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arXiv 2025年

作者： Yang, Yingyuan Liang, Guoyuan Wang, Can Wu, Xiaojun Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing10049 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Mechanical Engineering and Automation Harbin Institute of Technology at Shenzhen Shenzhen518055 China

Multi-view action recognition aims to identify actions in a given multi-view scene. Traditional studies initially extracted refined features from each view, followed by implemented paired interaction and integration, but they potentially overlooked the critical local features in each view. When observing objects from multiple perspectives, individuals typically form a comprehensive impression and subsequently fill in specific details. Drawing inspiration from this cognitive process, we propose a novel trunk-branch contrastive network (TBCNet) for RGB-based multi-view action recognition. Distinctively, TBCNet first obtains fused features in the trunk block and then implicitly supplements vital details provided by the branch block via contrastive learning, generating a more informative and comprehensive action representation. Within this framework, we construct two core components: the multi-view deformable aggregation (MVDA) and the trunk-branch contrastive learning. MVDA employed in the trunk block effectively facilitates multi-view feature fusion and adaptive cross-view spatio-temporal correlation, where a global aggregation module (GAM) is utilized to emphasize significant spatial information and a composite relative position bias (CRPB) is designed to capture the intra- and cross-view relative positions. Moreover, a trunk-branch contrastive loss is constructed between aggregated features and refined details from each view. By incorporating two distinct weights for positive and negative samples, a weighted trunk-branch contrastive loss is proposed to extract valuable information and emphasize subtle inter-class differences. The effectiveness of TBCNet is verified by extensive experiments on four datasets including NTU-RGB+D 60, NTU-RGB+D 120, PKU-MMD, and N-UCLA dataset. Compared to other RGB-based methods, our approach achieves state-of-the-art performance in cross-subject and cross-setting protocols. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Development of an Integrated CMUTs-Based Resonant Biosensor for Label-Free Detection of DNA with Improved Selectivity by Ethylene-Glycol Alkanethiols

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Engineering 2024年第10期41卷 231-241页

作者： Zhikang Li Yihe Zhao Gian Luca Barbruni Jie Li Zixuan Li Jiawei Yuan Ping Yang Libo Zhao Zhuangde Jiang Sandro Carrara State Key Laboratory for Manufacturing Systems Engineering the International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesXi’an Jiaotong UniversityXi’an 710049China Xi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and System Yantai 264003China School of Instrument Science and Technology Xi’an Jiaotong UniversityXi’an 710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China Bio/CMOS Interfaces Laboratory Institute of Electrical and MicroengineeringSchool of EngineeringÉcole Polytechnique Fédérale de LausanneNeuchâtel 2000Switzerland College of Mechanical and Electrical Engineering Shaanxi University of Science and TechnologyXi’an 710021China

Gravimetric resonant-inspired biosensors have attracted increasing attention in industrial and point-ofcare applications,enabling label-free detection of biomarkers such as DNA and *** micromachined ultrasonic transducers(CMUTs)are promising tools for developing miniaturized highperformance biosensing complementary metal–oxide–silicon(CMOS)***,their operability is limited by inefficient functionalization,aggregation,crosstalk in the buffer,and the requirement for an external high-voltage(HV)power *** this study,we aimed to propose a CMUTs-based resonant biosensor integrated with a CMOS front–end interface coupled with ethylene–glycol alkanethiols to detect single-stranded DNA oligonucleotides with large *** topography of the functionalized surface was characterized by energy-dispersive X-ray *** selectivity for onchip hybridization was demonstrated by comparing complementary and non-complementary singlestranded DNA oligonucleotides using fluorescence imaging *** sensor array was further characterized using a five-element lumped equivalent *** 4 mm^(2) application-specific integrated circuit chip was designed and developed through 0.18 lm HV bipolar-CMOS-double diffused metal–oxide–silicon(DMOS)technology(BCD)to generate on-chip 20 V HV boosting and to track feedback frequency under a standard 1.8 V supply,with a total power consumption of 3.8 mW in a continuous *** measured results indicated a detection sensitivity of 7.943×10^(-3) lmol·L^(-1)·Hz^(-1) over a concentration range of 1 to 100 lmol·L^(-1).In conclusion,the label-free biosensing of DNA under dry conditions was successfully demonstrated using a microfabricated CMUT array with a 2 MHz frequency on CMOS electronics with an internal HV ***,ethylene–glycol alkanethiols successfully deposited self-assembled monolayers on aluminum electrodes,which has never been attempted thus far on CMUTs,to enhance the sel

