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11th International Symposium on Intelligence Computation and Applications, ISICA 2019

作者： Tu, Geng Li, Qingyuan Jiang, Dazhi Department of Computer Science Shantou University Shantou515063 China Key Laboratory of Intelligent Manufacturing Technology Ministry of Education Shantou University Shantou100091 China Electrical Engineering and the Automation China University of Mining and Technology Beijing China

ISBN: (纸本)9789811555763

In recent years, gesture recognition that has the extensive application future is gradually the hotspot of the human-computer interaction. With the emergence of depth sensors such as Leap Motion, more depth information can be obtained, which provides a reliable data guarantee for recognizing complex gestures and better application scenarios. In this paper, five common dynamic gestures are defined firstly. Then, Leap Motion sensor is used to acquire the feature data of the hand. After comparing the recognition effects of HMM and DTW algorithms, a hybrid HMM-DTW model is proposed for dynamic gesture recognition. According to Kappa coefficient, it can be noticed that the model is substantial. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Gesture recognition

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Optimal Estimator Design and Properties Analysis for Interconnected Systems with Asymmetric Information Structure

arXiv

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arXiv 2021年

作者： Wang, Yan Xiong, Junlin Yang, Zaiyue Su, Rong School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China School of Electrical and Electronic Engineering Nanyang Technological University Nanyang Avenue 50 Singapore Department of Automation University of Science and Technology of China Hefei230026 China Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

This paper studies the optimal state estimation problem for interconnected systems. Each subsystem can obtain its own measurement in real time, while, the measurements transmitted between the subsystems suffer from random delay. The optimal estimator is analytically designed for minimizing the conditional error covariance. The boundedness of the expected error covariance (EEC) is analyzed. In particular, a new condition that is easy to verify is established for the boundedness of EEC. Further, the properties of EEC with respect to the delay probability are studied. We found that there exists a critical probability such that the EEC is bounded if the delay probability is below the critical probability. Also, a lower and upper bound of the critical probability is derived. Finally, the proposed results are applied to a power system, and the effectiveness of the designed methods is illustrated by simulations. © 2021, CC BY-SA.

关键词： Errors

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Background Flow Sensing for Autonomous Underwater Vehicles Using Model Reduction with Dynamic Mode Decomposition

Background Flow Sensing for Autonomous Underwater Vehicles U...

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IEEE/ASME (AIM) International Conference on Advanced intelligent Mechatronics

作者： Fengying Dang Sanjida Nasreen Feitian Zhang Bioinspired Robotics and Intelligent Control Laboratory at the Department of Electrical and Computer Engineering George Mason University Fairfax VA USA

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

Autonomous underwater vehicles (AUVs) have received increasing attention among scientific and engineering societies for their superior performances and promising future in marine environments. A key challenge to marine autonomy is the sensing of the background flow environment, which is important for navigation and motion control of AUVs. While existing flow estimation studies typically focus on (quasi-)steady flow fields for AUVs of well-defined shapes, this paper proposes a novel flow sensing algorithm that applies to any dynamic flows for arbitrary AUV shapes. The proposed flow sensing method assimilates distributed pressure measurements through coalescing recursive Bayesian estimation and dynamic mode decomposition (DMD)-based model reduction. To demonstrate the effectiveness of the proposed distributed flow sensing algorithm, both simulation and experimental results are presented on three testing prototypes of different shapes including a Joukowski foil, a triangle, and a rectangle.

关键词： Sensors Pressure measurement Estimation Heuristic algorithms Reduced order systems Vehicle dynamics Bayes methods

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Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater’s Condition

arXiv

引用

arXiv 2023年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize network regret and cumulative constraint violation. A novel distributed online algorithm is proposed and it achieves an O(T max{c,1−c}) network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the number of rounds and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T 1−c). Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to O(log(T)), and the network cumulative constraint violation bound is reduced to O(√log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is the first to achieve reduced (network) cumulative constraint violation bounds for (distributed) online convex optimization with adversarial constraints under Slater’s condition. Finally, the theoretical results are verified through numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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Urban Digital Twins for intelligent Road Inspection

Urban Digital Twins for Intelligent Road Inspection

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IEEE International Conference on Big Data

作者： Rui Fan Yikang Zhang Sicen Guo Jiahang Li Yi Feng Shuai Su Yanting Zhang Wenshuo Wang Yu Jiang Mohammud Junaid Bocus Xingyi Zhu Qijun Chen the Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems and State Key Laboratory of Intelligent Autonomous Systems Robotics & Artificial Intelligence Laboratory (RAIL) Tongji University Shanghai P. R. China Department of Computer Science & Technology Donghua University Shanghai P. R. China Department of Civil Engineering McGill University Montreal Canada CTO office at ClearMotion Billerica MA USA Department of Electrical & Electronic Engineering the University of Bristol BS8 1UB U.K Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji University Shanghai P. R. China

ISBN: (纸本)9781665480468

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022.

