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Chinese Control and Decision Conference, CCDC

作者： Yuying Guo Youmin Zhang Peng Shi Bin Jiang Zhengwei Zhu School of Information Engineering Southwest University of Science and Technology Mianyang China Department of Mechanical and Industrial Engineering Concordia University Montreal QUE Canada School of Engineering and Science Victoria University VIC Australia Department of Computing and Mathematical Sciences University of Glamorgan Pontypridd UK College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

This paper concerns the application of multi-model based adaptive control approach to accommodate actuator fault for flight control systems. isolation. This approach does not require the exact information about the controlled system and persistent input excitations. The method can increase robustness and provides stable adaptation of unknown faults. Asymptotic model following conditions and adaptive rules are derived and system stability is guaranteed, while appropriate switching of the multiple models ensures asymptotic tracking for system outputs. An aircraft model is given to illustrate the efficiency of the proposed method.

关键词： Adaptation models Actuators Elevators Switches Adaptive control Atmospheric modeling

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An Effective Approach for the Scheduling of Steel-making and Continuous Casting System with Stochastic Processing Requirements

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IFAC Proceedings Volumes 2011年第1期44卷 9500-9505页

作者： Liangliang Sun Wei Liu Peter B Luh Tianyou Chai Binglin Zheng State Key Laboratory of Integrated Automation for Process Industry (Northeastern University) Shenyang Liaoning Province China 110819 (Tel: +86(24)23998315) Manufacturing Computing Lab Electrical Engineering Department University of Connecticut Storrs Connecticut Unite States of American (Tel: +1(860)4861801))

Abstract The steel-making and continuous casting system (SCCS) is the bottleneck in the iron and steel production, SCCS often involve various uncertainties such as the emergency customer orders, inaccurate estimate of ingredient components, the unpredictable machine breakdown or the inaccurate estimate of processing time. How to consider such uncertainties to build a better schedule in a limited time by a computationally efficient manner is becoming critical for the production of iron and steel. The stochastic dynamic programming method is adopt to solve the obtained subproblems which are relaxed by Lagrangian relaxation multipliers, a good dual solution is selected by using “ordinal optimization,” and the actual schedule is dynamically constructed based on the dual solution and the realization of random processing requirements. The method has been tested by using practical data from the Shanghai Bashan steel plant in China and could get near optimal solutions in a limited time; the stochastic processing requirements are effectively handled for the production.

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Applications classification and scheduling on heterogeneous hpc systems using experimental research

Journal of Digital Information Management

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Journal of Digital Information Management 2011年第6期9卷 227-232页

作者： Xia, Yingjie Zhu, Mingzhe Kuang, Li Ma, Xiaoqiang Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China Hangzhou Institute of Service Engineering Hangzhou Normal University Hangzhou 310012 China School of Computing Science Simon Fraser University Burnaby BC V5A1S6 Canada

Heterogeneous high-performance computing (HPC) systems have been proposed as a power efficient alternative to traditional homogeneous systems. In heterogeneous HPC system, fast CPUs which have complex pipelines, high clock frequencies as well as high power consumption are combined with slow ones which have simple pipelines, low clock frequencies as well as low power consumption. Different types of applications should be distributed to run on different type of CPUs, in order to achieve a trade-off between energy and performance. In this paper, we use experimental research to investigate the applications classifications which categorize each application as the CPU-intensive, memory-intensive, or phase-change application. We also conduct some experiments to measure the current, power, and energy of different types of applications. Afterwards, a scheduling method for applications on heterogeneous HPC systems is proposed. The experiment shows that the scheduling method can trade off the latency and energy consumption based on the applications classification and measurement.

关键词： Program processors

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Fault identification for nonlinear switched systems: Average dwell time approach

Fault identification for nonlinear switched systems: Average...

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作者： Du, Dongsheng Jiang, Bin Shi, Peng College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 Jiangsu China School of Science Huaihai Institute of Technology 59 Cangwu Road Lianyungang 222005 Jiangsu China Department of Computing and Mathematical Sciences University of Glamorgan Pontypridd CF37 1DL United Kingdom School of Engineering and Science Victoria University Melbourne VIC 8001 Australia School of Mathematics and Statistics University of South Australia Mawson Lakes 5095 Australia

ISBN: (纸本)9789881725592

This paper investigates process fault identification for continuous-time nonlinear switched systems. A novel fault estimation algorithm, based on an adaptive fault diagnosis observer and average dwell time technique, is first proposed. In terms of linear matrix inequality, sufficient condition for the existence of the adaptive observer is derived. Simulation results are presented to illustrate the efficiency of the proposed results. © 2011 Chinese Assoc of Automati.