关键词： Capacitive micromachined ultrasonic transducers(CMUTs) DNA detection Self-assembled monolayer(SAM) Ethylene-glycol alkanethiols Application-specific integrated circuit(ASIC)

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A New Dynamic Routing Network for Monocular Depth Estimation 12

A New Dynamic Routing Network for Monocular Depth Estimation

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12th International Conference on CYBER technology in Automation, Control, and intelligent systems, CYBER 2022

作者： Luo, Zhehao Luo, Sijin Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework. © 2022 IEEE.

关键词： Deep neural networks

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Control Strategy of 6-DOF Manipulator based on Improved Active Disturbance Rejection Control

Control Strategy of 6-DOF Manipulator based on Improved Acti...

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第35届中国控制与决策会议

作者： Dongxu Zhang Lixiang Ma Bingyou Liu Pan Yang College of Electrical Engineering Anhui Polytechnic University Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Institute of Information Technology Anhui Dar Intelligent Control System Stock Co. Ltd

Aiming at the problems of high coupling,parameter uncertainty and external environment interference in practical applications of six degrees of freedom manipulator,a control strategy based on improved active disturbance rejection control was ***,in order to improve the response speed of the system,the sliding mode control tracking differentiator(SMC-TD) is designed based on the synovial *** order to reduce the system jitter,the new sliding mode function is used to improve the sliding mode control law,which reduces the system jitter and improves the system response ***,the han function is constructed to realize the design of continuous smooth extended state observer,and the improved fractional extended state observer(FO-ESO) is designed in combination with fractional calculus,which enhances the anti-interference ability of the ***,combined with the traditional nonlinear error feedback control law(NLSEF),an improved active disturbance rejection controller was built,and different control strategies were used to compare the six degrees of freedom manipulator in the same *** experimental results show that the controller has good control effect and anti-disturbance robustness,which proves the effectiveness of the proposed method.

关键词： six degrees of freedom manipulator Sliding mode control Improved fractional ESO

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EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-LSTM Hybrid Model

EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-L...

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World Rehabilitation Robot Convention (WRRC)

作者： Wujing Cao Xinqiang Guo Yupeng Zou Shuo Zhang Mingxiang Luo Worawarit Kobsiriphat Xinyu Wu Meng Yin Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Mechanical Engineering China University of Petroleum East China National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350355123

ISBN: (纸本)9798350355130

Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance.

关键词： Support vector machines Deep learning Accuracy Noise Gesture recognition Stroke (medical condition) Feature extraction Electromyography Convolutional neural networks Long short term memory

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A general assembly sequence planning algorithm based on hierarchical reinforcement learning

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Kongzhi yu Juece/Control and Decision 2022年第4期37卷 861-870页

作者： Zhao, Ming-Hui Zhang, Xue-Bo Guo, Xian Ou, Yong-Sheng Institute of Robotics and Automatic Information System Nankai University Tianjin300350 China Key Laboratory of Intelligent Robotics Tianjin300350 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

For assembly sequence planning problems, most of the existing algorithms focus on a single target configuration. For multi-target configurations and large-scale problems, existing algorithms often have dimension disaster problems with poor generalization ability. Therefore, this paper uses the characteristics of the hierarchical structure of assembly sequence planning problems and conducts a general assembly sequence planning method based on hierarchical reinforcement learning, which is suitable for multi-configuration assembly tasks. First of all, this paper constructs the assembly sequence planning problem as a hierarchical Markov decision process, in which the upper layer performs sequence planning, and the lower layer carries out workpiece motion planning, which conforms to the hierarchical structure of the assembly process, making the planning method more flexible and interpretable. Then, in view of the hierarchical Markov decision process, this paper proposes a general assembly sequence planning algorithm based on hierarchical reinforcement learning, which improves the adaptability and generalization ability of the planning method to multi-target tasks and the information utilization of the target configuration. Finally, the proposed method is verified on the built simulation platform. The results show that the proposed method can extract general information about assembly problems, and it has good decision-making ability for any initial state and other various configurations assembly tasks, which verifies the effectiveness and flexibility of the method. Thus, a more general and flexible assembly sequence planning algorithm suitable for multiple configurations is realized. Copyright ©2022 Control and Decision.

