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IEEE International Conference on Robotics and Biomimetics

作者： Kun Zhang Zaojun Fang Jianran Liu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467396769

To hit incoming balls back to a desired position, it is a key factor for table tennis robot to get racket parameters accurately. For modeling the stroke process, a novel model is built based on multiple neural networks. The input data for neural networks are the ball velocity differences during the stroke, and racket parameters are the output data. To reduce the influences from the invalid data, a neural network based on each empirical data is established. The training data are clustered based on the empirical data. The way of choosing a neural network to compute the racket parameters depends on the comparison between the new coming data and the empirical data. Moreover, a novel way based on a binocular vision system to verify the stroke model is proposed. Experimental results have showed that the stroke model created via the proposed method is applicable and the verification method is effective.

关键词： modeling stroke process binocular

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MRF based text binarization in complex images using stroke feature

MRF based text binarization in complex images using stroke f...

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International Conference on Document Analysis and Recognition

作者： Yanna Wang Cunzhao Shi Baihua Xiao Chunheng Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China

ISBN: (纸本)9781479918065

This paper presents a novel binarization technique for text images based on Markov Random Field (MRF) framework. We regard stroke as an obvious feature of text to produce clustering result, which will be optimized by MRF model combining color, texture, context features to get the final binarization. The main innovations of our method are: (1) the integrated image is split into sub-images on which we can automatically acquire seed pixels of foreground and background using stroke feature; and (2) diverse weights are attached to seed pixels according to their location information, then highly confident cluster centers of sub-image can be acquired by gathering weighted seeds. The experimental results show that our method is robust and accurate on both video and scene images.

关键词： Image color analysis Image edge detection Robustness

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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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第七届全国平行管理会议

作者： Yuan-yuan Chen Yisheng Lv State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190China

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

关键词： urban traffic prediction traffic condition intelligent transportation systems route planning

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Adaptive Dynamic Programming for Infinite Horizon Optimal Robust Guaranteed Cost control of a Class of Uncertain Nonlinear systems

Adaptive Dynamic Programming for Infinite Horizon Optimal Ro...

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American control Conference

作者： Ding Wang Derong Liu Hongliang Li Hongwen Ma The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781479917730

In this paper, an infinite horizon optimal robust guaranteed cost control scheme of a class of continuous-time uncertain nonlinear systems is established based on adaptive dynamic programming. The main idea lies in that the optimal robust guaranteed cost control problem can be transformed into an optimal control problem. Actually, the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to help solving the modified Hamilton-Jacobi-Bellman equation corresponding to the nominal system. Then, an additional stabilizing term is introduced to reinforce the updating process of the weight vector and reduce the requirement of an initial stabilizing control. An example is provided to illustrate the effectiveness of the present control approach.

关键词： Cost function Robustness Nonlinear systems Optimal control Feedback control Biological neural networks

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Online Reinforcement Learning by Bayesian Inference

Online Reinforcement Learning by Bayesian Inference

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International Joint Conference on Neural Networks

作者： Zhongpu Xia Dongbin Zhao The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781479919611

Policy evaluation has long been one of the core issues of the online reinforcement learning, especially in the continuous state domain. In this paper, the issue is addressed by employing Gaussian processes to represent the action value function from the probability perspective. By modeling the return as a stochastic variable, the action value function can sequentially update according to observed variables such as state and reward by Bayesian inference during the policy evaluation. The update rule shows that it is a temporal difference learning method with the learning rate determined by the uncertainty of a collected sample. Incorporating the policy evaluation method with the e-greedy action selection method, we propose an online reinforcement learning algorithm referred as to Bayesian-SARSA. It is tested on some benchmark problems and the empirical results verifies its effectiveness.

关键词： Online Reinforcement Learning Bayesian Inference core issues

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Preliminary Study for Motion Scaling Based control in Minimally Invasive Vascular Interventional Robot

Preliminary Study for Motion Scaling Based Control in Minima...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Zhen-Qiu Feng Gui-Bin Bian Xiao-Liang Xie Jian-Long Hao Zhan-Jie Gao Zeng-Guang Hou the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781424492695

Robot-assisted vascular interventions present promising trend for reducing the X-ray radiation to the surgeon during the operation. However, the control methods in the current vascular interventional robots only repeat the manipulation of the surgeon. While under certain circumstances, it is necessary to scale the manipulation of the surgeon to obtain a higher precision or a shorter manipulation time. A novel control method based on motion scaling for vascular interventional robot is proposed in this paper. The main idea of the method is to change the motion speed ratios between the master and the slave side. The motion scaling based control method is implemented in the vascular interventional robot we've developed before, so the operator can deliver the interventional devices under different motion scaling factors. Experiment studies verify the effectiveness of the motion scaling based control.

