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A Parallel Education Based Intelligent Tutoring systems Framework

A Parallel Education Based Intelligent Tutoring Systems Fram...

引用

2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Jing, Sifeng Tang, Ying Liu, Xiwei Gong, Xiaoyan Cui, Wei Liang, Joleen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

although online education has improved efficiency of learners' access to high-quality educational resources, realtime interaction between instructors and learners have not yet been achieved in online personalized learning. Intelligent Tutoring systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed. © 2020 IEEE.

关键词： Computer aided instruction

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A Tracking control Method based on Event-Triggered Adaptive Dynamic Programming 38

A Tracking Control Method based on Event-Triggered Adaptive ...

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第三十八届中国控制会议

作者： Ziyang Wang Qinglai Wei Derong Liu School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation Guangdong University of Technology

ISBN: (纸本)9781728123295;9789881563972

In this paper, an event-triggered adaptive dynamic programming(ADP) algorithm is developed to solve optimal tracking control problems. The event-triggered control law will be updated only when the triggering conditions are ***, the computational cost can be reduced and the celerity of tracking can be improved. Compared to existing works,novel triggering conditions are designed in this paper. Besides, the stability is guaranteed with less assumption. Neural networks are used to implement the tracking control algorithm. Finally, an example is employed to show the effectiveness of the algorithm.

关键词： Adaptive dynamic programming event-triggered control optimal tracking control

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Polsar Image Feature Extraction Based on Co-Regularization

Polsar Image Feature Extraction Based on Co-Regularization

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiayuan Huang Xiangli Nie Hong Qiao State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Wishart distance of covariance matrices and Euclidean distance of polarimetric features are two important similarity measurements for polarimetric synthetic aperture radar (Pol-SAR) image classification. This paper proposes a feature extraction method by combing the two distances for Pol-SAR image classification. Firstly, two weight graphs are constructed based on the two distances to represent the local information of the data. Specifically, the neighbouring samples are sought in a local region to reduce the computation burden and utilize the spatial information. Then the dimensionality reduction model is constructed based on the two weight graphs and co-regularization. The co-regularization aims to minimize the dissimilarity of low-dimensional features corresponding to two graphs. Finally, the obtained low-dimensional features are used for PolSAR image classification. Experimental results on real PolSAR datasets demonstrate the effectiveness and superiority of the proposed method.

关键词： Feature extraction Radar polarimetry Covariance matrices Euclidean distance Dimensionality reduction Measurement Matrix decomposition

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Path Planning Based on Segmented Bezier Curves and A* Algorithm for Mobile Robot

Path Planning Based on Segmented Bezier Curves and A* Algori...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Xiangrui Meng Shuang Liang Zhiqiang Cao Zhonghui Li Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Information Science Academy China Electronics Technology Group Corporation Beijing China Beijing Engo Technology Co. Ltd Beijing China

ISBN: (纸本)9781665405362

Path planning is an important problem for mobile robot in complex environments. In this paper, an optimized path planning method based on segmented Bezier curves and A* algorithm is proposed. The result of A* algorithm is used to guide the direction of segmented Bezier curves, which fit the A* path. The segmented Bezier curves are generated to meet the requirements of fitting index with the consideration of constraints from the environment and robot itself. The set of the optimized Bezier curves for each segmented A* path is connected end to end and regarded as the result of path planning for the mobile robot. The effectiveness of the proposed method is verified through the experiments.

关键词： Smoothing methods Conferences Biomimetics Fitting Path planning Mobile robots Indexes

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Learning Individual Features to Decompose state Space for Robotic Skill Learning

Learning Individual Features to Decompose State Space for Ro...

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Chinese control and Decision Conference, CCDC

作者： Fengyi Zhang Fangzhou Xiong Zhiyong Liu State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science Beijing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Due to suffering from the diversity and complexity of robotic tasks in continuous domains, robotic skill learning is the most challenging issue in this area, especially for robots with high-dimensional state spaces. To learn structured policies for continuous control, the graph neural networks (GNN) was previously applied to incorporate explicitly the robot structure into the policy network. In this work, we tackle the problem of robotic skill learning in high-dimensional state space with the help of graph neural networks. Instead of utilizing a general purpose multi-layer perceptron (MLP) as a unified controller to output actions for all joints of the robot, we construct a separate controller for each joint of the robot by using the individual features that have been extracted by GNN model. Empirical results on simulated continuous systems, including applications to PR2 task and Centipede task, demonstrate that the proposed framework can achieve satisfactory learning performance, and more importantly, it significantly reduces the parameters of the policy network.

