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arXiv 2018年

作者： Santos, V. Szezech, J.D. Batista, A.M. Iarosz, K.C. Baptista, M.S. Ren, H.P. Grebogi, C. Viana, R.L. Caldas, I.L. Maistrenko, Y.L. Kurths, J. Graduate in Science Program State University of Ponta Grossa Ponta Grossa Paraná84030-900 Brazil Department of Mathematics and Statistics State University of Ponta Grossa Ponta Grossa Paraná84030-900 Brazil Potsdam Institute for Climate Impact Research Potsdam Brandenburg14473 Germany Institute of Physics University of São Paulo São Paulo05508-900 Brazil Department of Physics Humboldt University Berlin Brandenburg12489 Germany Institute for Complex Systems and Mathematical Biology SUPA University of Aberdeen Aberdeen ScotlandAB24 3UE United Kingdom Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xi'an710048 China Xian Technological University Xi'an710021 China Department of Physics Federal University of Paraná Curitiba Paraná80060-000 Brazil Institute of Mathematics Centre for Medical and Biotechnical Research National Academy of Sciences of Ukraine Tereshchenkivska St. 3 Kyiv01030 Ukraine

We investigate the basin of attraction properties and its boundaries for chimera states in a circulant network of Hénon maps. It is known that coexisting basins of attraction lead to a hysteretic behaviour in the diagrams of the density of states as a function of a varying parameter. Chimera states, for which coherent and incoherent domains occur simultaneously, emerge as a consequence of the coexistence of basin of attractions for each state. Consequently, the distribution of chimera states can remain invariant by a parameter change, as well as it can suffer subtle changes when one of the basins ceases to exist. A similar phenomenon is observed when perturbations are applied in the initial conditions. By means of the uncertainty exponent, we characterise the basin boundaries between the coherent and chimera states, and between the incoherent and chimera states, respectively. This way, we show that the density of chimera states can be not only moderately sensitive but also highly sensitive to initial conditions. This chimera's dilemma is a consequence of the fractal and riddled nature of the basins boundaries. Coupled dynamical systems have been used to describe the behaviour of real complex systems, such as power grids, neuronal networks, economics, and chemical reactions. Furthermore, these systems can exhibit various kinds of interesting nonlinear dynamics, e.g. synchronisation, chaotic oscillations, and chimera states. The chimera state is a spatio-temporal pattern characterised by the coexistence of coherent and incoherent dynamics. It has been observed in a great variety of systems, ranging from theoretical and experimental arrays of oscillators, to in phenomena such as the unihemispheric sleep of cetaceans. We study the chimera state in a circulant network of Hénon maps, seeking to determine how the density of states in the network depends on the system parameters and the initial conditions. We have found that, as expected, the density of states might be inva

关键词： Neurons

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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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PSO-based design of PID controller with notch filter for flexible structures 7

PSO-based design of PID controller with notch filter for fle...

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7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016

作者： Zhang, Xuenan Hong, Juan Song, Zhuoyue State Key Laboratory of Intelligent Control and Decision of Complex Systems Ministry of Education of China China School of Automation Beijing Institute of Technology No.5 South Venue of ZhongGuan Village Beijing100081 China Beijing Institute of Aerospace Control Devices No.52 YongDing Road 100143 China

ISBN: (纸本)9784990534349

This paper addresses the tuning problem of a proportional-integral-derivative(PID) controller with notch filter for flexible space structure model based on the particle swarm optimization (PSO). For flexible structures, once vibration occurs, PID controller often fails to suppress the high amplitude and high order vibration in the system. Notch filter is used to minimize the effect of bending vibration caused by flexible structure but it may decrease of stability margin. In order to minimize the effect of bending vibration and guarantee both stability margin and time response performance at the same time, PSO algorithm is introduced to optimize both the PID controller and notch filter parameters. Simulation results demonstrate the effectiveness of the proposed design strategy. © 2016, Fuji Technology Press. All rights reserved.

关键词： Flexible structures

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Analyse Social Influence on Student Motivation based on Social Activity Network

Analyse Social Influence on Student Motivation based on Soci...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Shuai ZENG Jie ZHANG The State Key Lab of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Qingdao Academy of Intelligent Industries

Student motivation is one of the most important individual characters for explaining student performance in class. Existing researches show that it can be affected largely by social-contextual factors. Rather than focusing on social relationship, in this paper we construct social activity networks for analyzing the impact of collective behaviors on student motivation. We conduct field experiments to compare social activity networks with relationship networks by analyzing the structural characteristics. We also investigate the dynamic of social activities,and validate the effectiveness of social activity network in reflecting social influence on student motivation. Our results show that social activities are important factors affecting student motivation.

关键词： student motivation collective behaviour so cial network analysis online course

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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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Paragraph Vector Based Retrieval Model for Similar Cases Recommendation

Paragraph Vector Based Retrieval Model for Similar Cases Rec...

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World Congress on intelligent control and Automation

作者： Yifei Zhao Jing Wang Fei-Yue Wang Xiaobo Shi Yisheng Lv State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Institute of Smart Healthcare System Qingdao Academy of Intelligent Industries

ISBN: (纸本)9781467384155

Internet inquiry is playing an increasingly important role as the complement of the traditional medical service system, especially the similar cases recommendation. It can not only save the patients' waiting time, but also make use of the historical resources, for many cases with the same purpose have been solved perfectly. However, because of the diversity and non-standard of the patients' descriptions, the inquiry platform cannot find the cases with similar semantic easily. Most traditional retrieval methods require the overlap of two sentences, and this is not suitable with the diversity and non-standard descriptions. In this paper, we try to utilize the sentences' semantic representation in a continuous space to understand the cases, and then recommend the similar cases. We also incorporate it into query likelihood language models, trying to get better results. Our experimental data are all collected from a real internet inquiry platform, and the results show that our methods significantly outperform the state-of-the-art translation based methods for similar cases recommendation.

关键词： Semantics Internet Medical services Context MONOS devices Optimization Information retrieval

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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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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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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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