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作者： Song, Bing Ma, Yuxin Shi, Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai200237 China

Conventional principal component analysis (PCA) can obtain low-dimensional representations of original data space, but the selection of principal components (PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined. All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used. © 2015 Elsevier B.V.

关键词： Principal component analysis

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A fast MPC algorithm for reducing computation burden of MIMO

A fast MPC algorithm for reducing computation burden of MIMO

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作者： Qi, Rongbin Mei, Hua Chen, Chao Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

The computation burden in the model-based predictive control algorithm is heavy when solving QR optimization with a limited sampling step, especially for a complicated system with large dimension. A fast algorithm is proposed in this paper to solve this problem, in which real-time values are modulated to bit streams to simplify the multiplication. In addition, manipulated variables in the prediction horizon are deduced to the current control horizon approximately by a recursive relation to decrease the dimension of QR optimization. The simulation results demonstrate the feasibility of this fast algorithm for MIMO systems. © 2015 Elsevier B.V.

关键词： MIMO systems

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Introducing Robustness in controllability of Neuronal Networks 34

Introducing Robustness in Controllability of Neuronal Networ...

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第三十四届中国控制会议

作者： TANG Yang DU Wei The Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology The Institute of Textiles and Clothing The Hong Kong Polytechnic University

ISBN: (纸本)9781467374439

This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain,in which uncertainties in determination of driver nodes and control gains are considered. A framework by including interval uncertainties is proposed for robust controllability. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affect the controllability of neuronal networks.

关键词： Synchronization,Neuronal networks,controllability,Robustness,Multiobjective optimization

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Fuzzy H∞ Filtering for Nonlinear Networked Systems with Multiple Stochastic Time-Varying Delays, Packet Dropouts and Sensor Saturation 34

Fuzzy H∞ Filtering for Nonlinear Networked Systems with Mul...

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第三十四届中国控制会议

作者： XU Xiaoli YAN Huaicheng ZHANG Hao SHI Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education School of information Science and Engineering East China University of Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (纸本)9781467374439

This paper investigates the problem of H∞ filter design for a class of nonlinear networked system based on T-S fuzzy model. Multiple stochastic time-varying delays and some incomplete information are considered simultaneously. Incomplete information includes randomly occurring sensor saturation and packet dropouts. Stochastic time-varying delays are depicted as a sequence of stochastic and independent variables, which take values on 0 and 1. Two sets of Bernoulli distributed white noises are introduced to describe randomly occurring sensor saturation and packet dropouts. System conservatism is reduced due to introduce an approach of piecewise quadratic Lyapunov function. By solving a set of linear matrix inequalities(LMIs), the filter parameters are obtained. Finally, a simulation example is provided to illustrate the effectiveness of the proposed filter design approach.

关键词： H ∞ filtering,multiple time-varying delays,randomly occurring sensor saturation,packet dropouts,piecewise quadratic Lyapunov function

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Sparse optimal score based on generalized elastic net model for brain computer interface

Sparse optimal score based on generalized elastic net model ...

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International Conference on Information Science and Technology (ICIST)

作者： Qiang Wu Yu Zhang Ju Liu Jiande Sun Jie Li School of Information Science and Engineering Shandong University Jinan Shandong China Key Laboratory for Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China Department of Computer Science and Technology Tongji University Shanghai China

ISBN: (纸本)9781509012251

Brain computer interface (BCI) offers disabled people a nonmuscular communication pathway. Event-related potential (ERP) is an efficient way to achieve the BCI system. One of important issues for ERP classification is the under sample problem, that is the feature dimension is very high while the sample number is very strictly limited. In this paper, we introduce a P300 feature extraction and classification framework using the sparse optimal score method for discriminative analysis by generalized elastic net model. In order to break the curse of dimension, regularized estimation of within-class covariance matrix is achieved and ℓ1 penalty is applied to learn sparse discriminant vectors. The optimization problem is solved by the alternating least square procedure. We test the proposed framework on P300 target detection task and experimental results indicate that it is able to improve the classification accuracy in P300-based BCI system. The efficient features extracted by our proposed framework provide overall better P300 classification accuracy than several baseline methods especially in the single trial and few training samples case.

关键词： Training Feature extraction Algorithm design and analysis Covariance matrices optimization Electroencephalography Brain modeling

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Efficient Co3O4 Nanorod-Supported Pt Catalyst for Room-Temperature Decomposition of HCHO

Efficient Co3O4 Nanorod-Supported Pt Catalyst for Room-Tempe...

