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检索条件"机构=State Key Laboratory for Management and Control of Complex Systems"

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Human Action Recognition with Attribute Regularization

Human Action Recognition with Attribute Regularization

引用

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Zhong Zhang Chunheng Wang Baihua Xiao Wen Zhou Shuang Liu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. Yet such a method neglects the constraints that attributes impose on classes which may fail to constrain the semantic relationship between the attribute and object classifiers. In this paper, we explicitly consider such attribute-object relationship, and correspondingly, we modify the multi-task learning model by adding attribute regularization. In this way, the learned model not only shares the low-level features, but also gets regularized according to the semantic constrains. Our method is verified on two challenging datasets (KTH and Olympic Sports), and the experimental results demonstrate that our method achieves better results than previous methods in human action recognition.

关键词： Humans Semantics Optimization Histograms Prediction algorithms Accuracy Training

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Illumination-invariant completed LTP descriptor for cloud classification

Illumination-invariant completed LTP descriptor for cloud cl...

引用

Congress on Image and Signal Processing, CISP

作者： Shuang Liu Chunheng Wang Baihua Xiao Zhong Zhang Yunxue Shao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Cloud classification plays an essential role in a large number of applications. However, this issue is particularly challenging due to the extreme appearance variations under different atmospheric conditions. In this paper, a novel descriptor named illumination-invariant completed local ternary pattern (ICLTP) is proposed for cloud classification. The proposed descriptor effectively handles the illumination variations by introducing illumination invariant factor. Furthermore, the Quadratic-Chi metric, which is more suitable for comparing the difference between two histograms, is applied instead of Chi-Square metric. The experimental results demonstrate the superior performance of our strategy on two challenging cloud databases. Besides cloud classification, we further validate the proposed ICLTP operator on traditional texture classification, which show the good generalization ability.

关键词： Lighting Databases Measurement Clouds Histograms Pattern recognition Robustness

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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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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Long Ma Chunheng Wang Baihua Xiao Wen Zhou State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

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： Dictionaries Strontium Noise Training Sparse matrices Face Encoding

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Adaptive optimal control for a class of nonlinear partially uncertain dynamic systems via policy iteration

Adaptive optimal control for a class of nonlinear partially ...

引用

International Conference on Intelligent control and Information Processing (ICICIP)

作者： Derong Liu Xiong Yang Hongliang Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In this paper, by employing an online algorithm based on policy iteration (PI), an adaptive optimal control problem for continuous-time (CT) nonlinear partially uncertain dynamic systems is investigated. In this proposed algorithm, a discounted cost function is discussed, which is considered to be a more general case for optimal control problems. Two neural networks (NNs) are used to implement the algorithm, which aims at approximating the cost function and the control law, respectively. The uniform convergence to the optimal control is proven, and the stability of the system is guaranteed. An illustrating example is given.

关键词： Optimal control Nonlinear systems Cost function Convergence Heuristic algorithms Equations Dynamic programming

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Parallel Bus Rapid Transit (BRT) operation management system based on ACP approach

Parallel Bus Rapid Transit (BRT) operation management system...

引用

IEEE International Conference on Networking, Sensing and control

作者： Gang Xiong Xisong Dong Dong Fan Fenghua Zhu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Bus Rapid Transit (BRT) is an effective way to increase urban traffic capacity. But its operation and scheduling optimization are difficult. In this article, Parallel BRT Operation management System (PBOMS) is constructed based on ACP approach. It can detect the passenger's quantity on station platforms in real-time, traffic flow besides stations or at intersections, and the queuing length of vehicles on the road lines. It can provide short-term passenger and traffic saturation prediction in order to arrange transportation management more accurately to relieve the congestion. It can assess, improve and optimize the emergency management during holidays, public events, accidents and other emergency situations. It can improve the quality of real-time scheduling functions by using the measurement results detected from traffic videos, and so on. This system has been piloted in Guangzhou Zhongshan Avenue BRT, which was applied for BRT's monitoring, warning, forecasting, emergency management, real-time scheduling and other purposes, to improve Guangzhou BRT's smoothness, safety, efficiency and reliability.

关键词： Vehicles Data models Real time systems Monitoring Scheduling Roads

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Parallel public transport management and control system for Guangzhou Asian Games

Parallel public transport management and control system for ...

