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Where Does AlphaGo Go: From Church-Turing Thesis to AlphaGo Thesis and Beyond

IEEE/CAA Journal of Automatica Sinica 2016年第2期3卷 113-120页

作者： Fei-Yue Wang Jun Jason Zhang Xinhu Zheng Xiao Wang Yong Yuan Xiaoxiao Dai Jie Zhang Liuqing Yang IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences (SKL-MCCS CASIA) Research Center of Computational Experiments and Parallel Systems The National University of Defense Technology the Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer Science University of Denver the Department of Computer Science and Engineering University of Minnesota the Qingdao Academy of Intelligent Industries (QAII) Qingdao Shandong China and SKL-MCCS CASIA the Department of Electrical and Computer Engineering Colorado State University

An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation. © 2014 Chinese Association of automation.

关键词： Religious buildings

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Layers of Shared and Cooperative Control, assistance and automation

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IFAC-PapersOnLine 2016年第19期49卷 159-164页

作者： Pacaux-Lemoine, Marie-Pierre Flemisch, Frank Fraunhofer Institute for Communication Information Processing and Ergonomics FKIE Fraunhoferstrasse 20 Wachtberg53343 Germany Laboratory of Industrial and Human Automation control Mechanical engineering and Computer Science LAMIH CNRS UMR 8201 University of Valenciennes France

Over the last centuries we have experienced scientific, technological and societal progress that enabled the creation of intelligent assisted and automated machines with increasing abilities, and require a conscious distribution of roles and control between humans and machines. Machines can be more than either fully automated or manually controlled, but can work together with the human on different levels of assistance and automation in a hopefully beneficial cooperation. One way of cooperation is that the automation and the human have a shared control over a situation, e.g. a vehicle in an environment. The objective of this paper is to provide a common meta model of shared and cooperative assistance and automation. The meta models based on insight from the H(orse)-methaphor (Flemisch et al, 2003; Goodrich et al., 2006) and Human-Machine Cooperation principles (Hoc and Lemoine, 1998; Pacaux-Lemoine and Debernard, 2002; Pacaux-Lemoine, 2014), are presented and combined in order to propose a framework and criteria to design safe, efficient, ecological and attractive systems. Cooperation is presented from different points of view such as levels of activity (operational, tactical and strategic levels) (Lemoine et al, 1996) as well as the type of function shared between Human and machine (information gathering, information analysis, decision selection, action implementation) (Parasuraman et al., 2000). Examples will be provided in the aviation domain (e.g. Goodrich et. al 2012) and the automotive domain with the automation of driving (Hoeger et al, 2008; Flemisch et al., 2016; Tricot et al., 2004; Pacaux-Lemoine et al, 2004; Pacaux-Lemoine et al., 2015). © 2016

关键词： automation Man machine systems Attractive systems Automated machines Co operative control Human centered designs Human machine cooperation Information gathering Multi level systems Shared control

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Joint Optimization of Scheduling and Power Control in Wireless Networks: Multi-Dimensional Modeling and Decomposition

arXiv

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arXiv 2017年

作者： Liu, Lu Cheng, Yu Cao, Xianghui Zhou, Sheng Niu, Zhisheng Wang, Ping Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States School of Automation Southeast University Nanjing210096 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing100084 China School of Computer Engineering Nanyang Technological University 639798 Singapore

—The energy efficiency of future networks is becoming a significant and urgent issue, calling for greener network designs. At the same time, rapid development of wireless networks shows a trend of increasing complexity in network structure and resource space, leading to that optimizing the energy efficiency of such networks requires a joint solution over multi-dimensional resource space. However, the coupled resource dimensions and growing problem scales bring great challenges in obtaining the optimal solutions. In this paper, we develop a multi-dimensional network model on the basis of tuple-links associated with transmission patterns (TPs) and formulate the optimization problem as a TP based scheduling problem which jointly solves transmission scheduling, routing, power control, radio and channel assignment. In order to tackle the complexity issues raised from coupled resource dimensions, we propose a novel algorithm that decomposes the coupling scheduling and power control by exploiting the delay column generation technique to recursively solve a master problem for scheduling and a sub-problem for power allocation. Further, we theoretically prove that the performance gap between the proposed algorithm and the optimum is upper bounded by that for the sub-problem solution, where the latter is derived by solving a relaxed version of the sub-problem. Numerical results demonstrate the effectiveness of the multi-dimensional framework and the benefit of the proposed joint optimization in improving network energy efficiency. Copyright © 2017, The Authors. All rights reserved.

