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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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Enhanced biologically inspired model for image recognition based on a novel patch selection method with moment

arXiv

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arXiv 2017年

作者： Lu, Yan-Feng Jia, Li-Hao Qaio, Hong Li, Yi Research Center for Brain-inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China School of Information Engineering Nanchang University Nanchang China

Biologically inspired model (BIM) for image recognition is a robust computational architecture, which has attracted widespread attention. BIM can be described as a four-layer structure based on the mechanisms of the visual cortex. Although the performance of BIM for image recognition is robust, it takes the randomly selected ways for the patch selection, which is sightless, and results in heavy computing burden. To address this issue, we propose a novel patch selection method with oriented Gaussian-Hermite moment (PSGHM), and we enhanced the BIM based on the proposed PSGHM, named as PBIM. In contrast to the conventional BIM which adopts the random method to select patches within the feature representation layers processed by multi-scale Gabor filter banks, the proposed PBIM takes the PSGHM way to extract a small number of representation features while offering promising distinctiveness. To show the effectiveness of the proposed PBIM, experimental studies on object categorization are conducted on the CalTech05, TU Darmstadt (TUD), and GRAZ01 databases. Experimental results demonstrate that the performance of PBIM is a significant improvement on that of the conventional BIM. Copyright © 2017, The Authors. All rights reserved.

关键词： Image enhancement

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Discrete-Time Generalized Policy Iteration ADP Algorithm With Approximation Errors

Discrete-Time Generalized Policy Iteration ADP Algorithm Wit...

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IEEE Symposium Series on Computational Intelligence

作者： Qinglai Wei Benkai Li Ruizhuo Song The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

This paper concerns with a novel generalized policy iteration (GPI) algorithm with approximation errors. Approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The convergence of the developed algorithm is established to show that the iterative value function is convergent to a finite neighborhood of the optimal performance index function. Finally, numerical examples and comparisons are presented.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Neuro-dynamic programming Generalized policy iteration Nonlinear systems Optimal control Neural networks Reinforcement learning

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Analysis and forecasting of urban traffic condition based on categorical data

Analysis and forecasting of urban traffic condition based on...

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

作者： Yuan-yuan Chen Yisheng Lv Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9781509029280

Urban traffic prediction is a critical component in intelligent transportation systems for both citizens and traffic management agencies. It is beneficial to know current and future traffic conditions prior a trip or a route for travelers. And it is also very helpful for proactive traffic management for transportation administrative sectors. In this paper, we apply classification techniques to forecast traffic conditions based on categorical data collected from open web maps. To this end, we first collect traffic condition data from AMAP which is a web map, navigation and location based services provider in China. Then we primarily analyze AMAP data with trend analysis and power spectrum analysis. Finally, we employ random walk, naïve Bayes, decision tree and support vector machine methods to forecast traffic conditions in the future based on historical and current conditions. Experimental results demonstrate that it is feasible to make forecast on traffic conditions with reasonable accuracy.

关键词： Decision support systems Intelligent transportation systems Planning Decision trees Support vector machines Market research

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Autonomous vehicles testing methods review

Autonomous vehicles testing methods review

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International Conference on Intelligent Transportation

作者： WuLing Huang Kunfeng Wang Yisheng Lv FengHua Zhu State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China

ISBN: (纸本)9781509018901

Driving test is critical to the deployment of autonomous vehicles. It is necessary to review the related works since the methodologies summaries are rare, which will help to set up an integrated method for autonomous driving test in different development stages, and help to provide a reliable, quick, safe, low cost and reproducible method and accelerate the development of autonomous vehicle. In this paper, we review the related autonomous driving test works, including autonomous vehicle functional verification, vehicle integrated testing, system validation in different architectures. This review work will be helpful for autonomous vehicle development.

关键词： Testing Autonomous vehicles Sensors Safety Computer architecture Planning

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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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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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A Graph Matching Based key Point Correspondence Method for Lunar Surface Images

A Graph Matching Based Key Point Correspondence Method for L...

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World Congress on Intelligent control and Automation

作者： Yuren Zhang Xu Yang Hong Qiao Zhiyong Liu Chuankai Liu Baofeng Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Aerospace Flight Control Center

ISBN: (纸本)9781467384155

key point correspondence plays an important role in lunar surface image processing. Since lunar surface images often contain obvious illumination changes, noisy points and repetitive patterns, traditional appearance based algorithms may fail when local appearance descriptors become less distinctive. In this paper, we introduce a graph matching based algorithm to tackle this problem. First, by incorporating structural information, key point sets in lunar surface images are represented by graphs. Then key point correspondence is formulated as a specific graph matching problem which aims to find a specified number of best assignments, and effectively approximately solved. Finally, an outlier assignment elimination method is proposed based on the affine invariance assumption. Simulations on both benchmark datasets and lunar surface images witness the effectiveness of the proposed method.

关键词： Graph matching lunar surface images Correspondence

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Towards an Esox lucius inspired multimodal robotic fish

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Science China(Information Sciences) 2015年第5期58卷 182-194页

作者： WU ZhengXing YU JunZhi SU ZongShuai TAN Min LI ZhenLong State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Electronic Information and Control Engineering Beijing University of Technology

In this paper, we further explore multimodal locomotion via an updated robotic fish model based on Esox lucius. Besides the improved actuation properties like higher torque servomotors and powerful electronics,the robotic fish has some innovative mechanical design to pursue diverse swimming modes and superior performance. Specifically, we introduced a ±50° yawing head joint that functions as the neck for enhancing turning ability. A pair of pectoral mechanisms with two DOFs per fin is constructed to achieve 3-D swimming and to enrich multiple pectoral motions. At the control level, an improved central pattern generator(CPG) model allowing for free adjustment of the phase relationship among outputs is employed to produce rhythmic signals of multimodal swimming. Extensive experiments were carried out to examine how characteristic parameters in CPGs including amplitude, frequency, and phase lag affect the swimming performance. As a result, the robotic fish successfully performed various locomotion actions such as forward swimming, backward swimming, turning, diving, surfacing, as well as three pectoral motions in the form of pitching, heaving, and *** found that small phase lag between oscillating joints which means large propulsive body wave length and undulation width could lead to a faster swimming in body and/or caudal fin(BCF) locomotion.

关键词： bio-inspired robot robotic fish CPG multimodal swimming motion control

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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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