引用

IEEE International Conference on Networking, Sensing and control

作者： Xisong Dong Gang Xiong Dong Fan Fenghua Zhu Yisheng Lv State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Public traffic is required for the convenient travel, reducing traffic congestion and accidents, low-carbon, environmental protection, sustainable development, and traffic demand of giant sports and large business activities, etc. For public transport demand of the 16th Asian Games and the 2010 Asian Para Games held in Guangzhou in 2010, based on ACP approach, Parallel Public Transport management And control System (PPTMS) for Guangzhou Asian Games had been developed to support management decision of public transport. This system can help public transport managers to improve and enhance significantly the level of public transport management in Guangzhou, from experience-based formulation and manual implementation to scientific computation-based formulation and automatic implementation with intelligent systems, to guarantee the traffic demand effectively during the two Games.

关键词： Games Indexes Vehicles Streaming media Security Accidents

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Soft-signed sparse coding for ground-based cloud classification

Soft-signed sparse coding for ground-based cloud classificat...

引用

International Conference on Pattern Recognition

ISBN: (纸本)9781467322164

Traditional sparse coding has been successfully applied in texture and image classification in the past years. Yet such kind of method neglects the influence of the signs of coding coefficients, which may cause information loss in the sequential max pooling. In this paper, we propose a novel coding strategy for ground-based cloud classification, which is named soft-signed sparse coding. In our method, a constraint on the signs is explicitly added to the objective function of traditional sparse coding model, which can effectively regulate the ratio between the number of positive and negative non-zero coefficients. As a result, the proposed method can not only obtain low reconstruction error but also consider the influence of the signs of coding coefficients. The strategy is verified on two challenging cloud datasets, and the experimental results demonstrate the superior performance of our method compared with previous ones.

关键词： Encoding Image coding Clouds Dictionaries Vectors Image reconstruction Linear programming

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Heuristic dynamic programming-based H∞ control of discrete-time affine nonlinear systems

Heuristic dynamic programming-based H∞ control of discrete-...

引用

International Conference on Intelligent control and Information Processing (ICICIP)

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

In this paper, we solve the H ∞ control problems for discrete-time affine nonlinear systems with known dynamics. An iterative heuristic dynamic programming algorithm is derived and the convergence analysis is provided. Three neural networks are used to approximate the control policy, the disturbance policy, and the value function, respectively. A simulation example is presented to demonstrate the effectiveness of the proposed scheme.

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Contextual Fisher kernels for human action recognition

Contextual Fisher kernels for human action recognition

引用

International Conference on Pattern Recognition

In the literature of human action recognition, despite promising results have been obtained by the traditional bag-of-words model, the relationship among spatiotemporal points has rarely been considered. Furthermore, serious quantization error also exists in this kind of strategy. In this paper, we propose a novel coding strategy named contextual Fisher kernels to overcome these limitations. We add a Gaussian function to represent contextual information into the generative model. In this way, our method explicitly considers the spatio-temporal contextual relationships between interest points and alleviates quantization error. Our method is verified on two challenging datasets (KTH and UCF sports), and the experimental results demonstrate that our method achieves better results than the state-of-the-art methods in human action recognition.

关键词： Kernel Humans Context modeling Quantization Vectors Accuracy Covariance matrix

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Tuning of PID controller based on fruit fly optimization algorithm

Tuning of PID controller based on fruit fly optimization alg...

引用

2012 9th IEEE International Conference on Mechatronics and Automation, ICMA 2012

作者： Han, Jiuqi Wang, Peng Yang, Xin Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing 100190 China State Key Lab. of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467312776

The Proportional - Integral - Derivative (PID) controllers are one of the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. PID tuning is the key issue in the design of PID controllers and most of the tuning processes are implemented manually resulting in difficulty and time consuming. To enhance the capabilities of traditional PID parameters tuning techniques, modern heuristics approaches, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are employed recent years. In this paper, a novel tuning method based on Fruit Fly Optimization Algorithm (FOA) is proposed to optimize PID controller parameters. Each fruit fly's position represents a candidate solution for PID parameters. When the fruit fly swarm flies towards one location, it is treated as the evolution of each iterative swarm. After hundreds of iteration, the tuning results - the best PID controller parameters can be obtained. The main advantages of the proposed method include ease of implementation, stable convergence characteristic, large searching range, ease of transformation of such concept into program code and ease of understanding. Simulation results demonstrate that the FOA-Based optimized PID (FOA - PID) controller is with the capability of providing satisfactory closed - loop performance. © 2012 IEEE.

关键词： Three term control systems

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