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Adaptive control of air delivery system for PEM fuel cell using backstepping

Adaptive control of air delivery system for PEM fuel cell us...

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作者： Li, Peng Chen, Jie Cai, Tao Zhang, Bo School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Ministry of Education Beijing 100081 China

ISBN: (纸本)9788995605646

The proton exchange membrane (PEM) fuel cells have attracted significant attention due to high efficiency and low pollution. The performance and lifetime of the fuel cell power system depend on the heat and water management, gas flow control and pressure regulation. This paper focuses on the oxygen excess ratio regulation for the fuel cell system to improve the performance and extend the lifetime. Firstly, a third-order model of the air delivery subsystem is introduced based on suitable assumption and simplification. Secondly, the third-order model is transformed to the non-affine nonlinear form, and the oxygen excess ratio regulation is reconsidered as load current tracking problem. Using the backstepping technique and neural network, an adaptive controller is proposed for the tracking problem and guarantees the system's stability in presence of modeling errors, external disturbances and uncertainties. Finally, the simulation illustrates the effectiveness of the adaptive controller. © 2011 Asian control Association.

关键词： Backstepping

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Multi-group consensus via pinning control with non-linear heterogeneous agents

Multi-group consensus via pinning control with non-linear he...

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作者： Sun, Wei Bai, Yongqiang Jia, Rui Xiong, Rui Chen, Jie School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Ministry of Education Beijing 100081 China

ISBN: (纸本)9788995605646

The agents connected by networks are capable to reach a prescribed state if only a small fraction of them are controlled with feedback information. The present work tries to stabilize groups of non-linear agents in a directed network onto a stable state with several equilibriums. The agent dynamics can be identical or heterogeneous. But the agents in the same group share a same non-linear model. The conditions to reach multiple consensus states are provided under both non-switching and switching topologies. The maximum number of heterogeneous equilibriums is discussed. It is proved theoretically and by simulations that under certain conditions, pinning control can lead a multi-group network to reach a multi-valued consensus state. © 2011 Asian control Association.

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Recursive blind LMS parameter identification for single-input multiple-output system

Recursive blind LMS parameter identification for single-inpu...

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作者： Chen, Jie Ma, Tao Chen, Wenjie Zhang, Bo School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Ministry of Education Beijing 100081 China

ISBN: (纸本)9788995605646

A blind least-mean-squares (BLMS) algorithm is proposed for the parameter identification of single-input multiple-output (SIMO) systems. Without requiring knowledge of a reference signal, it is proved that the presented parameter estimates almost sure converge to their real value under the assumption that the observed noises are mutually independent distributed additive white sequence with known variance. The noise variance of observed signal is estimated from the eigenvalues of a matrix related to observed signal sequence firstly. We back our theoretical findings with experiments showcasing the potential merits of the BLMS in practice. © 2011 Asian control Association.

关键词： Eigenvalues and eigenfunctions

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Vulnerability analysis of power grid based on multi-agent complex systems

Vulnerability analysis of power grid based on multi-agent co...

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2011 IEEE International Conference on Service Operations, Logistics and Informatics, SOLI 2011

作者： Dong, Xisong Xiong, Gang Hou, Jiachen Dong, Fan Nyberg, Timo R. State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China BIT Research Centre Aalto University Aalto Finland

ISBN: (纸本)9781457705731

Modern power grid is a typical multi-level complex giant system. The conventional analytical methods based on reductionism can't provide sufficient guidance for its operation and management. complex system theory, based on holism, has its specific advantages in power grid's research. But, it has some limitations. In this article, we improve complex grid by introducing new parameters which can describe the grid's characters better and using multi-agent theory. As an application, the complex power grid constructed with actual data from North China grid is constructed and its vulnerability has been simulated and analyzed under different attacks. © 2011 IEEE.

关键词： complex networks

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Mobile sensor network node deployment via Central Voronoi Tessellation

Mobile sensor network node deployment via Central Voronoi Te...

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International Conference on intelligent control and Information Processing (ICICIP)

作者： R. Xiong Y. Q. Bai W. Sun X. Liu School of Automation and Key Laboratory of Complex Systems Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing China

The present work focuses on the node deployment algorithm of Wireless Sensor Networks. The Central Voronoi Tessellation algorithm is employed to optimize the node position. The energy consumption of the whole sensor network will be minimized by using this algorithm. Simulation of the proposed algorithm shows the effectiveness of minimizing the energy consumption.

关键词： Robot sensing systems Wireless sensor networks Mobile communication Mobile robots Mobile computing Classification algorithms

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Quadratic-Chi Similarity Metric Learning for Histogram Feature

Quadratic-Chi Similarity Metric Learning for Histogram Featu...

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第一届亚洲模式识别会议

作者： Rongguo Zhang Xinyuan Cai Chunheng Wang Baihua Xiao State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio

ISBN: (纸本)9781457701221

Histogram features, such as SIFT, HOG, LBP et al, are widely used in modern computer vision algorithms. According to [18], chi-square distance is an effective measure for comparing histogram features. In this paper, we propose a new method, named the Quadric-chi similarity metric learning (QCSML) for histogram features. The main contribution of this paper is that we propose a new metric learning method based on chi-square distance, in contrast with traditional Mahalanobis distance metric learning methods. The use of quadric-chi similarity in our method leads to an effective learning algorithm. Our method is tested on SIFT features for face identification, and compared with the state-of-art metric learning method (LDML) on the benchmark dataset, the Labeled Faces in the Wild (LFW). Experimental results show that our method can achieve clear performance gains over LDML.