关键词： Reinforcement learning

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Dynamic modeling and vibration analysis of a 6-DOFs industrial robot considering joint flexibility

Dynamic modeling and vibration analysis of a 6-DOFs industri...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Shen, Guodong Xu, Sheng Chen, Chunjie Wang, Qiang Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665436786

For the industrial robots, the vibration caused by the joint flexibility could affect the manipulator's performance significantly. Therefore, the modeling and vibration analysis of industrial robot considering joint flexibility has become an important topic in recent years. However, the research on flexible-joint manipulator is usually performed for planar robots, whereas the research on general spatial multi degrees of freedom (DOFs) industrial robots are still limited. In this paper, the dynamic modeling approach is developed for vibration analysis of the serial industrial robot having six DOFs. Firstly, based on the Spong's model of joint deformation, the flexibility of all joints is considered concurrently, and the partial velocity relationship between each link and the joint are deduced. Then, the dynamic model of the 6-DOFs flexible robot is derived according to the Kane equation. The results using the proposed Kane-based method are verified by several numerical simulations. Finally, vibration responses of the end-effector under different joint stiffness parameters are analyzed. The results in this study can offer some useful guidance for the path planning and control to suppress the vibration of the multi-DOFs industrial robots. © 2021 IEEE.

关键词： Vibration analysis

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Lensed fiber-optic scanning two-photon endomicroscopic imaging 2

Lensed fiber-optic scanning two-photon endomicroscopic imagi...

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2nd Conference on Biomedical Photonics and Cross-Fusion, BPC 2023

作者： Wang, Conghao Zhang, Xiulei Liu, Huilan Wang, Aimin Feng, Lishuang School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing100191 China State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing100871 China Laboratory of Intelligent Sensing Materials and Chip Integration Technology of Zhejiang Province Hangzhou innovation institute of Beihang University Hangzhou310063 China

ISBN: (数字)9781510667556

ISBN: (纸本)9781510667549

In recent years, two-photon endomicroscopy has developed as a promising label-free optical biopsy technique for diagnosing gastrointestinal tumors. In this study, we optimize the imaging resolution of the lensed fiber-optic scanning two-photon endomicroscopic imaging scheme. By fabricating a lensed fiber for fiber-optic scanning two-photon endomicroscopy, a lateral resolution of 2.1 μm and a field of view of 600 μm in two-photon endomicroscopic imaging is achieved. Furthermore, the objective-lens-free imaging capability is also validated using gastric wall tissue sections. © 2023 SPIE.

关键词： Fiber optics

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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Design and preliminary testing of a piezoelectric-based sole energy harvester

Design and preliminary testing of a piezoelectric-based sole...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Wang, Yidi Zhao, Xuan Shi, Qiuyu Liao, Haisu Wu, Xinyu Gao, Fei Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin999077 Hong Kong

ISBN: (纸本)9781665481090

Harvesting energy from the human body to replenish existing energy sources is receiving substantial attention. Recently scientists have been focusing on harvesting biomechanical energy from human feet during walking. This paper develops a piezoelectric-based sole energy harvester that is designed to be worn on the bottom of shoes to harvest energy, thus powering wearable electronic devices. Specifically, a cam with piezoelectric(PZT) stacks is mounted on the heel to scavenge energy during the heel strike. The cam can change the input's direction from the vertical direction to the horizontal direction. In addition, the output power of this part can be optimized or improved by generating a specified cam profile. A bimorph piezoelectric plate is installed on the forefoot to convert the toes' bending deformation into electricity. Herein, piezoelectric Macro Fiber Composites (MFC) are employed to capture the large deformation. Preliminary testing on a healthy subject is conducted to validate this design. The average power output of the harvester is 8.83 m W. © 2022 IEEE.

关键词： Cams

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