关键词： Linear matrix inequalities

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A hierarchical approach to represent relational data applied to clustering tasks

A hierarchical approach to represent relational data applied...

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International Joint Conference on Neural Networks (IJCNN)

作者： João C Xavier Anne M P Canuto Alex A Freitas Luiz M G Gonçalves Carlos N. Silla Universidade Federal do Rio Grande do Norte Natal RN BR Informatics and Applied Mathematics Department Federal University of Rio Grande do Norte (UFRN) Natal RN BRAZIL School of Computing University of Kent Canterbury Kent UK Computing and Automation Engineering Department Federal University of Rio Grande do Norte (UFRN) Natal RN BRAZIL

Nowadays, the representation of many real word problems needs to use some type of relational model. As a consequence, information used by a wide range of systems has been stored in multi relational tables. However, from a data mining point of view, it has been a problem, since most of the traditional data mining algorithms have not been originally proposed to handle this type of data without discarding relationship information. Aiming to ameliorate this problem, we propose a hierarchical approach for handling relational data. In this approach the relational data is converted into a hierarchical structure (the main table as the root and the relations as the nodes). This hierarchical way to represent relational data can be used either for classification or clustering purposes. In this paper, we will use it in clustering algorithms. In order to do so, we propose a hierarchical distance metric to compute the similarity between the tables. In the empirical analysis, we will apply the proposed approach in two well-known clustering algorithms (k-means and agglomerative hierarchical). Finally, this paper also compares the effectiveness of our approach with one existing relational approach.

关键词： Clustering algorithms Indexes Sediments Measurement Relational databases Data mining

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Gabor filter and eigen-flame image-based burning state recognition for sintering process of rotary kiln

Gabor filter and eigen-flame image-based burning state recog...

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IEEE Conference on Decision and Control

作者： Weitao Li Kezhi Mao Tianyou Chai Hong Zhang Hong Wang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110004 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Department of Computing Science University of Alberta Edmonton T6G-2P8 Canada Key Laboratory of Synthetical Automation for Process Industries (Northeastern University) Shenyang 110819 China

ISBN: (纸本)9781612848006

Accurate recognition of burning state is critical in sintering process control of rotary kiln. Recently, flame image-based burning state recognition has received much attention. However, most of the existing methods demand accurate image segmentation, which is quite challenging due to poor image quality caused by smoke and dust inside the kiln. In this study, we develop a more reliable method for burning state recognition without image segmentation issue. From the experience of operators, more discriminable flame and material zones will facilitate the subsequent feature extraction and burning state recognition. Motivated by this knowledge, we propose to include texture analysis based on Gabor filter as a preprocessing to improve the recognition result further and then extract global features based on eigen-flame images decomposition, and finally recognize the burning state using pattern classifier. The advantages of our method are threefold. Firstly, our Gabor filter selection approach can generate a compact filter bank to distinguish ROIs much more and offers help to facilitate the sequel. Secondly, the eigen-flame image method provides global features of an image with large class separability and hence can lead to more accurate classification performance. Thirdly, the new method is more robust and reliable than image segmentation-based methods and temperature-based method. Experimental studies show the effectiveness of our method.

关键词： Gabor filters burning Kilns image segmentation Image quality Rotary kilns

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Efficient dual-armed cluster tool performance via branch and cut optimization algorithm

Efficient dual-armed cluster tool performance via branch and...

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World Congress on Intelligent Control and automation (WCICA)

作者： Liangliang Sun Peter B. Luh Shian-Ching Chiou Shi-Chung Chang Jen-Hsuan Ho Hsin Yuan Chen Ji-Lun Chen Joey Chang Sam Hsu Key Laboratory of Integrated Automation of Process Industry Ministry of Education Northeastern University Shenyang China Manufacture computing laboratory University of Connecticut Storrs USA Department of Electrical & Computer Engineering University of Connecticut Storrs CT USA School of electrical engineering National Taiwan University Taipei Taiwan Inotera Memories Inc. Taoyuan Taiwan