关键词： Roads Data acquisition Inspection Big Data Rough surfaces Digital twins

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A two-branch neural network for non-homogeneous dehazing via ensemble learning

arXiv

引用

arXiv 2021年

作者： Yu, Yankun Liu, Huan Fu, Minghan Chen, Jun Wang, Xiyao Wang, Keyan Department of Electrical and Computer Engineering McMaster University Hamilton Canada Center for Research on Intelligent System and Engineering Institute of Automation University of Chinese Academy of Sciences Beijing China State Key Laboratory of Integrated Service Networks Xidian University Xi’an China

Recently, there has been rapid and significant progress on image dehazing. Many deep learning based methods have shown their superb performance in handling homogeneous dehazing problems. However, we observe that even if a carefully designed convolutional neural network (CNN) can perform well on large-scaled dehazing benchmarks, the network usually fails on the non-homogeneous dehazing datasets introduced by NTIRE challenges. The reasons are mainly in two folds. Firstly, due to its nonhomogeneous nature, the non-uniformly distributed haze is harder to be removed than the homogeneous haze. Secondly, the research challenge only provides limited data (there are only 25 training pairs in NH-Haze 2021 dataset). Thus, learning the mapping from the domain of hazy images to that of clear ones based on very limited data is extremely hard. To this end, we propose a simple but effective approach for non-homogeneous dehazing via ensemble learning. To be specific, we introduce a two-branch neural network to separately deal with the aforementioned problems and then map their distinct features by a learnable fusion tail. We show extensive experimental results to illustrate the effectiveness of our proposed method. The source code is available at https://***/liuh127/Two-branch-dehazing. Copyright © 2021, The Authors. All rights reserved.

关键词： Demulsification

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Adaptive Artificial Potential Fields with Orientation Control Applied to Robotic Manipulators

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IFAC-PapersOnLine 2020年第2期53卷 9924-9929页

作者： Caio Cristiano Barros Viturino Ubiratan de Melo Pinto Junior André Gustavo Scolari Conceição Leizer Schnitman LaR - Robotics Laboratory Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil CTAI - Training Center in Technology in Industrial Automation Department of Chemical Engineering Federal University of Bahia Salvador Brazil

This paper proposes the integration of an Adaptive Artificial Potential Fields algorithm with a new end effector orientation control technique for real-time robot path planning. The development of autonomous robotic systems has undergone several advances in path planning algorithms. These systems generate object collision-free paths in the robot’s workspace. In this context, the Artificial Potential Fields technique has been the focus of improvements in recent years due to its simplicity of application and efficiency in real-time systems, since it does not require a global mapping of the robot’s workspace. In spite of its efficiency, this technique is susceptible to local minimum problems of different natures, such as Goals Non-Reachable with Obstacles Nearby (GNRON). To solve this problem, we suggest the use of an improvement called Adaptive Artificial Potential Fields used in conjunction with the proposed end effector orientation control technique, which allows reaching a desired orientation of the end effector. The resulting force, generated from the Adaptive Artificial Potential Field, guides the robot end effector to the goal. The Robot Operating System (ROS) framework and a collaborative robot manipulator UR5 are used to validate the proposed method on an approaching task for an object on a 3D printer tray.

关键词： Artificial Potential Field Orientation Control Robotic manipulators Path planning

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Multi-modal Intent Recognition Method for the Soft Hand Rehabilitation Exoskeleton

Multi-modal Intent Recognition Method for the Soft Hand Reha...

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第三十九届中国控制会议

作者： Wenyuan Chen Peng Yu Guangyong Li Wenxue Wang Chen Yao Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering Swanson School of Engineering University of Pittsburgh

Stroke has become the second most disabling disease in the *** to the intensive demand for physical therapists and the severe dependence on hospitals,the cost for the treatment of stroke patients is *** the most flexible limb of the human body,the hand faces more severe challenges,which has a much lower degree of recovery than the upper and lower *** the face of these challenges,a new treatment,exoskeleton-based rehabilitation,has demonstrated new *** paper proposes a novel design of the soft hand exoskeleton based on bionics and anatomy and the exoskeleton could help the users bend and extend their fingers,which would greatly improve the motor ability of stroke *** the control of the six drive motors,the exoskeleton could achieve most of the hand's freedom of *** the same time,we propose a multi-modal intent recognition method based on machine vision and machine *** specific rehabilitation training scenarios,both healthy subjects and patients could complete grasping tasks in the wearing of the exoskeleton,overcoming potential security risks caused by misidentification due to using the single-modal intent understanding method.

关键词： soft hand exoskeleton bionics anatomy Conference muti-modal intention recognition method

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$H_{\infty }$ PID Control for Discrete-Time Fuzzy Systems With Infinite-Distributed Delays Under Round-Robin Communication Protocol

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IEEE Transactions on Fuzzy Systems 2021年第6期30卷 1875-1888页

作者： Yezheng Wang Zidong Wang Lei Zou Hongli Dong College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University London Uxbridge U.K. Institute of Complex Systems and Advanced Control and the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University Daqing China

This article is concerned with the $H_{\infty }$ proportional–integral–derivative (PID) control problem for class of discrete-time Takagi–Sugeno fuzzy systems subject to infinite-distributed time delays and round-robin (RR) protocol scheduling effects. The information exchange between the sensors and the controller is conducted through a shared communication network. For the purpose of alleviating possible data collision, the well-known RR communication protocol is deployed to schedule the data transmissions. To stabilize the target system with guaranteed $H_{\infty }$ performance index, a novel yet easy-to-implement fuzzy PID controller is developed whose integral term is calculated based on the past measurements defined in a limited time window with hope to improve computational efficiency and reduce accumulation error. Based on the Lyapunov stability theory and the convex optimization technique, sufficient conditions are derived to ensure the exponential stability as well as the $H_{\infty }$ disturbance attenuation/rejection capacity of the underlying system. Furthermore, by utilizing the cone complementarity linearization algorithm, the nonconvex controller design problem is transformed into an iterative optimization one that facilitates the controller implementation. Finally, simulation examples are given to show the effectiveness and correctness of the developed control method.

关键词： PD control Protocols PI control Control systems Symmetric matrices Communication networks Optimization

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