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Human Action Recognition by Bagging Data Dependent Representation

Human Action Recognition by Bagging Data Dependent Represent...

引用

21st International Conference on Pattern Recognition (ICPR)

作者： Zhou, Wen Wang, Chunheng Xiao, Baihua Zhang, Zhong Ma, Long Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China

ISBN: (纸本)9784990644109;9781467322164

Traditional methods based on bag-of-word representation are easily affected by noise, and they also cannot handle the problem when a test distribution differs from the training distribution. In this paper, we propose a novel method for human action recognition by bagging data dependent representation. Different with traditional methods, the proposed method represents each video by several histograms. These histograms are obtained by bagging according to an estimated prior several times in both training and testing. The data dependent property of our method depends on the prior which reflects the training distribution. There are two advantages of the proposed method. First, it alleviates the distribution difference between training set and test set. Second, the bagging operation reduces noise and improves the performance significantly. Experimental results show the effectiveness of the proposed method.

关键词： Pattern recognition

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H_∞ control of Unknown Discrete-Time Nonlinear systems with control Constraints Using Adaptive Dynamic Programming

<i>H</i><sub>∞</sub> Control of Unknown Discrete-Time Nonli...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Liu, Derong Li, Hongliang Wang, Ding Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9781467314909

In this paper, we solve the H-infinity robust optimal control problem for discrete-time nonlinear systems with control saturation constraints using the iterative adaptive dynamic programming algorithm. First, a heuristic dynamic programming algorithm is derived to solve the Hamilton-Jacobi-Isaacs equation associated with the H-infinity control problem, and a convergence analysis is provided. Then, a dual heuristic dynamic programming algorithm with nonquadratic performance functional is developed to overcome the control saturation constraints. Finally, to facilitate the implementation of the algorithm, four neural networks are used to approximate the unknown nonlinear system, the control policy, the disturbance policy, and the value function.

关键词： Iterative methods

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Adaptive Dynamic Programming with Stable Value Iteration Algorithm for Discrete-Time Nonlinear systems

Adaptive Dynamic Programming with Stable Value Iteration Alg...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467314909

In this paper, a new stable value iteration adaptive dynamic programming (ADP) algorithm, named "theta-ADP" algorithm, is proposed for solving the optimal control problems of infinite horizon discrete-time nonlinear systems. By introducing a parameter theta in the iterative ADP algorithm, it is proved that any of iterative control obtained in the proposed algorithm can stabilize the nonlinear system which overcomes the disadvantage of traditional value iteration algorithms. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative. theta-ADP algorithm. Finally, a simulation example is given to illustrate the performance of the proposed method.

关键词： Nonlinear systems

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Sparse Representation based on Matrix Rank Minimization and K-Means Clustering for Recognition

Sparse Representation based on Matrix Rank Minimization and ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ma, Long Wang, Chunheng Xiao, Baihua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467314909

In this paper, we propose a sparse coding algorithm based on matrix rank minimization and k-means clustering and for recognition. We consider the problem of removing the noise in the training samples and generating more samples at the same time. To accomplish this, we extended the matrix rank minimization problem to cope with complex data. Samples from the same class are segmented into several groups by k-means clustering algorithm, and matrix rank minimization is applied on the clustered data to separate the noises and recover the low-rank structures in the grouped data. An over-complete dictionary is constructed by connecting the low-rank structures and the training samples together to keep the samples diversity. Sparse representation is operated based on this over-complete dictionary for recognition. Furthermore, a parameter is introduced to adjust the weighting of the coefficients that code the noises. We apply the proposed algorithm for character and face recognition. Experiments with improved performances validate the effectiveness of the proposed algorithm.

关键词： Face recognition

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CPG Parameter Search for a Biomimetic Robotic Fish Based on Particle Swarm Optimization

CPG Parameter Search for a Biomimetic Robotic Fish Based on ...

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IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Wu, Zhengxing Yu, Junzhi Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467321273;9781467321259

This paper addresses the parameter search issue of a Central Pattern Generator (CPG) governed fishlike swimming. Since the CPG parameters involving amplitudes, frequencies, and phase lags are closely related to the propulsive performance, an idea optimizing the CPG characteristic parameters for the maximum propulsive speed is formed and implemented. Specifically, a dynamic model of robotic fish swimming using Kane's method is developed to guide the primary parameter search. A particle swarm optimization (PSO) algorithm is further employed to optimize the CPG parameters for an enhanced performance. Numerical simulations and robotic experiments superior to previously published results are finally given, validating the effectiveness of the PSO-based search scheme.

关键词： biomimetics hydrodynamics marine propulsion mobile robots particle swarm optimisation robot dynamics search problems

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Sparse Representation for Face Recognition based on Discriminative Low-Rank Dictionary Learning

Sparse Representation for Face Recognition based on Discrimi...

