from the clean ones manually to obtain sufficient paired images. However, not only huge time costs but expert knowledge are needed to ensure the synthesized images are realistic enough. In addition, the superior performance of these deraining networks trained on manually synthesized rain images is hard to be maintained when testing on real rain images. To address these issues, we propose a scene adaptive asymmetric CycleGAN (SAA-CycleGAN) which transfers clean images to their rainy counterparts automatically so that adequate realistic rain images can be obtained for training deraining networks in a supervised way. Moreover, SAA-CycleGAN can both remove rain from rainy images and synthesize rain on clean images benefiting from the cycle consistency strategy. Since the information is not symmetric during the rain synthesis process and the deraining process, the generators are designed with different architecture accordingly for these two processes. Comprehensive experiments show that the SAA-CycleGAN is able to synthesize more lifelike rain images and achieve similar deraining performance compared with the state-of-the-art deraining methods. © 2021, Springer Nature Switzerland AG.

关键词： Rain

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Towards Fast Face Image Quality Assessment via Latent Diffusion Model 18th

Towards Fast Face Image Quality Assessment via Latent Diffu...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Yan, Zheyu Zhao, Weisong Zhu, Xiangyu Gao, Li Zhang, Xiao-Yu Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China China Mobile Financial Technology Co. Ltd. Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819610679

Face recognition technology has gradually come into wide application in real life. However, in real-life unconstrained scenarios, low-quality images could be matched to unpredictable individuals, thereby affecting the stability of recognition and even causing security problems. Conducting quality assessment on face images before facial recognition can help alleviate these problems by rejecting low-quality samples. In this paper, we propose a Fast Diffusion-model-based Face Image Quality Assessment method (FDif-FIQA). It uses a diffusion model to perturb face images and measures image quality based on the similarity of features before and after the perturbation. The denoising process is transferred from the pixel space to the latent space, thereby reducing the computational cost. We evaluated the performance of this method with multiple mainstream face recognition models on various public datasets. Compared to state-of-the-art methods, our approach demonstrates highly competitive performance and significant improvements in computational efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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Multiparametric atomic force microscopy imaging of single native exosomes

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生物化学与生物物理学报（英文版） 2021年第3期53卷 385-388页

作者： Mi Li Xinning Xu Ning Xi Wenxue Wang Xiaojing Xing Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China Cancer Hospital of China Medical University Shenyang 110042China Liaoning Cancer Hospital&Institute Shenyang 110042China Department of Industrial and Manufacturing Systems Engineering The University of Hong KongHong Kong 999077China

The advent of atomic force microscopy(AFM)provides a novel powerful tool for addressing biological issues in their native *** uses a sharp tip mounted at the end of a micro-fabricated cantilever to probe the specimens immobilized on the *** with other high-resolution imaging and mea-surement techniques(***-resolution optical microscopy,scan-ning electron microscopy,transmission electron microscopy,and magnetic/optical tweezers),the unique merit of AFM is that it is able to simultaneously acquire the structures and properties of living biological samples under aqueous conditions with unprece-dented spatiotemporal resolution(nanometer spatial resolution and millisecond temporal resolution)[1],making AFM an invalu-able platform for revealing the underlying mechanisms guiding life *** wide applications of AFM in molecular and cell biology provide considerable novel insights into the physi-ological/pathological processes[2].Particularly,in recent years,a new AFM imaging mode,which is called peak force tapping(PFT)mode[3],emerges as a promising way for correlating the topography of the specimens with the mechanics of *** PFT mode,the vibrating AFM tip indents the sample at each sam-pling point to record force curves,and the peak force of the force curve is used as the feedback[4].By analyzing the force curves,several types of mechanical properties of the specimens can be real-timely visualized,and thus PFT mode is also called multiparametric imaging AFM[5],offering novel possibilities for resolving the fine structures and properties of fragile biological specimens.

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