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2016 4th IEEE International Conference on Cloud Computing and Intelligence systems（IEEE CCIS2016）

作者： Qingchao Kong Wenji Mao Chunyang Liu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences CNCERT/CC

ISBN: (纸本)9781509012572

The interactive behavior of Web users often makes some online contents more popular than others. Thus the popularity of online contents can help us understand public interest and attention behind user interactions. Modeling and predicting the popularity of online contents is an important research topic and can facilitate many practical applications in different domains. Previous work on popularity modeling and prediction usually treat each online content separately, and neglect the interaction information between online contents, represented as interaction relations. In this paper, we explore the interaction relations between online contents, specifically competition and cooperation relations, for popularity prediction. We first define the interaction relations between different online contents and propose a method for the calculation of interaction information. We then apply the non-negative matrix factorization（NMF） technique to get a low dimensional representation of interaction features for online contents, which are used by classifiers for popularity prediction. We finally evaluate the proposed approach using two datasets from Sina Weibo（i.e., original tweets and topic hashtags）. The experimental results show that interaction features alone can yield relatively good performance, and by incorporating interaction features into traditional feature based methods, our method can further improve popularity prediction results.

关键词： popularity prediction interactions of online contents user interactions social media analytics

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Optimal Innovation-Based Deception Attack on Remote state Estimation

Optimal Innovation-Based Deception Attack on Remote State Es...

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American control Conference

作者： Shuang Wu Ziyang Guo Dawei Shi Karl Henrik Johansson Ling Shi Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology 100081 China ACCESS Linnaeus Centre School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781509045839

The security issue in cyber-physical systems has attracted growing interests in the last decades. This paper considers how false data injection attack can degrade the estimation quality of a remote state estimation system. In this system, smart sensors measure a dynamic process and send preprocessed data through a communication network to a remote estimator to estimate the process. It is assumed that there are malicious attackers in the communication network, who are able to obtain and falsify all the data sent by the sensors. It is common that the remote estimator is equipped with a residue-based detector to detect potential attacks. We propose a class of deception attack and analyze its feasibility. We show that the proposed attack enables the attacker to inject false data into the remote estimator without being detected. We derive a criterion to judge the optimality of performance of this type of attack in the sense of maximizing the estimation error covariance. Furthermore, we find that a simple linear attack strategy, which flips the sign of intercepted signal, satisfies the optimality criterion. We present numerical examples to illustrate our theoretical results.

关键词： Cyber-physical systems (CPS) deception attack remote state estimation

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Predicting User's Multi-Interests With Network Embedding in Health-Related Topics

Predicting User's Multi-Interests With Network Embedding in ...

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International Joint Conference on Neural Networks

作者： Zhipeng Jin Ruoran Liu Qiudan Li Daniel D. Zeng YongCheng Zhan Lei Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Management Information Systems University of Arizona

ISBN: (纸本)9781509006212

With the rapid growth of Web 2.0, social media has become a prevalent information sharing and seeking channel for health surveillance, in which users form interactive networks by posting and replying messages, providing and rating reviews, attending multiple discussion boards on health-related topics. Users' behaviors in these interactive networks reflect users' multiple interests. To provide better information service for users, it is necessary to analyze the user interactions and predict users' multi-interests. Most existing work in predicting users' multi-interests based on multi label network classification focuses on using approximate inference methods to leverage the dependency information to improve classification results. Inspired by deep learning techniques, DEEPWALK learns label independent latent representations of vertices in a network using local information obtained from truncated random walks, which provides an efficient way for predicting users multi-interests from user interactions. In this paper, we develop a user's multi-interests prediction model based on DEEPWALK, weight information of user interactions is considered when modeling a stream of short constrained random walks and SkipGram is employed to generate more accurate representations of user vertices, which help identify users' interests. Experimental results on two real world health-related datasets show the efficacy of the proposed model.

关键词： User interaction network Multi-interests prediction Weight information DEEPWALK Consumers user-computer interaction interactive network Information Services local information. Information Sharing Random walk

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Improvement of reliable H control for discrete-time fuzzy systems with time-varying delays

Improvement of reliable H control for discrete-time fuzzy sy...

