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3rd IFAC Workshop pn Cyber-Physical and Human systems (CPHS)

作者： Zhang, Mei Zhang, Zhicheng Wang, Xiao Yu, Hui Xia, Yifan Tan, Kanran Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Peoples Liberat Army Gen Hosp Dept Med Ctr 7 Beijing 100700 Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth Hants England Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA

With the development of augmented reality (AR) technologies, more and more approaches are proposed for medical applications. With the help of AR technology, the doctor can highly improve the spatial perception and obtain more infounation beneath the displayed image. Generally, 3D reconstruction, registration, tracking and depth visualization are all important for an AR system, which directly deteunine the accuracy of the system. However, none of recent systems have achieved a perfect 3D vision for doctors. It is particularly challenging for the AR system to perform well for operation on highly defounable body tissues. Therefore, there is an urgent need to improve the 3D vision and intelligence of AR systems. In this paper, we provide a review of the recent development of augmentation technologies for surgery and medical treatment. And then, we introduce the parallel intelligence theory into AR systems, which can provide a feasible approach to enhance the efficiency. Copyright (C) 2020 The Authors.

关键词： Potential impact of automation and open problems Augmented reality Medical treatment Parallel theory 3D technology

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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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第32届中国控制与决策会议

作者： Fengyi Zhang Fangzhou Xiong Zhiyong Liu State Key Lab of Management and Control for Complex Systems Institute of AutomationChinese Academy of Science School of Artificial Intelligence University of Chinese Academy of Sciences(UCAS) CAS Centre for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences

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 *** learn structured policies for continuous control,the graph neural networks(GNN) was previously applied to incorporate explicitly the robot structure into the policy *** this work,we tackle the problem of robotic skill learning in high-dimensional state space with the help of graph neural *** 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 *** 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： Robotic Skill Learning Graph Neural Networks state Decomposition

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Decentralized Autonomous Organizations: Concept, Model, and Applications

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2019年第5期6卷 870-878页

作者： Wang, Shuai Ding, Wenwen Li, Juanjuan Yuan, Yong Ouyang, Liwei Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Beijing Jintong Network Investment Co Ltd AI Business Div Beijing 100101 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China

Decentralized autonomy is a long-standing research topic in information sciences and social sciences. The self-organization phenomenon in natural ecosystems, the Cyber Movement Organizations (CMOs) on the Internet, and the Distributed Artificial Intelligence (DAI), and so on, can all be regarded as its early manifestations. In recent years, the rapid development of blockchain technology has spawned the emergence of the so-called Decentralized Autonomous Organization [DAO, sometimes lab.led as Decentralized Autonomous Corporation (DAC)], which is a new organization form that the management and operational rules are typically encoded on blockchain in the form of smart contracts, and can autonomously operate without centralized control or third-party intervention. DAO is expected to overturn the traditional hierarchical management model and significantly reduce organizations & x2019;costs on communication, management, and collab.ration. However, DAO still faces many challenges, such as security and privacy issue, unclear legal status, and so on. In this article, we strive to present a systematic introduction of DAO, including its concept and characteristics, research framework, typical implementations, challenges, and future trends. Especially, a novel reference model for DAO which employs a five-layer architecture is proposed. This article is aimed at providing helpful guidance and reference for future research efforts.

关键词： Organizations Blockchain Smart contracts Internet Peer-to-peer computing Collab.ration Blockchain consensus mechanism decentralized autonomous organizations (DAO) smart contracts

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HackRL: Reinforcement learning with hierarchical attention for cross-graph knowledge fusion and collab.rative reasoning

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KNOWLEDGE-BASED systems 2021年 233卷 107498-107498页

作者： Yang, Linyao Wang, Xiao Dai, Yuxin Xin, Kejun Zheng, Xiaolong Ding, Weiping Zhang, Jun Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Nanjing Joinmap Data Res Inst Nanjing 211100 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

Reasoning aiming at inferring implicit facts over knowledge graphs (KGs) is a critical and fundamental task for various intelligent knowledge-based services. With multiple distributed and complementary KGs, the effective and efficient capture and fusion of knowledge from different KGs is becoming an increasingly important topic, which has not been well studied. To fill this gap, we propose to explore cross-KG relation paths with the anchor links identified by entity alignment for the knowledge fusion and collab.rative reasoning of multiple KGs. To address the heterogeneity of different KGs, this paper proposes a novel reasoning model named HackRL based on the reinforcement learning framework, which incorporates the long short-term memory and hierarchical graph attention in the policy network to infer indicative cross-KG relation paths from the history trajectory and the heterogeneous environment for predicting corresponding relations. Meanwhile, an entity alignment oriented representation learning method is utilized to embed different KGs into a unified vector space based on the anchor links to reduce the impact of distinct vector spaces, and two training mechanisms, action mask and retrain with sampled paths, are proposed to optimize the training process to learn more successful indicative paths. The proposed HackRL is validated on three cross-lingual datasets built from DBpedia on the link prediction and fact prediction tasks. Experimental results demonstrate that HackRL achieves better performance on most tasks than existing methods. This work provides an industrially-applicable framework for fusing distributed KGs to make better decisions. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Knowledge fusion Knowledge reasoning Decision-making Hierarchical graph attention Reinforcement learning

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Comparative Study of Four Speed controllers of Brushless DC Motors for Industrial Applications 3rd

Comparative Study of Four Speed Controllers of Brushless DC ...

