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

作者： Liang Peng Zeng-Guang Hou Weiqun Wang 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

In this paper, a dynamic EMG-torque model of the elbow joint is developed based on ANN, and two novel test methods are proposed to validate its generalization performance. A time-delay neural network (TDNN) model is built and proved to have less risk of overfitting than the most-used multilayer feedfoward neural network (MFNN) model for dynamic EMG-torque modeling. Both EMG and kinematic features are included in the input of ANN, but the zero-EMG test shows that the trained ANN is part of the inverse joint dynamics rather than the EMG-torque model, and some random samples for ANN training are added to overcome this problem. The single-muscle test shows that an inappropriate choice of the motion type may cause the model to estimate wrong torque directions. After tuning and testing, the root mean square error (RMSE) across all subjects is 0.60±0.20 N.m.

关键词： EMG-torque Model Neural Networks RMSE Neural network ROOT-MEAN-SQUARE ERRORS Artificial neural networks Elbow Elbow Joint random sample Test Method

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Link Flow Estimation for Parallel Transportation management

Link Flow Estimation for Parallel Transportation Management

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第七届全国平行管理会议

作者： Peijun Ye The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Link flow is critical to investigate the traffic state in parallel transportation management and thus has been object of growing interest in the past few ***,tradition estimation methods mostly use partial link counts only and convert this problem into observability *** paper proposed a new mathematical model based on both partial link counts and the Automatic Vehicle Identification *** approach is tested using the actual traffic data from the city of Chengdu,*** results indicate it is feasible to combine these two data sources to estimate the total link flows.

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Modeling the stroke process in table tennis robot using neural network

Modeling the stroke process in table tennis robot using neur...

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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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A Method of SMS Spam Filtering Based on AdaBoost Algorithm

A Method of SMS Spam Filtering Based on AdaBoost Algorithm

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World Congress on Intelligent control and automation

作者： Xipeng Zhang Gang Xiong Yuexiang Hu Fenghua Zhu Xisong Dong Timo R. Nyberg State Key Laboratory of Management and Control for Complex Systems Institute of Automation of Chinese Academy of Science Beijing CO 100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan CO 523000 China Hainan Zhongke Flower Ocean Cloud Commerce Technology Co. Ltd Haikou CO570311 Hainan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing CO 100190 China Qingdao Academy of Intelligent Industries Qingdao CO 266061 China Department of Industrial Engineering and Management Aalto University Aalto CO FI-00076 Finland

ISBN: (纸本)9781467384155

Short message is one of the most common communication media for mobile subscribers, so major mobile operators are devoted to improve their Short Message Service (SMS). However, the annoying and undesired messages, also named message spam or simply spam, not only worsen the users' experience, but also cause their complaints on SMS. In this paper, we present a novel Chinese SMS spam filtering framework based on AdaBoost algorithm to provide accurate and effective short messages classification. Three content-based weak filters are introduced to boost the performance of final classification decision. Results from Receiver Operating Characteristics (ROC) analysis prove the proposed method has such advantages as higher efficiency and fewer parameters over those established SMS spam filtering methods. The application of the proposed method is expected to block the most spam for mobile subscribers and improve the service quality of SMS. With simple data processing and few training parameters, the proposed method can be applied into the practice of short text classification.

关键词： Filtering Mobile communication Training Unsolicited electronic mail Classification algorithms Text categorization Bayes methods

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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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