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Robust mean-shift tracker with local saliency feature and spatial pattern preserved metric

Robust mean-shift tracker with local saliency feature and sp...

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International Conference on Mechatronic Science, Electric Engineering and Computer

作者： Linshan Liu Suiwu Zheng Hong Qiao Min Wang State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479925667

Robust object tracking in crowded and cluttered dynamic scenes is a very difficult task in robotic vision due to complex and changeable environment and similar features between the background and foreground. In this paper, we present an improved mean-shift tracker which uses discriminative local saliency feature and a new spatial pattern preserved similarity metric method to overcome above difficulties in mean-shift based tracking approaches. The local saliency feature, which is composed of contrast color, texture and gradient around the target, is proposed to find the most distinguished features between the target and background, and it could enhance the tracking performance greatly in the cluttered and complex environment. Another important benefit of this feature is that the saliency map form could be easily embedded into the mean-shift framework. The new similarity metric try to preserve the spatial pattern to reduce the similarity errors caused by different spatial structure. It is beneficial to the mean-shift tracker to face the targets and scenes which has identical texture and color feature and with different spatial patterns. Finally, the efficiency of the proposed improved mean-shift tracker is validated through the plenty experimental results and analysis.

关键词： Target tracking Histograms Image color analysis Robustness Kernel Color

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Action Knowledge Extraction from Web Text

Action Knowledge Extraction from Web Text

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IEEE International Conference on Intelligence and Security Informatics

作者： Ansheng Ge Wenji Mao Daniel Zeng Lei Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467362146

Action knowledge is an important type of behavioral knowledge and of vital importance to many applications in social computing, especially in behavior modeling, analysis and prediction. In this paper, we present a computational method to action knowledge extraction from online media. Our approach is based on mutual bootstrapping and combined with knowledge reasoning. Compared with the related work, our approach can acquire more types of action knowledge, and needs much less human labor. We evaluate the performance of our method using the Web textual data from security informatics domain. The experimental results show the effectiveness of our proposed method.

关键词： Action knowledge Knowledge extraction Bootstrapping Reasoning

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EMG-based Estimation of Knee Joint Angle under Functional Electrical Stimulation Using an Artificial Neural Network

EMG-based Estimation of Knee Joint Angle under Functional El...

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第三十二届中国控制会议

作者： Yixiong Chen Jin Hu Feng Zhang Pengfeng Li Zengguang Hou State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781479900305

In this paper,an artificial neural network is proposed to estimate knee joint angle in hybrid activation of knee extension motion,including voluntary muscle contraction and functional electrical stimulation(FES) induced *** electromyography(EMG) signals of three muscles responsible for knee extension and FES parameter which describe the FES intensity are used as input vector of the neural network,while the estimated knee angle is the *** the experiment, FES with different combinations of parameters(pulse amplitude and pulse width) was delivered to the rectus femoris muscle of a healthy male subject when the knee was in a periodic extension motion by voluntary muscle *** EMG signals of three muscles,parameters of FES as well as the actual knee angle were ***,there were 52,233 and 17,420 sampling points corresponding to 261 and 87 seconds used to train and validate the neural *** result shows the trained network has a satisfactory performance on knee joint angle estimation whose output well follows the curve of actual knee angle. Root mean square error between estimated angle and actual angle is employed to represent the estimation accuracy which is 5.07 degree according to the experimental data.

关键词： Functional electrical stimulation Electromyography Neural network

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3D modeling of coronary arteries based on tubular-enhanced CURVES segmented regions for robotic surgical simulation

3D modeling of coronary arteries based on tubular-enhanced C...

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IEEE International Conference on Robotics and Biomimetics

作者： Fan Yang Zeng-Guang Hou Shao-Hua Mi Gui-Bin Bian Xiao-Liang Xie State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479927456

The visualization of the coronary vasculature is of utmost importance in interventional cardiology. Intravascular surgical robots assist the practitioners to perform the complex procedure while protecting them from the tremendous occupational hazards. Robotic surgical simulation aims to provide support for the learners in both efficiency and convenience. The blood vessels especially the coronary arteries with rich details are the key part of the anatomic scenario of the virtual training system. The variations in diameters and directions make the segmentation of the coronary arteries a difficult work. In this paper, a robust and semi-automatic approach for the segmentation of the coronary arteries is developed. The approach is based on the multi-scale tubular enhancement and an improved geodesic active contours model. The demonstrated approach firstly enhances the tubular objects by computing their “vesselness”. Next the edge potential maps are calculated based on the enhanced information. Meanwhile, the initial contours are generated by a modified fast marching method. Then the actual wave fronts evolution extracts the details of the coronary arteries. Finally the visualization model is organized based on the segmentation results by the marching cubes method. This approach has been proved successful for the visualization of the coronary arteries based on the CTA information.

关键词： Arteries Robots Computational modeling Level set Eigenvalues and eigenfunctions Visualization Training

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Predicting User Participation in Social Networking Sites

Predicting User Participation in Social Networking Sites

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IEEE International Conference on Intelligence and Security Informatics

作者： Qingchao Kong Wenji Mao Daniel Zeng State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467362146

Social networking sites provide a convenient way for users to participate in discussion groups and communicate with others. While users situate in and enjoy such a social environment, it is important for various security related applications to understand, model and analyze participating users' behavior. In this paper, we make an attempt to model and predict user participation behavior in discussion groups of social networking sites. Our work employs a feature-based approach, which considers four types of features: thread features, content similarity, user behavior and social features. We conduct an empirical study on a popular social networking site in China, ***. The experimental results show the effectiveness of our approach.