关键词： Energy efficiency

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On the nonexistence of event-based triggers that preserve Gaussian state in presence of package-drop

On the nonexistence of event-based triggers that preserve Ga...

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American Control Conference

作者： Enoch Kung Junfeng Wu Dawei Shi Ling Shi Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Automatic Control The Royal Institute of Technology (KTH) State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

ISBN: (纸本)9781509045839

State estimation is a core objective in cyber-physical systems. In the state estimation problem over linear systems, the Kalman filter is the standard solution. The filter is the format on which the solutions to subsequent estimation problems are based. Among these problems are the estimation problem in the presence of packet drops and estimation problem involving event-based triggers. We study in this paper both phenomena simultaneously. In an attempt to find the Kalman-like filter, which proves the Gaussianity of the state and offers a set of update equations, our paper shows that no such filter exists. More precisely, one cannot find an event-based trigger such that under possible packet drops, the state variable remains a Gaussian variable. This conclusion can be reasonably extended to a more general setting.

关键词： State estimation Packet drops Event-based triggers State estimation Event-based Filters State variable TRIGGERS Gauss

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Combined data mining techniques based patient data outlier detection for healthcare safety

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International Journal of Intelligent Computing and Cybernetics 2016年第1期9卷 42-68页

作者： Gebeyehu Belay Gebremeskel Chai Yi Zhongshi He Dawit Haile State Key Laboratory of Power Transmission Equipment System Security and New TechnologyCollege of AutomationChongqing UniversityChongqingChina College of Computer Science Chongqing UniversityChongqingChina School of Engineering Science and TechnologyVirginia State UniversityPetersburgVirginiaUSA

Purpose–Among the growing number of data mining(DM)techniques,outlier detection has gained importance in many applications and also attracted much attention in recent *** the past,outlier detection researched papers appeared in a safety care that can view as searching for the needles in the ***,outliers are not always ***,the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care,in ***/methodology/approach–It is a combined DM(clustering and the nearest neighbor)technique for outliers’detection,which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare *** outcomes or the knowledge implicit is vitally essential to a proper clinicaldecision-making *** method isimportant to thesemantic,andthe novel tactic of patients’events and situations prove that play a significant role in the process of patient care safety and ***–The outcomes of the paper is discussing a novel and integrated methodology,which can be inferring for different biological data *** is discussed as integrated DM techniques to optimize its performancein the field of health and *** is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient *** on these facts,outliers are detected as clusters and point events,and novel ideas proposed to empower clinical services in consideration of customers’*** is also essential to be a baseline for further healthcare strategic development and research *** limitations/implications–This paper mainly focussed on outliers *** isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not ***,the research can be extended more about the hierarchy of patient prob

关键词： Data mining Clustering Healthcare Mining algorithm Nearest neighbor Outlier detection

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Identification of Nonlinear State-Space Models Using Joint State Particle Smoothing 35

Identification of Nonlinear State-Space Models Using Joint S...

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第35届中国控制会议

作者： GENG Li-Hui NINNESS Brett Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education School of Electrical Engineering and Computer Science The University of Newcastle

ISBN: (纸本)9781509009107

On the basis of a previous expectation maximization(EM) algorithm, this paper applies the particle Markov chain Monte Carlo(MCMC) technique to estimate nonlinear state-space models(SSMs). The smoothed simultaneous particles for joint states are generated by importance resampling(IR) and are directly used to compute a log-likelihood function and its Jacobian vector and Hessian matrix. As a result, a relatively less computational load can thus be obtained, which is crucial to practical applications of the particle MCMC. In addition, the SSM parameters can be recursively iterated by the more efficient Newton method, in which an optimal step length for parameter update in each iteration is optimized by a 1-D search method. This presented identification method has a good global parameter convergence in case iteration parameters are initialized from quite large intervals of their respective true values. Finally, a numerical simulation for identification of a classical SSM is used to show the effectiveness of the studied identification method.

关键词： Nonlinear state-space models Smoothed simultaneous particles Importance resampling Newton method

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Data-based Networked Tracking Control System with Time Delay in the Forward Channel 35

Data-based Networked Tracking Control System with Time Delay...

