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12th International Conference on CYBER Technology in automation, control, and Intelligent systems, CYBER 2022

作者： Wang, Yifan Cheng, Long Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The lack of rehabilitation therapists and the increase of disabled individuals have reduced the opportunities for patients to receive well rehabilitation assessment. Towards solving this challenge, establishing a therapist-assistance assessment system to reduce the medical resource consumption is demanding. This paper proposes an automatic hand rehabilitation assessment system based on the Leap Motion vision sensor and a self-designed hand strength measurement device. The assessment algorithm requires the patients to complete every motion given in Fugl-Meyer assessment scale (FMA), and automatically scores the completion level of each motion via recognizing different hand gestures and hand grasping strength. As illustrated by the experiments on ten volunteers, the proposed assessment system can obtain the reliable scores which are consistent with the ones manually obtained via FMA. © 2022 IEEE.

关键词： Patient rehabilitation

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Visual Grasping with Spectral Clustering and Heuristic Searching for Robot in Cluttered Environments

Visual Grasping with Spectral Clustering and Heuristic Searc...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Geng, Wenjie Cao, Zhiqiang Tang, Yingbo Wang, Shuo Jing, Fengshui The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665481090

Grasping the target object is an essential requirement for the robot to provide better services. It becomes complicated especially in cluttered environments, which still remains challenging. This paper proposes a grasping chain generation solution that enables the robot to grasp the target after other obstruction objects are moved in a good order. SSD is firstly adopted to acquire the information of detectable objects and then Euclidean clustering is employed to obtain the untrained objects. After that, the minimum bounding box of each object is obtained, which is then projected on the plane and represented by a smooth differentiable minimum ellipse. On this basis, an information density kernel function is designed to express the interaction between objects. By abstracting each ellipse as a node of the graph whose edge weight is calculated by this kernel function, the whole scene is described in a form of graph. To simplify the complexity of the scene graph, we use spectral clustering algorithm to classify the objects, and the task-oriented objects graph is constructed according to the objects closely related to the target one. As a result, the searching space is reduced. With space division of task-oriented objects graph, each candidate grasping chain is iteratively extended by using the heuristic searching and the best chain with the shortest length is determined. The proposed method can deal with secondary obstruction and its effectiveness is testified by experiments. © 2022 IEEE.

关键词： Iterative methods

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EGCN: Ensemble Graph Convolutional Network for Neural Architecture Performance Prediction

EGCN: Ensemble Graph Convolutional Network for Neural Archit...

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2021 International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2021

作者： Liu, Xin DIng, Zixiang Li, Nannan Chen, Yaran Zhao, Dongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China

ISBN: (纸本)9781665402453

Neural Architecture Search (NAS) is proposed to automatically search novel neural networks. Currently, one typical problem of NAS is that its computation requirements are too high to stand for most researchers. In fact, it consumes a lot of resources to train subnetworks for architecture search. If the performance of each subnetwork can be predicted accurately without training, the computational burden will be alleviated. Graph Convolutional Network (GCN) is proven to have powerful capabilities for topological information perception and extraction. It is suitable to use GCN for predicting neural architecture performance which is related to its *** this paper, we treat GCN as the performance predictor with two improvements. First, a novel neural architecture data processing method named DATAPRO2 is designed to improve GCN's performance. Then, we propose EGCN, a model-based performance predictor which employs ensemble technique on GCN with DATAPRO2 to alleviate the overfitting issue caused by the imbalanced dataset for neural architecture performance prediction. Experimental results on CVPR-2021-NAS-TRACK2 dataset show that EGCN contributes to obtaining better predictive performance than vanilla GCN and other popular predictors. © 2021 IEEE.

关键词： Topology

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Adaptive control of Discrete-time Nonlinear systems Using ITF-ORVFL

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 556-563页

作者： Xiaofei Zhang Hongbin Ma Wenchao Zuo Man Luo School of Automation Beijing Institute of TechnologyBeijing 100081 School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China School of Automation Beijing Institute of Technologyand also with the State Key Laboratory of Intelligent Control and Decision of Complex Systems(Beijing Institute of Technology)Beijing 100081China School of Automation Beijing Institute of TechnologyBeijing 100081and he is with Beijing Institute of Electronic System EngineeringBeijing 100854China School of Automation Beijing Institute of TechnologyBeijing 100081and she is with Ant GroupBeijing 310013China

Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly *** network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural *** this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training *** link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is *** experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.

关键词： Adaptive control initial-training-free online learning algorithm random vector functional link networks

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A Pilot Study on Coupling Behaviors of Limb Movements and Brain Activation Based on EEG and fNIRS Synchronization Information

A Pilot Study on Coupling Behaviors of Limb Movements and Br...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Tang, Xi Li, Qingge Tang, Yao Tian, Lan Zheng, Yue Li, Xiangxin Jiang, Naifu Shang, Peng Li, Guanglin Peng, Liang Fang, Peng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems The Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350327182

Motion is one of the basic physiological functions of human beings. However, many brain diseases such as stroke may cause different degrees of motor dysfunctions for patients. As a commonly used rehabilitation method, active and passive exercise training may enhance patients' neuromuscular functions and recover their motor abilities. It is known that limb movements are strongly coupled with brain activation but there is currently insufficient exploration on the coupling behaviors from the perspective of informatics. In this study, the coupling relationship between limb movements and brain activation was preliminarily studied based on three healthy subjects. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals were synchronously collected during lower limb movements, and time-frequency analysis (TFA) and transfer entropy (TE) analysis were performed to quantitatively study the brain activation behaviors. In the experiments, a desynchronization phenomenon of μ rhythm in EEG was observed during exercise states, and the experimental results demonstrate the activation rule of motor and prefrontal cortexes upon limb movements. Calculations show that there exists a bidirectional flow of information between EEG and cerebral oxygen metabolism signals, but with a difference between different directions. This work may support the rehabilitation for patients with motor dysfunctions with a guidance of quantitative indicators and also benefit the exploration on neuroscience. © 2023 IEEE.