关键词： histogram feature metric learning Quardic-chi similarity face identification

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A Local Learning Based Image-To-Class Distance for Image Classification

A Local Learning Based Image-To-Class Distance for Image Cla...

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第一届亚洲模式识别会议

作者： Xinyuan Cai Rongguo Zhang Baihua Xiao Chunheng Wang State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation

ISBN: (纸本)9781457701221

Image-To-Class distance is first proposed in Naive- Bayes Nearest-Neighbor. NBNN is a feature-based image classifier, and can achieve impressive classification accuracy. However, the performance of NBNN relies heavily on the large number of training samples. If using small number of training samples, the performance will degrade. The goal of this paper is to address this issue. The main contribution of this paper is that we propose a robust Image-to-Class distance by local learning. We define the patch-to-class distance as the distance between the input patch to its nearest neighbor in one class, which is reconstructed in the local manifold space;and then our image-toclass distance is the sum of patch-to-class distance. Furthermore, we take advantage of large-margin metric learning framework to obtain a proper Mahalanobis metric for each class. We evaluate the proposed method on four benchmark datasets: Caltech, Corel, Scene13, and Graz. The results show that our defined Image-To-Class Distance is more robust than NBNN and Optimal-NBNN, and by combining with the learned metric for each class, our method can achieve significant improvement over previous reported results on these datasets.

关键词： local learning image-to-class distance large margin metric learning Na(i)ve Bayes Nearest-Neighbor image classification

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Contextual Constrained Independent Component Analysis Based Foreground Detection for Indoor Surveillance

Contextual Constrained Independent Component Analysis Based ...

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第一届亚洲模式识别会议

作者： Zhong Zhang Wen Zhou Baihua Xiao Shuang Liu Chunheng Wang State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation

ISBN: (纸本)9781457701221

Recently, Independent Component Analysis based foreground detection has been proposed for indoor surveillance applications where the foreground tends to move slowly or remain still. Yet such a method often causes discrete segmented foreground objects. In this paper, we propose a novel foreground detection method named Contextual Constrained Independent Component Analysis (CCICA) to tackle this problem. In our method, the contextual constraints are explicitly added to the optimization objective function, which indicate the similarity relationship among neighboring pixels. In this way, the obtained de-mixing matrix can produce the complete foreground compared with the previous ICA model. In addition, our method performs robust to the indoor illumination changes and features a high processing speed. Two sets of image sequences involving room lights switching on/of and door opening/closing are tested. The experimental results clearly demonstrate an improvement over the basic ICA model and the image difference method.

关键词： foreground detection contextual constraints indoor surveillance independent component analysis

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Background Modeling by Exploring Multi-scale Fusion of Texture and Intensity in complex Scenes

Background Modeling by Exploring Multi-scale Fusion of Textu...

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第一届亚洲模式识别会议

作者： Zhong Zhang Wen Zhou Chunheng Wang Baihua Xiao Shuang Liu State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation

ISBN: (纸本)9781457701221

Background modeling is a fundamental yet challenging issue in video surveillance. Traditional methods usually adopt single feature type to solve the problem, while the performance is usually unsatisfactory when handling complex scenes. In this paper, we propose a multi-scale framework, which combines both texture and intensity feature, to achieve a robust and accurate solution. Our contributions are three folds: first, we provide a multi-scale analysis for the issue;second, for texture feature we propose a novel texture operator named Scale-invariant Centersymmetric Local Ternary Pattern, and a corresponding Pattern Adaptive Kernel Density Estimation technique for its probability estimation;third, we design a Simplified Gaussian Mixture Models for intensity feature. Our method is tested on several complex real world videos with illumination variation, soft shadows and dynamic backgrounds. The experimental results clearly demonstrate that our method is superior to the previous methods.

关键词： background modeling multi-scale fusion texture intensity

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Translational offset detection based on far-field intensity for grating tiling

Translational offset detection based on far-field intensity ...

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2011 IEEE International Conference on Mechatronics and Automation, ICMA 2011

作者： Huang, Yanlong Fang, Zaojun Xu, De Tan, Min Xia, Lan State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Laboratory of Shanghai Institute of Laser and Plasma Chinese Academy of Engineering Physics Shanghai China

ISBN: (纸本)9781424481149

The far-field intensity is detected from far-field image to estimate the piston distance between two gratings. The image processing algorithm includes projections along the horizontal and vertical directions, search for each focal spot's centre, feature extraction and intensity computation. Each focal spot's centre can be found with the projections. A self-growing method is used for feature extraction, where the threshold value depends on the gray value of each focal spot's centre. For each focal spot, the sum of gray values within the relevant domain is taken to be its energy intensity. Furthermore, the energy ratio of left and main focal spots (or main and right focal spots) is computed. A formula that expresses the piston distance between two gratings as the function of the energy ratio is fitted with several measured points. Based on this formula, the piston distance is obtained for a new energy-ratio. Finally, the proposed method is verified with a series of experiments. © 2011 IEEE.

关键词： Image processing

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