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3rd IFAC Workshop pn Cyber-Physical and Human systems (CPHS)

作者： Tamir, Tariku Sinshaw Xiong, Gang Shen, Zhen Gong, Xiaoyan Liu, Sheng Lodhi, Ehtisham Wan, Li Dong, Xisong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Donggguan 523808 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Ten Dimens Guangdong Technol Co Ltd Foshan 528225 Peoples R China

Direct current (DC) motors are one of the most important kind of motors and are widely used in robotic and industrial applications. Recently, there have been significant efforts to develop direct current (DC) motors in an attempt to control speed of motors. However, conventional controlling approaches perform undesirably in terms of stability and quick response. Therefore, this paper presents a hybrid intelligent controller configuration for optimized speed control of brushless direct current (BLD C) motors in a factory supervisory control data acquisition (SCADA) system. We compare this hybrid intelligent controller with a conventional PID controller, fuzzy logic controller (FLC), and artificial neural network model reference controller (ANNMRC) in M4 Tlab. and the results show that the hybrid (neuro-fuzzy) controller performs superior in terms of stability, speed trajectory tracking capability, fast response, and simplicity for implementation. Copyright (C) 2020 The Authors.

关键词： BLDC motor fuzzy logic controller (FLC) artificial neural network model reference controller (ANNMRC) hybrid (neuro-fuzzy) control

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A decentralized social networking architecture enhanced by blockchain

A decentralized social networking architecture enhanced by b...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Zeng, Shuai Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Research Center of Military Computational Experiments and Parallel System National University of Defense Technology Changsha China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728128535

The privacy issues become a major problem that should be resolved for the existing centralized online social networks, which have prompted researchers to consider the decentralization framework for online social networks. In this paper, we propose a novel decentralized social networking architecture enhanced by blockchain technology. We use a sharding framework to increase the system scalab.lity, a blockchain system to ensure the data integrity and consistency, a reputation-based authority control method to improve the system security. © 2019 IEEE.

关键词： Blockchain

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Social Manufacturing: A Paradigm Shift for Smart Prosumers in the Era of Societies 5.0

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2019年第5期6卷 822-829页

作者： Wang, Fei-Yue Shang, Xiuqin Qin, Rui Xiong, Gang Nyberg, Timo R. Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan 523808 Peoples R China Aalto Univ Sch Sci Aalto 00076 Finland

Welcome to the fifth issue of the IEEE Transactions on Computational Social systems (TCSS) this year. Seventeen regular articles and a brief discussion on social manufacturing (SM) are presented here. In addition, a special issue on & x201C;Human-Centric Cyber Social Computing & x201D;is *** would like to take the opportunity to thank the Guest Editors for their time and effort devoted to the special issue.

关键词： Manufacture

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Heuristic rank selection with progressively searching tensor ring network

arXiv

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arXiv 2020年

作者： Li, Nannan Pan, Yu Chen, Yaran Ding, Zixiang Zhao, Dongbin Xu, Zenglin 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 Beijing101408 China Shenzhen China Pengcheng Lab Shenzhen China

Recently, Tensor Ring Networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performance of TRNs, rank selection is seldom studied in previous works and usually set to equal in experiments. Meanwhile, there is not any heuristic method to choose the rank, and an enumerating way to find appropriate rank is extremely time-consuming. Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region. Therefore, based on the above phenomenon, we propose a novel progressive genetic algorithm named Progressively Searching Tensor Ring Network Search (PSTRN), which has the ability to find optimal rank precisely and efficiently. Through the evolutionary phase and progressive phase, PSTRN can converge to the interest region quickly and harvest good performance. Experimental results show that PSTRN can significantly reduce the complexity of seeking rank, compared with the enumerating method. Furthermore, our method is validated on public benchmarks like MNIST, CIFAR10/100, UCF11 and HMDB51, achieving the state-of-the-art performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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Optimize Student Learning via Random Forest-Based Adaptive Narrative Game

Optimize Student Learning via Random Forest-Based Adaptive N...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Ryan Hare Ying Tang Wei Cui Joleen Liang Department of Electrical and Computer Engineering Rowan University Glassboro USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China Squirrel AI Learning Shanghai China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge relative to the problem presented to him through his game-playing behavior data, such as time taken to find solutions, errors in solutions, and emotional indicators. Hints, prompts, and/or individualized lessons are then offered to the player to guide their learning in a positive and productive direction. Our preliminary pilot study demonstrates that the model can make accurate classifications, from which proper assistance can then be provided to individual students as they play.

关键词： Games Databases Forestry Education Problem-solving Presses Artificial intelligence

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Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks 3rd

Traffic Congestion Prediction using Decision Tree, Logistic ...

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3rd IFAC Workshop pn Cyber-Physical and Human systems (CPHS)

作者： Tamir, Tariku Sinshaw Xiong, Gang Li, Zhishuai Tao, Hao Shen, Zhen Hu, Bin Menkir, Heruye Mulugeta Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Cloud Comp Ctr Guangdong Engn Res Ctr 3D Printing & Intelligent Dongguan 523808 Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Debremarkos Univ Inst Technol Debremarkos 269 Ethiopia

Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in clementine environment and decision tree outperforms all other models with an accuracy of 97.65%. Copyright (C) 2020 The Authors.

关键词： Confusion matrix Data preprocessing Python TensorFlow Clementine environment ITS

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