关键词： Preliminary Study Motion Scaling Based control surgeon Surgeons control Methods Robot Scale formation Dental Scaling Scaling based

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Multivariate embedding based causaltiy detection with short time series

Multivariate embedding based causaltiy detection with short ...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Chuan Luo Daniel Zeng State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Department of Management Information Systems University of Arizona Tucson USA

Existing causal inference methods for social media usually rely on limited explicit causal context, preassume certain user interaction model, or neglect the nonlinear nature of social interaction, which could lead to bias estimations of causality. Besides, they often require sufficiently long time series to achieve reasonable results. Here we propose to take advantage of multivariate embedding to perform causality detection in social media. Experimental results show the efficacy of the proposed approach in causality detection and user behavior prediction in social media.

关键词： Time series analysis Manifolds Media Training Neural networks Nonlinear dynamical systems Social network services

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Inferring social influence and meme interaction with Hawkes processes

Inferring social influence and meme interaction with Hawkes ...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Chuan Luo Xiaolong Zheng Daniel Zeng State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Department of Management Information Systems University of Arizona Tucson USA

Revealing underlying social influence among users in social media is critical to understanding how users interact, on which a lot of security intelligence applications can be built. Existing methods fail to take into account the interaction relationships among memes. In this paper, we propose to simultaneously model social influence and meme interaction in information diffusion with novel multidimensional Hawkes processes. Experimental results on both synthetic and real world social media data show the efficacy of the proposed approach.

关键词： Media Social network services Data models Security Informatics Diffusion processes Benchmark testing

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Multi-Channel Ad Inventory Allocation in Real-Time Bidding Advertising Market

Multi-Channel Ad Inventory Allocation in Real-Time Bidding A...

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第七届社会计算会议

作者： Li Juanjuan Yuan Yong Qin Rui The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190China Qingdao Academy of Intelligent Industries Qingdao Shandong266109China

Real-time bidding(RTB)advertising is a promising business model emerging with the integration of big data and online *** on analysis of Web users' Cookie data,RTB has the potential of identifying target audiences and delivering best matched ads in real time,and also enabling advertisers bid for each matched ad impression via auction-based programmatic buying *** RTB markets,publishers are suppliers of ad impressions(or ad inventory),and typically they have multiple channels to sell these ad *** this paper,we analyze three commonly-used strategies for publishers to allocate the ad inventory,i.e.,the offline guaranteed contract,the online RTB channel,and the hybrid *** explicitly take the quality of ad impressions into consideration,since it is a key parameter for publishers' decision when choosing a proper channel to sell these ad *** establish models to optimize the reserve price for maximizing the revenue of those publishers choosing the online RTB channel and the hybrid *** also compared these three strategies in pursuit of finding the proper strategy for publishers when selling ad impressions with different qualities.

关键词： Real-time bidding ad inventory allocation reserve price ad impression quality

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Containment control of General Linear Multi-agent systems with Multiple Dynamic Leaders:a Fast Sliding Mode Based Approach

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IEEE/CAA Journal of Automatica Sinica 2014年第2期1卷 134-140页

作者： Huiyang Liu Long Cheng Min Tan Zengguang Hou State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

In this paper, distributed containment control problems of general linear multi-agent systems are investigated. The objective is to make the followers in a multi-agent network converge to the convex hull spanned by some leaders whose control inputs are nonzero and not available to any *** mode surfaces are defined for the cases of reduced order and non-reduced order, respectively. For each case, fast sliding mode controllers are designed. It is shown that all the error trajectories exponentially reach the sliding mode surfaces in a finite time if for each follower, there exists at least one of the leaders who has a directed path to the follower, and the leaderscontrol inputs are bounded. The control Lyapunov function for exponential finite time stability, motivated by the fast terminal sliding mode control, is used to prove reachability of the sliding mode surfaces. Simulation examples are given to illustrate the theoretical results.

关键词： Multi-agent systems Sliding mode control Trajectory Robot sensing systems Robot kinematics Manifolds

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