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检索条件"机构=State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control"

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Global-Local Structure Analysis for Fault Detection

Global-Local Structure Analysis for Fault Detection

引用

2010 49th IEEE Conference on Decision and control

作者： Ruowei Fu Muguang Zhang Zhihuan Song Zhiqiang Ge State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou 310027 Zhejiang China State Key Laboratory of Industrial Control Technology Ningbo Institute of Technology Zhejiang University Ningbo 315100 China

ISBN: (纸本)9781424477456

In this paper a new dimensionality reduction technique named global-local structure analysis (GLSA) is proposed. It constructs a dual-objective optimization function, which exploits the underlying geometrical manifold and keeps the global information for dimensionality reduction simultaneously. This combines the advantages of locality preserving projections (LPP) and principal component analysis (PCA) under a unified framework. Besides, GLSA successfully avoids the singularity problem in LPP and shares the orthogonal property with PCA. A further contribution of this paper is to propose a strategy for determining the parameter eta which is used to balance the subobjectives corresponding to global and local structure preservings. For fault detection purpose, two traditional statistics T~(2) and SPE are constructed based on the new proposed GLSA method. Case studies on a numerical example and Tennessee Eastman process demonstrate the efficiencies of GLSA in feature extraction and fault detection.

关键词： Defect detection structured analysis dimensionality reduction numerical example Feature extraction

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Neuron controller with Fuzzy Gain Scheduling Based on Takagi-Sugeno Scheme for Hydraulic Turbine Generators

Neuron Controller with Fuzzy Gain Scheduling Based on Takagi...

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The 8th World Congress on Intelligent control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Ning Wang Wei Zhang Jili Tao National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejian Ningbo Institute of Technology Zhejiang University Ningbo Zhejiang Province China 315100

A neuron model-free control method with fuzzy gain scheduling is presented in this paper. A single neuron is designed to produce control signal in model-free way, and the gain of the neuron controller is tuned by Takagi-Sugeno fuzzy scheme. Simulation tests of a hydraulic turbine generator working under different operating conditions are made. The results show good performance and strong robustness of the proposed method.

关键词： Neurons Hydraulic turbines Generators Takagi-Sugeno model Process control control systems Load modeling

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Dynamic Optimization of Multivariable Endothermic Reaction in Cascade CSTR

Dynamic Optimization of Multivariable Endothermic Reaction i...

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The 8th World Congress on Intelligent control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Yang Chen Zhijiang Shao Jixin Qian Kexin Wang Zhiliang Zhan Zuhua Xu State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou Zhejiang China

The dynamic optimization problem of a multivariable endothermic reaction in cascade continuous stirred tank reactors is solved with simultaneous method in this paper. Radau collocation is applied in discretization because of its stiff decay and high precision. A two-layer optimization is presented to get a fast convergence rate when dealing with the nonlinear case. In the industry process, the load variation may be very large and may cause output variable's big overshoot. In order to reduce the overshoot, a segmentation load variation method is introduced. The good results of this nonlinear ordinary differential equations system show the validity of these methods.

关键词： Optimization Load management Cooling Chemical reactors Steady-state Biological system modeling Nonlinear dynamical systems

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Adaptive Local Weighted Kernel-based Regression for Online Modeling of Batch Processes

Adaptive Local Weighted Kernel-based Regression for Online M...

引用

2010 IEEE International Conference on Intelligent Computing and Intelligent systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Haiqing Wang Zhihuan Song Kun Chen Jun Ji Yi Liu State Key Laboratory of Industrial Control Technology Institute of Industrial Process ControlZhejia Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou P

Fed-batch processes are inherently more difficult to characterize than continuous processes due to the variations under different operation stages, drifting and small-sample condition. The classical kernel-based regression (KR) methods, e.g., least squares support vector regression (LSSVR), aim to achieve a universal generalization performance, which may fail in some local regions when applied to batch process modeling. Local LSSVR model which only uses the neighbors of the query instance helps improve the accuracy, but it generally leads to a heavy computation load. Inspired by the idea of universal and local learning simultaneously, an adaptive local weighted kernel-based regression (ALWKR) method is proposed. That is. adaptive weights are assigned to corresponding samples based on the similarity measurement, followed by a recursive updating to obtain local models. This ALW-KR framework is applied to the prediction of biomass concentration in the penicillin fed-batch process. The experimental results show that the proposed ALWKR model could predict the biomass concentration more accurate and robust to batch-to-batch variation than traditional KR methods.

关键词： kernel-based regression adaptive local weight similarity index recursive updating fed-atch process modeling

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Probabilistic process monitoring with Bayesian regularization

Probabilistic process monitoring with Bayesian regularizatio...

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American control Conference

作者： Muguang Zhang Zhiqiang Ge Zhihuan Song State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou 310027 Zhejiang China

ISBN: (纸本)9781424474264

To monitor industrial processes through a probabilistic manner, the probabilistic principal component analysis (PPCA) method has recently been introduced. However, PPCA has its inherent limitation that it cannot determine the effective dimensionality of latent variables. This paper intends to introduce a Bayesian treatment upon the traditional principal component analysis method for process monitoring, which can automatically determine the effective number of retained principal components. Thus, a Bayesian principal component analysis based monitoring approach is developed. A case study of the Tennessee Eastman (TE) benchmark process shows the feasibility and efficiency of the proposed method.

