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11th International Conference on Natural Computation, ICNC 2015

作者： Hu, Huiping Jin, Jing Zhang, Yu Wang, Xingyu Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai200237 China

ISBN: (纸本)9781467376792

Brain computer interface (BCI) could help patients to manipulate external devices based on the specific brain activities. One of the most popular BCI systems is the visual-based BCI system. Mostly, users were asked to pay attention not only to the target stimulus, but also to the real control target which would bring high workload to users and affect the control efficiency of BCI systems. In this paper, a real-time monitoring system was developed to solve this problem by showing the environment information from the camera on the computer screen. Five subjects took part in this experiment and all of them were asked to control a small car to the target position. Our result showed that all subjects could finish the task within two or three minutes. In this study, subject did not need to switch their attention on the car which was out of their sight, and it would help to improve the usability of BCI in the practical application. © 2015 IEEE.

关键词： Cameras

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

Improved performance of process monitoring based on selectio...

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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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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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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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$H_{\infty }$ 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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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 novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier

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Chinese Journal of chemical Engineering 2015年第9期23卷 1484-1501页

作者：曹萃文张亚坤于腾顾幸生辛忠李杰 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology State Key Laboratory of Chemical Engineering East China University of Science and Technology State Key Laboratory of Multiphase Complex Systems Institute of Processing Engineering Chinese Academy of Sciences

Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm(DE), genetic algorithm(GA), and particle swarm optimization algorithm(PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO(3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms(EAs) can be improved,and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms.

关键词： 3-Layer mixed cultural evolutionary framework Optimal operation Syngas production Coal-water slurry gasifier

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Design of a visual information-based brain-computer interface control system

Design of a visual information-based brain-computer interfac...

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International Conference on Natural Computation (ICNC)

作者： Huiping Hu Jing Jin Yu Zhang Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai China

ISBN: (纸本)9781467376808

关键词： Electrodes Silicon Biology Monitoring Electroencephalography Navigation Robots

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Design and control of a p-xylene oxidation process

Design and control of a p-xylene oxidation process

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作者： Tao, Lili Hu, Zhihua Qian, Feng College of Engineering Shanghai Second Polytechnic University Shanghai201209 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

The p-xylene (PX) oxidation process is of great industrial importance because of the strong demand of the global polyester fiber. A steady-state model of the PX oxidation has been studied by many researchers. In our previous work, a novel industrial p-xylene oxidation reactor model using the free radical mechanism based kinetics has been developed. However, the disturbances such as production rate change, feed composition variability and reactor temperature changes widely exist in the industry process. In this paper, dynamic simulation of the PX oxidation reactor was designed by Aspen Dynamics and used to develop an effective plantwide control structure, which was capable of effectively handling the disturbances in the load and the temperature of the reactor. Step responses of the control structure to the disturbances were shown and served as the foundation of the smooth operation and advanced control strategy of this process in our future work. © 2015 Elsevier B.V.

关键词： Oxidation

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Robust optimization for a Multi-Product Integrated Problem of Planning and Scheduling under Products Uncertainty

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Journal of Applied Mathematics and Physics 2015年第1期3卷 16-24页

作者： Mengwen Chen Cuiwen Cao Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling (based on the work of Terrazas-Moreno & Grossmann (2011) [1]) under products prices uncertainty. With the objective of maximizing the total profit in planning time horizon, the planning section determines the amount of each product, each product distributed to each market, and the inventory level in each manufacturing site during each scheduling time period;the scheduling section determines the products sequence, start and end time of each product running in each production site during each scheduling time period. The uncertainty sets used in robust optimization model are box set, ellipsoidal set, polyhedral set, combined box and ellipsoidal set, combined box and polyhedral set, combined box, ellipsoidal and polyhedral set. The genetic algorithm is utilized to solve the robust optimization models. Case studies show that the solutions obtained from robust optimization models are better than the solutions obtained from the original integrated planning and scheduling when the prices are changed.

关键词： Uncertainty Robust optimization Integrated Problem of Planning and Scheduling GA

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