关键词： User participation Behavior modeling and prediction Social networking sites

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Task-oriented active training based on adaptive impedance control with iLeg — A horizontal exoskeleton for lower limb rehabilitation

Task-oriented active training based on adaptive impedance co...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Jin Hu Zeng-Guang Hou Yixiong Chen Liang Peng Long Peng State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479927456

A horizontal exoskeleton for lower limb rehabilitation called iLeg has been developed by our laboratory which consists of two 3-DOF (degrees of freedom) robotic leg orthoses. This paper proposes a position-based impedance control with the compensation of BP NNs (back propagation neural networks) for the exoskeleton. Based on the control scheme, the task-oriented active training is investigated where impedance parameters are self-adjusted to movement deviation and patient activities with fuzzy logic. An adaptive haptic interface of active compliance is ensured to provide positive feedback to patients when their effort is desired or negative otherwise, which encourages patients to practice the desired movement following the predefined directed path. Besides, the timing freedom is separated from spatial trajectory and determined by patients. Voluntary effort hence becomes a requirement during the exercises, no effort no movement, so that active contribution of patients is highly motivated. Simulation results have verified the feasibility of the control scheme and the training strategy. An active compliant environment is created with adaptive haptic interface for task-oriented patient-driven training of multi-joint coordination.

关键词： Training Impedance Vectors Joints Robot kinematics Artificial neural networks

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Multi-class classification via discriminative multiple subspace learning

Multi-class classification via discriminative multiple subsp...

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International Conference on Mechatronic Science, Electric Engineering and Computer

作者： Tang Tang Hong Qiao Suiwu Zheng State Key Laboratory of Management and Control for Complex Systems Organization Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479925667

Subspace learning has long been a fundamental yet important problem of modeling data distributions. In this paper, we propose to learn multiple linear subspaces in a supervised way for multi-class classification. To this end, a discriminative term redefining decision margin in terms of reconstruction error is incorporated into the model. The term enjoys similar properties of hinge loss function to the benefit of classification and leads to a training process seeking the balance between unsupervised learning and supervised learning. In the experiments on written digits dataset, our algorithm outperforms other methods proposed recently in both accuracy and computation efficiency.

关键词： Dictionaries Classification algorithms Optimization Computational modeling Training Accuracy Principal component analysis

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Optimized neural network ensemble by combination of particle swarm optimization and differential evolution

Optimized neural network ensemble by combination of particle...

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10th International Symposium on Neural Networks, ISNN 2013

作者： Zhao, Zeng-Shun Feng, Xiang Wei, Fang Wang, Shi-Ku Cao, Mao-Yong Hou, Zeng-Guang College of Information and Electrical Engineering Shandong University of Science and Technology Qingdao 266590 China School of Control Science and Engineering Shandong University Jinan 250061 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642390647

The Neural-Network Ensemble (NNE) is a very effective method where the outputs of separately trained neural networks are combined to perform the prediction. In this paper, we introduce the improved Neural Network Ensemble (INNE) in which each component forward neural network (FNN) is optimized by particle swarm optimization (PSO) and back-propagation (BP) algorithm. At the same time, the ensemble weights are trained by Particle Swarm Optimization and Differential Evolution cooperative algorithm(PSO-DE). We take two obviously different populations to construct our algorithm, in which one population is trained by PSO and the other is trained by DE. In addition, we incorporate the fitness value from last iteration into the velocity updating to enhance the global searching ability. Our experiments demonstrate that the improved NNE is superior to existing popular NNE. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Iterative methods

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Label micro-blog topics using the Bayesian inference method

Label micro-blog topics using the Bayesian inference method

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Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2013

作者： Gao, Heng Li, Qiudan Zheng, Xiaolong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Songshan Lake Dongguan 523808 China

ISBN: (纸本)9783642396922

Classifying Micro-blog content is a popular research topic in social media, which can help users access their favorite information quickly. Much research focuses on classifying Micro-blog content with short text dataset. The challenge is the classification effect may be hampered by content ambiguity. To address this challenge, we propose a novel classification framework using external knowledge base and the Bayesian inference method. We first introduce Baidu Encyclopedia to extract effective features, and then we train efficient classifiers with the probabilistic graphic model. The proposed model can classify micro-blog content reliably. Experiments on Sina-weibo dataset demonstrate the effectiveness of the proposed method. © 2013 Springer-Verlag.

关键词： Classification (of information)

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A Prior-Free Encode-Decode Change Detection Test to Inspect Datastreams for Concept Drift

A Prior-Free Encode-Decode Change Detection Test to Inspect ...

引用

International Joint Conference on Neural Networks

作者： Cesare Alippi Li Bu Dongbin Zhao Politecnico di Milano Italy and The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467361279

Online change detection in datastreams has attracted many researchers and is becoming a very hot topic whose relevance will further increase with research on Big Data. Concept drift is induced by changes in stationarity of the process generating the data caused by faults, time variance of the environment and inaccuracy of the change detection mechanism. Here, we propose a recurrent auto-associative Encode-Decode machine trained to reconstruct input data. The generated residual is then inspected for structural changes with a Change Detection Test (CDT). Although any CDT can be used, in the paper we focus the attention on the Hierarchical Intersection of Confidence Intervals change detection test for its capability of controlling false positives with a two layered test and an online version of the Lepage Change Point Model. Once concept drift is detected, the designed Encode-Decode machine, globally acting as an Encode-Decode CDT, is retrained on new data to detect subsequent changes.

关键词： Concept Drift CDT detect subsequent changes

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