Fab is the facility that produces silicon wafers. Capital investment of semiconductor fab has grown to 3-4 billions. Semiconductor equipments' cost per fab may count up to 74% of the capital investment. The semiconductor manufacturing industry is moving towards an efficient cluster approach for semiconductor equipments to create a higher throughput for wafer processing. Physical Vapor Deposition (PVD) is one of the most important processing for wafers' production. To improve fab production, the key issues are to make a near-optimal tool sequence optimization in a computationally efficient manner and be able to assess the quality of the near-optimal results for PVD cluster tools. In this paper, based on the Petri net for the PVD cluster tools, the tool sequence optimization problem is formulated to minimize the makespan of the wafers processing time with the PVD cluster tools, and then the formulation is converted to linear to be solved by the branch-and-cut method in the standard commercial solver CPLEX. Special methods for the linear conversion are highlighted. Effective search strategy, proper time unit and suitable solution gap selection and proper management for the sparse matrix are adopted to reduce the CPU calculation time base on the characteristic of tool sequence optimization problem. Numerical testing demonstrated that the models and algorithms developed can effectively optimize production and improve the quality of fab production.

关键词： Production Optimization Robots Semiconductor device modeling Sparse matrices Firing Analytical models

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A novel kernel discriminant feature extraction framework based on mapped virtual samples for face recognition

A novel kernel discriminant feature extraction framework bas...

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IEEE International Conference on Image Processing

作者： Sheng Li Xiaoyuan Jing David Zhang Yongfang Yao Lusha Bian College of Automation Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Software Engineering Wuhan University of China Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China

In this paper, we propose a novel kernel discriminant feature extraction framework based on the mapped virtual samples (MVS) for face recognition. We calculate a non-symmetric kernel matrix by constructing a few virtual samples (including eigen-samples and common vector samples) in the input space, and then express kernel projection vectors by using mapped virtual samples (MVS). Under this framework, we realize two MVS-based representative kernel methods including kernel principal component analysis (KPCA) and generalized discriminant analysis (GDA). Experimental results on the AR and CAS-PEAL face databases demonstrate that the proposed framework can effectively improve the classification performance of kernel discriminant methods. In addition, the MVS-based kernel approaches have a lower computational cost in contrast with the related kernel methods.

关键词： Kernel Vectors Databases Face Training Feature extraction Face recognition

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Sparse cost-sensitive classifier with application to face recognition

Sparse cost-sensitive classifier with application to face re...

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IEEE International Conference on Image Processing

作者： Jiangyue Man Xiaoyuan Jing David Zhang Chao Lan College of Automation Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Software Engineering Wuhan University of China Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China

Sparse representation technique has been successfully employed to solve face recognition task. Though current sparse representation based classifier proves to achieve high classification accuracy, it implicitly assumes that the losses of all misclassifications are the same. However, in many real-world applications, different misclassifications could lead to different losses. Driven by this concern, we propose in this paper a sparse cost-sensitive classifier for face recognition. Our approach uses probabilistic model of sparse representation to estimate the posterior probabilities of a testing sample, calculates all the misclassification losses via the posterior probabilities and then predicts the class label by minimizing the losses. Experimental results on the public AR and FRGC face databases validate the efficacy of the proposed approach.

关键词： Face recognition Databases Face Image processing Principal component analysis Probabilistic logic Testing

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Adaptive Feedforward Control for Dynamically Substructured Systems Based on Neural Network Compensation

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IFAC Proceedings Volumes 2011年第1期44卷 944-949页

作者： G. Li J. Na D.P. Stoten X. Ren School of Engineering Computing and Mathematics University of Exeter North Park Road Exeter EX4 4QF UK Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming 650093 China Cryogenic System Section Plant Engineering Division ITER Organization St Paul Lez Durance 13115 France ACTLab Department of Mechanical Engineering University of Bristol Queens Building University Walk Bristol BS8 1TR UK School of Automation Beijing Institute of Technology Beijing 100081 China

Abstract The potential applications of dynamically substructured systems (DSS) with both numerical and physical substructures can be found in diverse dynamics testing fields. In this paper, a feedforward adaptive controller based on a neural network (NN) is proposed to improve the DSS testing performance. To facilitate the NN compensation design, a modified DSS framework is developed so that the DSS control can be considered as a regulation problem with disturbance rejection. Then an NN feedforward compensation technique is proposed to cope with uncertainties and nonlinearities in the DSS physical substructure. The proposed NN technique generalizes the existing results in the literature. Real-time experimental results on a mechanical test rig demonstrate the improved performance by using the NN compensation strategy.

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