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第35届中国控制会议

作者： TONG Shiwen QIAN Dianwei WANG Song JIANG Shan TIAN Kuo FANG Jianjun DENG Xianrui College of Automation Beijing Union University School of Computer and Control Engineering North China Electric Power University Beijing Key Laboratory of Information Service Engineering China United Engineering Corporation Beijing BBEF Science & Technology Co. Ltd. Department of Computer Science Tangshan Normal University

ISBN: (纸本)9781509009107

Networked Tracking Control(NTC) is a challenging problem,especially for the unknown model system that only input-output data are expected to be *** paper has proposed a Data-based Networked Tracking Control(DNTC)*** method uses fuzzy cluster modeling technology to establish the predictive model of the process according to the input-output *** the differences between the desired outputs and the predicted outputs of the process are treated as inputs of a Fuzzy Sliding Mode Controller(FSMC) to obtain a string of future control *** control actions are packed and transferred from the controller side to the plant ***,the time delays in the forward communication channel are compensated by selecting appropriate control action in the plant *** outputs show rapid response and good accuracy of the proposed method.

关键词： Networked tracking control fuzzy cluster modeling fuzzy sliding mode control time delay

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On-line Monitoring for Coal Low Heating Value in Coal-fired Power Plant Boiler Based on CM-LSSVM-PLS 35

On-line Monitoring for Coal Low Heating Value in Coal-fired ...

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第35届中国控制会议

作者： Shi Yuanhao Wang Jingcheng Ren Yifeng School of Computer Science and Control Engineering North University of China Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

ISBN: (纸本)9781509009107

A novel comprehensive real-time on-line calculation model of coal's Low Heating Value(LHV) in coal-fired boiler is presented in this ***,the training operational data is partitioned into several subsets by means of C-Means cluster ***,the Least Squares Support Vector Machine(LS-SVM) method is used to training the sub-models in each ***,the sub-models are combined into one model based on Partial Least Squares algorithm(PLS).The models' effectiveness is illustrated by the validation of online simulation in which the real operation data is *** result shows that it is potential to considerably fulfill not only the accuracy of online monitoring of LHV of the coal into the boiler,but also the real-time computations that are supplied by using indirect heat balance approach.

关键词： Coal-fired Power Plant Boiler Thermal Efficiency Low Heating Value C-Means Cluster LS-SVM PLS

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GSVM-based proteochemometrics modeling for prediction of kinase-inhibitor interaction

International Journal of Control and Automation

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International Journal of Control and automation 2016年第10期9卷 123-134页

作者： Wang, Yiqi Chen, Qingfeng Wang, Chaohong Liang, Yan School of Computer Electronic and Information Guangxi University Nanning530004 China State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources Guangxi University China School of Automation and Information Engineering Qingdao University of Science and Technology 266061 China

Life activity is closely related to the dynamic change of protein. Protein Phosphorylation is one of the most important proc GSVM-Based Proteochemometrics Modeling (PCM) for Prediction of Kinase-inhibitor Interaction within the protein modification after translation. It is found that more than 30% proteins can be phosphorylated. Abnormal protein kinases can lead to diverse diseases, such as cancers. Kinase inhibition is an effective method for disease treatment. However, some inhibitors are able to interact with several kinases that hidden but interesting kinase/inhibitor relationships may be included. Use of multi-targeted mining that select inhibitors act on a group of kinases increases the chance to achieve clinical antitumor activity. Proteochemometrics is a novel technology to predict inhibitor-kinase interactions from the chemical properties of kinase inhibitors which can help design more selective treatment and show better curative effect and low toxicity. This article uses a novel machine learning method called granular support vector machines (GSVM) to correlate the descriptors of kinase inhibitors and kinases to the interaction activities. GSVM develops on the basis of statistical learning theory and granular computing theory and thus provides an interesting new mechanism to address complex classification problems. Compared with other algorithms, GSVM gets better predictive abilities whose q2=0.89. © 2016 SERSC.

关键词： Forecasting

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Joint depth map interpolation and segmentation with planar surface model 16

Joint depth map interpolation and segmentation with planar s...

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2016 SIGGRAPH ASIA Technical Briefs, SA 2016

作者： Xu, Shibiao Dai, Longquan Zhang, Jiguang Tang, Jinhui Kumar, G. Hemanth Zhang, Yanning Zhang, Xiaopeng National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Computer Science University of Mysore Mysore India School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781450345415

Depth map interpolation and segmentation has been a long-standing problem in computer vision. However, many people treat them as two independent problems. Indeed, the two problems are complementary. The results of one problem can aid in improving the results of the other in powerful ways. Assuming that the depth map consists of planar surfaces, we propose a unified variational formula for joint depth map interpolation and segmentation. Specifically, our model uses a multi-label representation of the depth map, where each label corresponds to a parametric representation of the planar surface on a segment. Using alternating direction method, we are able to find the minimal solution. Experiments show our algorithm outperforms other methods. © 2016 ACM.

关键词： Interpolation

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