关键词： Electroencephalography

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TAO-Based Data Governance in Intelligent Transportation systems

TAO-Based Data Governance in Intelligent Transportation Syst...

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International Conference on Intelligent Transportation

作者： Xiaolong Liang Juanjuan Li Rui Qin Wenwen Ding Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems and State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences and Collaborative Laboratory for Intelligent Science and Systems Beijing China

Intelligent Transportation systems (ITS) are heavily dependent on private user data. However, most ITS fails to harness the potential of this invaluable data due to the absence of effective data governance mechanisms promoting users' contributions. Moreover, data contributors face security risks such as privacy preservation, data leakage, etc., as well as high costs in data sharing, while benefits are disproportionately reaped by system operators and interaction. This systemic imbalance could indirectly incentivize a surge in inactions and even malicious actions. In response to these challenges, we propose the design of a True Autonomous Organization (TAO) for ITS, namely ITS TAO. Utilizing the newly designed decision models with decentralized organization structures and the three-power structure, ITS TAO aims to realize the fair distribution of rights and benefits for ITS data contributors. Furthermore, we design a real-time evaluation system based on parallel intelligence capable of identifying potential hazards.

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AR-C&P: A Tangible Programming for Children Based Augmented Reality 22

AR-C&P: A Tangible Programming for Children Based Augmented ...

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Proceedings of the Tenth International Symposium of Chinese CHI

作者： Danli Wang Yanyan Zhao Shuo Feng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China and School of Artificial Intelligence University of Chinese Academy of Sciences China

ISBN: (纸本)9781450398695

Augmented Reality (AR) technology enables continuous visual feedback and varied digital scenes on the real world. This paper presents AR-C&P, an AR-based tangible programming tool for young children. It allows children to create AR maze map with low-cost materials and plan the route by constructing tangible program sequences. Meanwhile, a variety of real-time visual feedback from the screen will be superimposed on the real scenes of the whole process. AR-C&P is intuitive and intriguing which could enrich children’s creating experience, develops their cognitive developmental skills and helps children learn fundamental programming concepts. We conducted two experiments. The first experiment reveals the relationship between task completion and cognitive developmental skills. The second experiment reveals the effect of AR on learning programming.

关键词： Augmented Reality children edutainment programming languages tangible programming

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CFSeRec:A Contrastive Framework for Sequential Recommendation

CFSeRec:A Contrastive Framework for Sequential Recommendatio...

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第42届中国控制会议

作者： Tong Wang Yaping Dai Shuai Shao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

In sequential recommender systems,the main problems are the long-tailed distribution of data and noise interference.A Contrastive Framework for Sequential Recommendation(CFSeRec) is proposed to solve these two problems *** shuffling and adversarial attack data augmentation methods are used in the framework to improve the quality and quantity of training data,so that the long-tailed problem is *** the application of projection head method,the sequence representation becomes more general and robust,rather than just adapted to the task of contrastive ***,the impact of noise on sequence recommender systems is effectively *** on four public datasets show that CFSeRec achieves state-of-the-art performance in the metrics of hit ratio and normalized discounted cumulative gain,when comparing to the seven previous frameworks.

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Comprehensive Gear Shift Schedule Design for 2-AMT Electric Vehicles Based on Sparrow Search Algorithm

Comprehensive Gear Shift Schedule Design for 2-AMT Electric ...

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第42届中国控制会议

作者： Tianyi Wang Liling Ma Junzheng Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

With the aim of 2-AMT electric vehicles,a comprehensive shift schedule that considers both power and economy is ***,the objective function of the comprehensive shift schedule is constructed,which is the weighted sum of vehicle acceleration time and vehicle power consumption per unit ***,Sparrow Search Algorithm is used to solve the objective function and obtain the comprehensive shift ***,AVL Cruise simulation software is used to simulate vehicle dynamics and economy under an *** results show that the comprehensive shift schedule is similar to the optimal power shift schedule in terms of dynamic performance,and the economy is optimized by 3%.The feasibility of the comprehensive shift schedule is verified.

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Two Degree of Freedom Active Disturbance Rejection control Strategy for Time Delay systems

Two Degree of Freedom Active Disturbance Rejection Control S...

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第42届中国控制会议

作者： Xiyuan Shan Tao Cai School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

This paper is concerned with the controller design and the theoretical analysis for time-delay systems,a two degree of freedom(feedforward and feedback) control method is proposed,which combines advantages of the Smith predictor and the active disturbance rejection control(ADRC).The feedforward part of controller is used to track the set point,the feedback part of controller(ADRC) is used to suppress interferences and the Smith predictor is used to correct time *** proposed control design is easy to tune parameters and has been proved to effectively control systems with large time *** bounded input bounded output(BIBO) stability of closed-loop system is ***,numerical simulations show the effectiveness and practicality of the proposed design.

关键词： Time-delay Active Disturbance Rejection control(ADRC) Smith Predictor(SP)

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