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SSRN 2023年

作者： Zhu, Yongsheng Zhao, Tianming Sun, Fengxin Jia, Changjun Ye, Hui Jiang, Yawei Wang, Kuo Huang, Chaorui Xie, Yannan Mao, Yupeng Physical Education Department Northeastern University Shenyang110819 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China Key Laboratory for Organic Electronics and Information Displays Institute of Advanced Materials Jiangsu Key Laboratory for Biosensors Jiangsu National Synergetic Innovation Center for Advanced Materials Nanjing University of Posts and Telecommunications Jiangsu Nanjing China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China College of Information Science and Engineering Northeastern University Shenyang China School of Strength and Conditioning Training Beijing Sport University Beijing China

Metauniverse is a future society based on the convergence of the physical and the digital world, which is fully indebted to the sophisticated human-machine interface (HMI) to connect the two worlds. In this study, a metauniverse sport interactive system based on TENG has been developed, realizing interaction with human beings, devices, and the internet in real-time. This metauniverse sport interactive system is consistent with a self-powered anaerobic power meter (APM), a wireless transmission module, personalized data analysis based on the professional Wingate anaerobic power test method, and an augmented reality (AR) application. Self-powered APM is integrated with the axles of the bicycle to monitor human exercises, such as peak power (PP), mean power (MP), and fatigue index (FI). The data is transmitted to the cloud by the wireless transmission module and users can freely explore the metauniverse through the AR application. As a result of machine learning, the professional Wingate anaerobic power test method can not only reflect the anaerobic capacity, but also be used for athlete selection. This work provides a new approach to online competitions, sport training, and athlete selection and provides a new way to build metauniverse. © 2023, The Authors. All rights reserved.

关键词： Human computer interaction

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Deep Learning for Visual SLAM in Transportation robotics:A review

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Transportation Safety and Environment 2019年第3期1卷 177-184页

作者： Chao Duan Steffen Junginger Jiahao Huang Kairong Jin Kerstin Thurow Institute of Artificial Intelligence&Robotics(IAIR) School of Traffic&Transportation EngineeringCentral South University410075 China Institute of Automation University Rostock18119 Germany 3Center for Life Science AutomationUniversity of Rostock18119 Germany

Visual SLAM(Simultaneously Localization and Mapping)is a solution to achieve localization and mapping of robots *** achievements have been made during the past decades,geography-based methods are becoming more and more successful in dealing with static ***,they still cannot handle a challenging *** the great achievements of deep learning methods in the field of computer vision,there is a trend of applying deep learning methods to visual *** this paper,the latest research progress of deep learning applied to the field of visual SLAM is *** outstanding research results of deep learning visual odometry and deep learning loop closure detect are ***,future development directions of visual SLAM based on deep learning is prospected.

关键词： deep learning visual SLAM transportation robotics mobile robots

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Composite Adaptive Control for Anti-Unwinding Attitude Maneuvers: An Exponential Stability Result without Persistent Excitation

arXiv

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arXiv 2021年

作者： Shao, Xiaodong Hu, Qinglei Li, Daochun Shi, Yang Yi, Bowen School of Aeronautic Science and Engineering Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Mechanical Engineering University of Victoria VictoriaBCV8N 3P6 Canada Australian Center for Field Robotics University of Sydney SydneyNSW2006 Australia

This paper provides an exponential stability result for the adaptive anti-unwinding attitude tracking control problem of a rigid body with uncertain but constant inertia parameters, without requiring the satisfaction of persistent excitation (PE) condition. Specifically, a composite immersion and invariance (I&I) adaptive controller is derived by integrating a prediction-error-driven learning law into the dynamically scaled I&I adaptive control framework, wherein we modify the scaling factor so that the algorithm design does not involve any dynamic gains. To avoid the unwinding problem, a barrier function is introduced as the attitude error function, along with the tactful establishment of two crucial algebra properties for exponential stability analysis. The regressor filtering method is adopted in combination with the dynamic regressor extension and mixing (DREM) procedure to acquire the prediction error using only easily obtainable signals. In particular, aiding by a constructive liner time-varying filter, the scalar regressor of DREM is extended to generate a new exciting counterpart. In this way, the derived controller is shown to permit closed-loop exponential stability without PE, in the sense that both output-tracking and parameter estimation errors exponentially converge to zero. Further, the composite learning law is augmented with a power term to achieve synchronized finite/fixed-time parameter convergence. Numerical simulations are performed to verify the theoretical findings. © 2021, CC BY.