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IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ma, Long Wang, Chunheng Xiao, Baihua Zhou, Wen Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467312288

In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest coefficients to represent the test signal as linear combination of the bases in an over-complete dictionary. Motivated by low-rank matrix recovery and completion, assume that the data from the same pattern are linearly correlated, if we stack these data points as column vectors of a dictionary, then the dictionary should be approximately low-rank. An objective function with sparse coefficients, class discrimination and rank minimization is proposed and optimized during dictionary learning. We have applied the algorithm for face recognition. Numerous experiments with improved performances over previous dictionary learning methods validate the effectiveness of the proposed algorithm.

关键词： Face recognition

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Finite-Horizon Neural Optimal Tracking control for a Class of Nonlinear systems with Unknown Dynamics

Finite-Horizon Neural Optimal Tracking Control for a Class o...

引用

10th World Congress on Intelligent control and Automation (WCICA)

作者： Wang, Ding Liu, Derong Li, Hongliang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467313988

A neural-network-based finite-horizon optimal tracking control scheme for a class of unknown nonlinear discrete-time systems is developed. First, the tracking control problem is converted into designing a regulator for the tracking error dynamics under the framework of finite-horizon optimal control theory. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic programming algorithm is introduced to obtain the finite-horizon optimal controller to make the cost function close to its optimal value within an g-error bound. Furthermore, in order to implement the algorithm via dual heuristic dynamic programming technique, three neural networks are employed to approximate the error dynamics, the cost function, and the control law, respectively. In addition, a numerical example is given to demonstrate the validity of the present approach.

关键词： Adaptive dynamic programming approximate dynamic programming finite-horizon optimal tracking control intelligent control neural networks

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Parallel Public Transport system and Its Application in the Evacuation of Large-scale Activities

Parallel Public Transport System and Its Application in the ...

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15th International IEEE Conference on Intelligent Transportation systems (ITSC)

作者： Zhu, Fenghua Chen, Songhang Lv, Yisheng Ye, Peijun Xiong, Gang Dong, Xisong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

ISBN: (纸本)9781467330633

ACP (Artificial societies, Computational experiments, and Parallel execution) approach is adopted by our method to build parallel public transport system (PPTS). The framework of PPTS is proposed and some components are analyzed. Two key steps in building up PPTS are discussed. The first is growing up artificial public transport system from bottom up using agent-based technology. The second is implementing schedule plans of public vehicles using cloud computing. One specific PPTS is established for Guangzhou 2010 Asian Games. Its effectiveness is verified and illustrated by comparing the traffic parameters between before and after the employment.

关键词： ACP approach parallel public transport system (PPTS) computational experiment agent

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Neural-Network-Based Optimal control for Discrete-Time Nonlinear systems Using General Value Iteration

Neural-Network-Based Optimal Control for Discrete-Time Nonli...

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31st Chinese control Conference

作者： Li Hongliang Liu Derong Wang Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9789881563811

In this paper, we propose a novel adaptive dynamic programming (ADP) scheme based on general value iteration to obtain near optimal control for discrete-time nonlinear systems with continuous state and control space. First, the selection of initial value function is different from the traditional value iteration, and a new method is introduced to demonstrate the convergence property and convergence speed of the value function. Then, the control law obtained at each iteration can stabilize the system under some conditions. At last, three neural networks with Levenberg-Marquardt training algorithm are used to approximate the unknown nonlinear system, the value function and the optimal control law. One simulation example is presented to demonstrate the effectiveness of the present scheme.

关键词： Adaptive dynamic programming approximate dynamic programming optimal control value iteration neural networks reinforcement learning

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A Clustering based Hierarchical Image Feature Location Method for complex Shaped Work-piece

A Clustering based Hierarchical Image Feature Location Metho...

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31st Chinese control Conference

作者： Qi Yuhan Jing Fengshui Tan Min Diao Wenchao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9789881563811

This paper addresses an image feature location method for complex shaped work-piece. The "rough estimation first and then precise location" hierarchical feature location method is proposed to roughly estimate the work-piece's pose first and then precisely locate it in 2D image space. The proposed method has the following main steps: (1) simple features are detected first then more complex features are examined later, using the locations of the previously found features;(2) an improved local adaptive threshold algorithm is used to binarize the work-piece's gray image;(3) a size adjustable binary template is designed to match hole features;(4) a work-piece is finally located in image space according to its CAD model using the nearest neighbor clustering algorithm. The method has been tested on a sort of complex shaped automobile engine cylinder head as an example and the location results are satisfactory.

关键词： hierarchical feature location adaptive threshold adjustable binary template nearest neighbor clustering

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