关键词： Robots Task analysis Aerospace electronics Feature extraction Training Biological neural networks

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K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Gong, Xiaoyan Tang, Ying Liu, Xiwei Jing, Sifeng Cui, Wei Liang, Joleen Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100084 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

Nowadays, AI education from Kindergarten to 9th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts. © 2020 IEEE.

关键词： Curricula

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A Parallel Framework for Scene Understanding

A Parallel Framework for Scene Understanding

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wenwen Zhang Jiangong Wang Kunfeng Wang School of Software Engineering Xi'an Jiaotong University Xi'an China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China College of Information Science and Technology Beijing University of Chemical Technology Beijing China

Different related computer vision tasks make it possible to understand and analyze the scene totally. However, there is no useful framework combining different tasks for scene understanding. In this paper, we investigate the current state of scene understanding from different aspects relying on different computer vision tasks and propose an effective framework based on the theory of parallel vision to combine different tasks for scene understanding. With the maintained artificial scene throughout time from the proposed framework, we can understand the scene in an elegant way. To explain our proposed framework, a case study is given.

关键词： Computer vision Digital twin Conferences Task analysis

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Hierarchical Learning from Demonstrations for Long-Horizon Tasks

Hierarchical Learning from Demonstrations for Long-Horizon T...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Boyao Li Jiayi Li Tao Lu Yinghao Cai Shuo Wang China Academy of Launch Vehicle Technology Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China

Although reinforcement learning (RL) has achieved great success in robotic manipulation skills learning, it is still challenging for long-horizon tasks. Combining RL with demonstrations is an effective solution. In this paper, we propose a novel hierarchical learning from demonstrations method for long-horizon tasks, which leverages (i) object-centered segmentation of demonstrations to automatically segment the teaching trajectories into episodes. (ii) a bi-level hierarchical imitation learning method with a parallel training mechanism to train the two-level policies simultaneously. Experimental results on three challenging long-horizon tasks with sparse rewards show that our proposed method significantly outperforms state-of-art approaches in terms of both sample-efficiency and success rate. Moreover, our method is the only one which achieves satisfactory performance in tasks of multi-object stack and multi-object push&stack.

关键词： Deep learning Training Learning systems automation Conferences Reinforcement learning Trajectory

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An Obstacles Avoidance Algorithm Based on Improved Artificial Potential Field 17

An Obstacles Avoidance Algorithm Based on Improved Artificia...

引用

17th IEEE International Conference on Mechatronics and automation, ICMA 2020

作者： Lu, SiXi Li, En Guo, Rui Yuquan Road Beijing100049 China Institute of Automation Chinese Academy of Science State Key Laboratory of Management and Control for Complex System 95 East ZhongGuanCun Road Beijing China State Grid Shandong Electric Power Company Jinan250001 China

ISBN: (纸本)9781728164151

Obstacle avoidance is one of the most important issue in the motion planning and control of robot. There are many algorithms to avoid obstacles, but obstacle avoidance algorithm for manipulator is needed consider not only end effector, but also all links of manipulator. In terms of real-time and speed of all system, the algorithm also should be easy as possible. In this paper, an algorithm based on the improved artificial potential field is proposed to solve the problem of the local optimum. The idea of improvement is to find a path by fitting curve with target point and critical oscillating points. There is also a comparison in this paper with planning a path by tangent line. The conclusion proves that the path is much smoother by the algorithm based on improved artificial potential field in this paper. Furthermore, the problem of judder on the trajectory of traditional artificial potential field is also solved in this paper by an improved repulsive potential field function. Finally, the algorithm is proved by planning a smooth path for obstacle avoidance on manipulator: KinovaGen3 in MATLAB. © 2020 IEEE.

关键词： Manipulators

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Digital Twin and Its Application in Transportation Infrastructure

Digital Twin and Its Application in Transportation Infrastru...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yan Gao Shuyue Qian Zihan Li Ping Wang Feiyue Wang Qing He Key Laboratory of High-Speed Railway Engineering Ministry of Education Southwest Jiaotong University School of Civil Engineering Southwest Jiaotong University Chengdu China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

A prevailing challenge lies in how to properly design, construct and maintain transportation infrastructure engineering. Due to the development of sensor technology and computing technology, the amount of equipment monitoring data has multiplied and it causes difficulties in real-time state assessment and prediction under the traditional modeling condition. The introduction of Digital Twin technology, which aims to reflect the performance of the real-world product by simulating a virtual space, can solve these problems effectively. This paper reviews recent applications of four types of transportation infrastructure: railways, highways, bridges, and tunnels. Also, the existing research gaps are identified.

关键词： Road transportation Digital twin Transportation Predictive models Real-time systems Rail transportation Data models

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