关键词： Process monitoring Probabilistic Process Bayesian inference industrial processes component analysis

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Adaptive Least Contribution Elimination Kernel Learning Approach for Rubber Mixing Soft-sensing Modeling

Adaptive Least Contribution Elimination Kernel Learning Appr...

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2010 IEEE International Conference on Intelligent Computing and Intelligent systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Yan-chen Gao Jun Ji Hai-qing Wang Ping Li State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou P.R.China

Rubber mixing process is a typical nonlinear fed-batch process without well developed mechanism. Soft-sensing modeling of the mixture's Mooney viscosity is crucial and challenging since this index is an important process criterion to judge the quality of rubber compounds while the measurement of Mooney viscosity is time-consuming and laborious to assay. Furthermore, the mixing process is drifting and volatile even noisy;only few data samples could be used to modeling. In present paper, an adaptive least contribution elimination kernel learning (ALCEKL) approach is proposed to predict the Mooney viscosity. It adopts a sparsity strategy of least contribution elimination and presents a buffer based learning algorithm associated with improved space angle index (SAI) strategy. Experiments on field data indicate that proposed approach is more robust and accurate than other kernelized modeling methods with feasible computational complexity under field circumstances.

关键词： component kernel learning rubber mixing softsensor Mooney viscosity ALCEKL

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Free-tagging methods for opinion analysis of online news comments

Free-tagging methods for opinion analysis of online news com...

引用

2010 8th IEEE International Conference on control and Automation, ICCA 2010

作者： Zhao, Changwei Liu, Heng Peng, Qinke Yang, Ying State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an China School of Electronic and Information Engineering Henan University of Science and Technology Luoyang China National key Laboratory of Electromechanical Engineerning and Control Xi'an Institute of Electrical and Information Technology Xi'an China

ISBN: (纸本)9781424451951

To overcome challenges of processing timeliness and changeful topics when analyzing opinions of online news comments, free-tagging methods including two steps, object identification and polarity analysis, are proposed in this paper. In the first step, from news text, we employ the Affinity Propagation Cluster (APC) to extract key sentences whose name entities will be identified and their specified types will be identified as comment objects. After identifying comment objects, the method of distance-weight count of polarized words and semantic rules are used to determine the polarity of comments. Our method does not require labeled corpus, therefore it can reduce artificial workloads and avoid problems of non-versatility caused by changeful topics of comments. Experiment results show that our method is capable of identifying objects and polarity of online news comments with higher precision and wide coverage rate, and can satisfy the requirements of online news comments analysis. © 2010 IEEE.

关键词： Semantics

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A dynamic dual-rate beacon scheduling method of ZigBee/IEEE 802.15.4 for target tracking

A dynamic dual-rate beacon scheduling method of ZigBee/IEEE ...

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International Conference on Mobile Ad-hoc and Sensor Networks

作者： Chen, Shantao Almeida, Luís Wang, Zhi State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China Institute of Telecommunications DEEC - Faculty of Engineering University of Porto Rua Dr. Roberto Frias 4200-465 Porto Portugal

ISBN: (纸本)9780769543154

ZigBee/IEEE 802.15.4 beacon-enabled mode allows low duty-cycle operation with high energy saving in the wireless sensor network. However, low duty cycles introduce higher end-to-end delay in event propagation and generally lower reactivity of the network with respect to the actual sensing. In target tracking systems, both qualities should be satisfied, namely, low energy consumption while no target is being tracked and low end-to-end delay in target tracking mode. Hence, we need to define a trade-off between the energy consumption and the end-to-end delay. In this paper, we propose a dynamic dual-rate beacon scheduling method with two rate switching schemes modifying ZigBee/IEEE 802.15.4 to enhance its real-time property but still maintain the advantage of low energy consumption. We evaluate our scheme through a simulation using the OPNET simulator. The results show that our scheme exhibits low energy consumption while presenting reduced end-to-end delay, outperforming usual single rate working modes. © 2010 IEEE.

关键词： Target tracking

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Robust control for Discrete-Time Networked control systems

Robust Control for Discrete-Time Networked Control Systems

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The 8th World Congress on Intelligent control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Dongxiao Wu Jun Wu Sheng Chen State Key Laboratory of Industrial Control Technology　Institute of Cyber-System and Control　Zhejiang School of Electronics and Computer Science University of Southampton Highfield Southampton SO17 1BJ

This paper considers analysis and synthesis of discrete-time networked control systems (NCSs), where the plant has additive uncertainty and the controller is updated with the sensor information at stochastic time intervals. It is shown that the problem is linked to robust control of linear discrete-time stochastic systems and a new small gain theorem is established. Based on this result, sufficient conditions are given for ensuring mean square stability of the NCS, and the genetic algorithm is utilised to design the controller of the NCS based on a linear matrix inequality technique.

关键词： Stochastic systems Networked control systems Delay Stability analysis Robust control Observers Additives

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Notice of Retraction: An integral inequality approach to robust stability of uncertain neutral systems with multiple delays

Notice of Retraction: An integral inequality approach to rob...

引用

International Conference on industrial and Information systems, IIS

作者： Zu-Xin Li Yong Gu National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control University of Zhejiang Hangzhou Zhejiang China School of Information Engineering Huzhou Teacher''s College Huzhou Zhejiang China

This article has been retracted by the publisher.

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