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2016 IEEE International Conference on Fuzzy systems, FUZZ-IEEE 2016

作者： Wang, Shenquan Jiang, Yulian Li, Yuanchun Liu, Derong College of Electrical and Electronic Engineering Changchun University of Technology Changchun130012 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781509006250

This paper is concerned with the reliable H control problem for discrete-time Takagi-Sugeno (T-S) fuzzy systems with time-varying delays and stochastic actuator faults based on a novel summation inequality. A discrete-time homogeneous Markov chain is used to represent the stochastic behavior of actuator faults. By employing a fuzzy basis-dependent Lyapunov functional and the novel summation inequality, an improved sufficient condition is established to ensure that the resultant closedloop system is stochastically stable with an H performance index. Meanwhile, the solvability condition for the reliable H control is also established, by which the reliable H fuzzy controller can be solved from linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the present approach. © 2016 IEEE.

关键词： Linear matrix inequalities

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Real-time Depth-based Tracking Using A Binocular Camera

Real-time Depth-based Tracking Using A Binocular Camera

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World Congress on Intelligent control and automation

作者： Leijie Zhang Zhiqiang Cao Xiangrui Meng Chao Zhou Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467384155

Depth map provides rich information and it can be utilized in object tracking to handle some challenging problems in conventional RGB tracking such as occlusions and model drift. In this paper, we present a tracker that provides an effective real-time target tracking method based on a binocular camera. The proposed tracker is an extension of the popular KCF algorithm that leverages a circulant structure of tracking-by-detection with kernels for tracking. On this basis, we design a simple yet effective method to detect occlusions and recover tracking using noisy depth map obtained from a binocular camera. Firstly, one needs to identify exact and reasonable peak number of the target region's depth histogram and apply GMM model to evaluate the depth distribution. The occlusion is then detected based on the depth evaluation and the maximum response from KCF. When the occlusion happens, it is segmented to build the corresponding search region for recovery. The experimental results demonstrate the effectiveness of the proposed method and the superiority to the KCF tracker.

关键词： Target tracking Kernel Cameras Matrices Algorithm design and analysis Histograms Correlation

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A Robot Pose Estimation Approach Based on key Feature Registration

A Robot Pose Estimation Approach Based on Key Feature Regist...

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World Congress on Intelligent control and automation

作者： Wenbo Yuan Tianzhu Wang Zhiqiang Cao Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467384155

Pose estimation is a fundamental issue in the research of mobile robot. A robot pose estimation approach based on key feature registration is proposed in this paper. The key features in the environment are extracted firstly. When dealing with many non-corresponding points, registration has a slow convergence rate, and gains poor performance. To solve these problems, a key Feature Based Accelerated Iterative Closest Point (KF-AICP) algorithm is presented for key feature registration. Then the current robot pose can be estimated based on the result of registration. The dataset is established using 2D laser radar in indoor scene. The experimental results show that the proposed approach achieves good accuracy and real-time in pose estimation of mobile robot. The validity of proposed approach is verified.

关键词： Iterative closest point algorithm Pose estimation Laser radar Feature extraction Mobile robots Data models

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A Novel Model Analysis Method and Dynamic Modelling for Hybrid Structure Flexible Manipulator

A Novel Model Analysis Method and Dynamic Modelling for Hybr...

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IEEE International Conference on Mechatronics and automation

作者： Teng Long En Li Guodong Yang Zize Liang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781509023974

Tip deflection and vibration have a large impact on the manipulator operation safety, and hybrid structure flexible manipulator has a larger workspace and more flexibility. In this paper, a new modeling scheme for a hybrid structure flexible manipulator is proposed. In the proposed scheme, the joint variables of hybrid structure flexible manipulator are divided in rigid joint angle and small joint angle deviations. The prismatic joint is divided into three segments according to the structure features of flexible manipulator. The three segments are the foundation segment, overlap segment and extension segment. Each segment is modeled by the theory of the Euler-Bernoulli beam, and the elastic deflection is described by assumed mode method, the kinetic energy and potential energy of every segment is analyzed respectively. Base on the previous researches of modeling for multi-link flexible manipulator, the dynamic equations of the manipulator are obtained by using the Lagrangian approach. In order to verify the correctness of model and analyze the characteristics of model, the comparative simulation experiments are designed. Tip vibration, angle deviation, vibration frequency and amplitude are observed, and the characteristics of the model are analyzed according to the experiment results.