关键词： Navigation

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Mode Tracking Scheme Among Remeshed Models for Structural Optimization

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Journal of Shanghai Jiaotong university(Science) 2019年第6期24卷 706-715页

作者： LIU Jun DING Ziqi LU Qi HAN Xianhong National Engineering Research Center of Die&Mold CAD School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200030China Robotics and Discrete Automation Business Research China ABB Engineering(Shanghai)Ltd.Shanghai 201319China

Mode tracking is required in the structural optimization when the frequencies of certain specified modes must be maintained within a suitable range.A simple tracking method employing the mode number is invalid or misleading when local modes appear or disappear during mesh *** this work,a mode tracking scheme combining the nearest neighbor method(NNM)with the modal assurance criterion(MAC)is *** NNM algorithms are compared,and the k-dimensional tree(kd-tree)NNM is used to transform eigenvectors(mode shapes)from different scales to identical one.A threshold determination method is implemented for the MAC to assess the similarities in all the calculated *** the basis of the mode tracking scheme,specified modes can be tracked between different finite element method(FEM)models which have different meshes and optimized *** effectiveness is verified through an example of shape optimization using an electric motor structure.

关键词： mode tracking scheme remeshed models optimization process nearest neighbor method(NNM) modal assurance criterion(MAC)

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Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape

arXiv

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arXiv 2022年

作者： Zhu, Xiangyu Yu, Chang Huang, Di Lei, Zhen Wang, Hao Li, Stan Z. Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 95 Zhongguancun Donglu Beijing100190 China Beijing100049 China Beijing Advanced Innovation Center for BDBC Beihang University Beijing China Beijing100049 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Engineering Westlake University Hangzhou China

3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori. However, previous reconstructed 3D faces suffer from degraded visual verisimilitude due to the loss of fine-grained geometry, which is attributed to insufficient ground-truth 3D shapes, unreliable training strategies and limited representation power of 3DMM. To alleviate this issue, this paper proposes a complete solution to capture the personalized shape so that the reconstructed shape looks identical to the corresponding person. Specifically, given a 2D image as the input, we virtually render the image in several calibrated views to normalize pose variations while preserving the original image geometry. A many-to-one hourglass network serves as the encode-decoder to fuse multiview features and generate vertex displacements as the fine-grained geometry. Besides, the neural network is trained by directly optimizing the visual effect, where two 3D shapes are compared by measuring the similarity between the multiview images rendered from the shapes. Finally, we propose to generate the ground-truth 3D shapes by registering RGB-D images followed by pose and shape augmentation, providing sufficient data for network training. Experiments on several challenging protocols demonstrate the superior reconstruction accuracy of our proposal on the face shape. Copyright © 2022, The Authors. All rights reserved.

关键词： Geometry

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

arXiv

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on Computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

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Study on Photocatalytic, Electric, and Sensing Behavior of Co- and Ag- Codoped Tin Dioxide (Sno2) Nano Particles

SSRN

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SSRN 2023年

作者： Zhang, Qiang Zou, Xiao Wang, Yan Habibi, Mostafa Shaanxi Key Laboratory of Catalysis School of Chemistry and Environmental Science Shaanxi University of Technology Shaanxi Hanzhong723001 China School of Pharmacy Shaanxi Institute of International Trade & Commerce Shaanxi Xianyang712046 China Faculty of Architecture and Urbanism UTE University Calle Rumipamba S/N and Bourgeois Quito Ecuador Center of Excellence in Design Robotics and Automation Department of Mechanical Engineering Sharif University of Technology Azadi Avenue P.O. Box 11365-9567 Tehran Iran Department of Biomaterials Saveetha Dental College and Hospital Saveetha Institute of Medical and Technical Sciences Chennai600 077 India

The role of Co and Ag codoping on the electric, optical, and sensing performance of tin dioxide nano particles was investigated. Co-Ag codoping reduced particle size from 51.8 nm to 41.6 nm and the unit cell dimensions changed. Overall, Co and Ag resulted in significant rise in oxygen vacancy of SnO2 so that defects compensate unbalanced electron charge induced by dopants. It was also figured out that Co-Ag codoping improved the conductivity of SnO2 at low and high temperatures. Doping Led to decrement in the band gap energy of SnO2 from 3.66 eV to 3.07 eV, which shows improved optical properties of the compound. Study on photodegradation activity of SnO2 showed up to 97. % enhancement due to Co-Ag co-doping. Finally, the sensor response of the compound revealed that the response intensity of tin dioxide increased significantly when 4 wt.% Co and 2 wt. % Ag are doped into it. © 2023, The Authors. All rights reserved.