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第二届湖北省化学化工青年科学家论坛

作者： Zhihua Xu Zhaoxiong Yan Jiaguo Yu Key Laboratory of Optoelectronic Chemical Materials and Devices of Ministry of Education and Hubei Key Laboratory of Industrial Fume and Dust Pollutio ControlJianghan University State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology

Formaldehyde（HCHO）emitted from the widely used building and furnishing materials,etc.,is regarded as a typical indoor air *** formaldehyde（HCHO）from air at room（ambient）temperature by effective catalysts is of significance for improving indoorair *** catalysts with high efficiency and good recyclability are highly *** this study,Pt supported on

关键词： Efficient Co3O4 Nanorod-Supported Pt Catalyst for Room-Temperature Decomposition of HCHO

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$H_{\infty }$ Fault Detection for Networked Mechanical Spring-Mass Systems With Incomplete Information

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IEEE Transactions on Industrial Electronics 2016年第9期63卷 5622-5631页

作者： Huaicheng Yan Fengfeng Qian Hao Zhang Fuwen Yang Ge Guo Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education and also with the School of Information Science and Engineering East China University of Science and Technology Shanghai China Department of Control Science and Engineering Tongji University Shanghai China School of Engineering Griffith University Gold Coast QLD Australia School of Information Science and Technology Dalian Maritime University Dalian China

This paper focuses on the H ∞ fault detection (FD) problem for spring-mass systems (SMSs) over networks with distributed state delays, random packet losses, sensor saturation as well as multiplicative noises via unreliable communication channels. The output measurements are affected by sensor saturation which is described by sector-nonlinearities. The multiplicative noises are described as a form of Gaussian white noises multiplied by the states. A series of stochastic variables are introduced to describe the randomly occurring distributed state delays. Random packet losses are also introduced in unreliable communications. The purpose of this paper is to design an FD filter such that: 1) The FD dynamic system is exponentially stable in the mean square. 2) The error between the fault signal and the residual signal is controlled to the minimum. 3) The optimal H ∞ filtering performance index is achieved. A sufficient condition for the FD filter design is derived in terms of the solution to a linear matrix inequality (LMI). When the LMI has a feasible solution, the explicit parameters of the desired FD filter can be obtained. Finally, a simulation experiment is illustrated to show the effectiveness and application of the designed method.

关键词： Fault detection Packet loss Vehicles Delays Robustness Suspensions

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Cultivation of three microalgae strains under mixotrophic conditions for biodiesel production

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chemical Engineering Transactions 2016年 50卷 409-414页

作者： Rios, Luisa F. Da Silva Soares, Carla Tasić, Marija B. Maciel, Maria R. Wolf Filho, Rubens Maciel University of Campinas Chemical Engineering Faculty Laboratory of Optimization Design and Advanced Process Control-Bioen Division Av. Albert Einstein 500 Campinas SP Brazil Faculty of Technology University of Niš Bulevar oslobodjenja 124 Leskovac16000 Serbia

ISBN: (纸本)9788895608419

Microalgal biomass have a potential to be used as feedstock for biodiesel production because of high photosynthetic rates, high biomass production, faster growth in comparison to traditional feedstocks and to not be competitive with food production. In this paper, the growth and lipid content of three microalgae strains, namely Chorella vulgaris, Desmodesmus sp. and Desmodesmus brasiliensis, were studied during 9 days of mixotrophic cultivation. The laboratory scale experiments were carried out in BG-11 media enriched with 10 g/L glucose at initial medium pH of 7.5, temperature of 26 ± 4 °C and light flux of 62 μE m-2 s-1. The highest biomass concentration of 3.82 mg/mL was found in the Desmodesmus brasiliensis, followed with 3.64 mg/mL of Desmodesmus sp. and 3.32 mg/mL of Chorella vulgaris strain. However, the Desmodesmus brasiliensis had the lowest lipid content of 6%, followed with 13.2% of Chorella vulgaris, whereas the Desmodesmus sp. strain produced the highest lipids (19.45%). Copyright © 2016, AIDIC Servizi S.r.l.,.

关键词： Feedstocks

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Improved performance of process monitoring based on selection of key principal components

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Chinese Journal of chemical Engineering 2015年第12期23卷 1951-1957页

作者：宋冰马玉鑫侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and TechnologyMinistry of Education

Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be *** the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.

关键词： Principal component analysis Information loss Fault detection key principal component

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A fast MPC algorithm for reducing computation burden of MIMO

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Chinese Journal of chemical Engineering 2015年第12期23卷 2087-2091页

作者：祁荣宾梅华陈超钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and Technology

关键词： Fast MPC algorithm Computation burden One-bit operation Dimension reduction

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