关键词： model analysis dynamic modelling hybrid structure flexible manipulator

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A novel policy iteration based deterministic Q-learning for discrete-time nonlinear systems

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Science China(Information Sciences) 2015年第12期58卷 147-161页

作者： WEI QingLai LIU DeRong State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

In this paper, a novel iterative Q-learning algorithm, called "policy iteration based deterministic Qlearning algorithm", is developed to solve the optimal control problems for discrete-time deterministic nonlinear systems. The idea is to use an iterative adaptive dynamic programming(ADP) technique to construct the iterative control law which optimizes the iterative Q function. When the optimal Q function is obtained, the optimal control law can be achieved by directly minimizing the optimal Q function, where the mathematical model of the system is not necessary. Convergence property is analyzed to show that the iterative Q function is monotonically non-increasing and converges to the solution of the optimality equation. It is also proven that any of the iterative control laws is a stable control law. Neural networks are employed to implement the policy iteration based deterministic Q-learning algorithm, by approximating the iterative Q function and the iterative control law, respectively. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming Q learning policy iteration neural networks nonlinear systems optimal control

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Motion detection based on improved Sobel and ViBe algorithm

Motion detection based on improved Sobel and ViBe algorithm

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Chinese control Conference (CCC)

作者： Yujia Zhang Xiaoguang Zhao Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Beijing CN

Motion detection plays a crucial role in most video based applications. A particular background subtraction technique called ViBe (Visual Background Extractor) is commonly used to obtain foreground objects from the background due to its high detection rate and low computational complexity. However, the performance is not very satisfying. Therefore, this paper presents an improved ViBe algorithm to increase the accuracy and robustness of motion detection. Specifically, a foreground feature map is created by optimizing the result of ViBe algorithm. Then the edge detection of the original video frames is achieved after pre-sharpening using improved Sobel operator and Otsu algorithm. Finally, by feature fusion (of the foreground and background feature maps) and contour filling, the motion detection results can be obtained. The experiments demonstrate the improvements of the proposed modifications at a limited additional cost.

关键词： Image edge detection Motion detection Feature extraction Filtering algorithms Signal processing algorithms Maximum likelihood detection Nonlinear filters

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Assessment for spatial driving forces of HFMD Prevalence in Beijing, China 2

Assessment for spatial driving forces of HFMD Prevalence in ...

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2nd ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency management, EM-GIS 2016

作者： Wang, Jiaojiao Wang, Quanyi Cao, Zhidong Wang, Xiaoli Zeng, Daniel Dajun State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences No. 95 Zhongguancun East Road Haidian District Beijing China Beijing Center for Disease Prevention and Control No.16 He Pingli Middle Street Dongcheng District Beijing China

ISBN: (纸本)9781450345804

Hand-foot-mouth disease (HFMD) outbreak greatly threatened Beijing city, the capital city of China, in 2008. The control prevention of HFMD has become an urgent mission for Beijing Center for Disease control and Prevention and a focus problem for the citizens. Medical, social and environmental situations account for much of HFMD morbidity. The spatial driving forces of HFMD occurrence vary across geographical regions, whereas the factors that play a significant role in HFMD prevalence may be concealed by global statistics analysis. This study aims at the identification of the association between the spatial driving forces and HFMD morbidity across the study area and the epidemiological explanation of the results. HFMD spatial driving forces are represented by 6 factors which was obtained by Pearson Correlation analysis and Stepwise Regression method. Compared to Classical Linear Regression Model (CLRM), Geographically weighted regression (GWR) techniques were implemented to predict HFMD morbidity and examine the nonstationary of HFMD spatial driving forces. Informative maps of estimated HFMD morbidity and statistically significant spatial driving forces were generated and rigorously evaluated in quantitative terms. Prediction accuracy by GWR was higher than that by CLRM. The residual led to by CLRM suggested a significant degree of spatial dependence, while that by GWR indicated no significant spatial dependence. In the three regions plotted by Beijing city Ring Roads, HFMD morbidity was found to have significantly positive or negative association with the 6 kinds of spatial driving forces. GWR model can effectively represent the spatial heterogeneity of HFMD driving forces, significantly improve the prediction accuracy and greatly decrease the spatial dependence. The results improve current explanation of HFMD spread in the study area and provide valuable information for adequate disease intervention measures. CCS Concepts •Modeling and simulation → Model verificat

关键词： Diseases

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