关键词： Photocatalytic activity

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Modeling and Detecting Communities in Node Attributed Networks

arXiv

引用

arXiv 2021年

作者： Ren, Ren Shao, Jinliang Bishop, Adrian N. Zheng, Wei Xing School of Automation Engineering University of Electronic Science and Technology of China 611731 China The Research Center on Crowd Spectrum Intelligence Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518054 China Australia Canberra Research Lab Australia The School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW2751 Australia

As a fundamental structure in real-world networks, in addition to graph topology, communities can also be reflected by abundant node attributes. In attributed community detection, probabilistic generative models (PGMs) have become the mainstream method due to their principled characterization and competitive performances. Here, we propose a novel PGM without imposing any distributional assumptions on attributes, which is superior to the existing PGMs that require attributes to be categorical or Gaussian distributed. Based on the block model of graph structure, our model incorporates the attribute by describing its effect on node popularity. To characterize the effect quantitatively, we analyze the community detectability for our model and then establish the requirements of the node popularity term. This leads to a new scheme for the crucial model selection problem in choosing and solving attributed community detection models. With the model determined, an efficient algorithm is developed to estimate the parameters and to infer the communities. The proposed method is validated from two aspects. First, the effectiveness of our algorithm is theoretically guaranteed by the detectability condition. Second, extensive experiments indicate that our method not only outperforms the competing approaches on the employed datasets, but also shows better applicability to networks with various node attributes. © 2021, CC BY.

关键词： Population dynamics

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Holographic MIMO Communications with Arbitrary Surface Placements: Near-Field LoS Channel Model and Capacity Limit

arXiv

引用

arXiv 2023年

作者： Gong, Tierui Wei, Li Huang, Chongwen Yang, Zhijia He, Jiguang Debbah, Mérouane Yuen, Chau School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China 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 Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates KU 6G Research Center Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits. Empowered by the nearly continuous, large and energy-efficient surfaces with powerful electromagnetic (EM) wave control capabilities, H-MIMO opens up the opportunity for signal processing in a more fundamental EM-domain, paving the way for realizing holographic imaging level communications in supporting the extremely high spectral efficiency and energy efficiency in future networks. In this article, we propose a generalized EM-domain near-field channel modeling and study its capacity limit of point-to-point H-MIMO systems that equips arbitrarily placed surfaces in a line-of-sight (LoS) environment. Two effective and computational-efficient channel models are established from their integral counterpart, where one is with a sophisticated formula but showcases more accurate, and another is concise with a slight precision sacrifice. Furthermore, we unveil the capacity limit using our channel model, and derive a tight upper bound based upon an elaborately built analytical framework. Our result reveals that the capacity limit grows logarithmically with the product of transmit element area, receive element area, and the combined effects of 1/d2mn, 1/d4mn, and 1/d6mn over all transmit and receive antenna elements, where dmn indicates the distance between each transmit element n and receive element m. Particularly, 1/d6mn dominates in the near-field region whereas 1/d2mn dominates in the far-field region. Numerical evaluations validate the effectiveness of our channel models, and showcase the slight disparity between the upper bound and the exact capacity, which is beneficial for predicting practical system performance. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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Joint torque estimation for dynamic jumping control of compliant gear-driven legged robot

Joint torque estimation for dynamic jumping control of compl...

引用

2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Cheng, Dingan Jin, Bingchen Sun, Caiming Zhang, Aidong Liu, Shoubin Shenzhen China Shenzhen China Robotics Research Center Peng Cheng Laboratory Shenzhen China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen China

ISBN: (纸本)9781728163215

This paper is to explain compliant torque control for gear driven legged robot. In this paper, active torque compliance control is realized without torque sensor or any mechanically elastic component. The joint torque estimation is very important for torque control. However, Joint torque sensor for medium-size legged robot is so large and heavy that it increases the inertia of the leg, slows down the dynamic response, and leads to complicated electrical system. Therefore, we only use the encoder information and the motor current to estimate the joint torque without torque sensors. Compliant torque control have advantages of absorbing impacts and saving energy. But, torque control is difficult to guarantee the position precision, because the accurate model of the robot is hard to obtain. In order to ensure both compliance and position accuracy, hybrid torque and position control is applied to our robot. In the jumping motion experiments, it is verified that the joint torque can reach peck value in 4.75 ms when jumping up. While The landing peak torque in hybrid control is lower than 1/6 of that under position control and 1/2 of that in torque control. This indicates that the compliance torque control can absorb a large part of shock. © 2019 IEEE.

关键词： Compliance control

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