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2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011

作者： Meng, Lijing Jin, Jing Wang, Xingyu Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai China

ISBN: (纸本)9781424493524

There has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain-computer interface systems (BCIs). The electrode channels usually used in SSVEP classification are O1, O2 and Oz. However, optimal results of SSVEP recognition could not be obtained from the same electrode setting (O1, O2 and Oz), which was caused by subject variation. In this paper, three methods (Sequential floating forward selection (SFFS), discrete particle swarm optimization (DPSO) and F-score) were employed to select the optimal electrode channels. The electrode channels obtained by SFFS, DPSO and F-score were compared with traditional electrode channels (i.e., O1, O2 and Oz), which are usually used in SSVEP BCI. The results show that SFFS and DPSO can obtain higher classification accuracy than traditional approach. The results also show that SFFS is superior to DPSO in terms of calculating time, and F-score is not good compared to other two methods. Channel selection can not only reduce features for data analysis but also reduce the time for channel installation. © 2011 IEEE.

关键词： Brain computer interface

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A data-driven soft sensor modeling for furnace temperature of opposed multi-burner gasifier

A data-driven soft sensor modeling for furnace temperature o...

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2011 7th International Conference on Natural Computation, ICNC 2011

作者： Li, Jie Zhong, Weimin Cheng, Hui Kong, Xiangdong Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781424499533

The Opposed Multi-Burner (OMB) Coal-Water Slurry (CWS) gasification is a new large-scale coal gasification technology with higher product yield, lower oxygen and coal consumption than that of Texaco CWS gasification technology. However, current furnace temperature measurements of OMB and other gaisifiers are unstable and even short-life due to the extreme internal environment: high temperature, strong corrosion, etc. Therefore a new data-driven soft sensor modeling technique for furnace temperature of OMB gasifier is proposed and the selection of secondary variables and model structure of BP neural network is studied in this paper. Results indicate that, the furnace temperature predictive model integrating Principal Component Analysis (PCA) and BP neural network has a promising performance with good predictive precision. © 2011 IEEE.

关键词： Coal

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Fast consensus of directed network via optimal pinning control

Fast consensus of directed network via optimal pinning contr...

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第三十二届中国控制会议

作者： YANG Wen WANG Ying WANG Xiaofan Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Science and Technology) Ministry of Education Department of Automation Shanghai Jiaotong University

ISBN: (纸本)9781479900305

In this paper,we consider fast and desired consensus of directed network via pinning ***,we provide a sufficient condition for the stability of desired consensus ***,we investigate the problem of selecting optimal pinned nodes for driving fastest consensus,which is formulated as an Mixed-Integer Semidefinite ***,we illustrate all the results by simulating on some typical directed networks.

关键词： Node selection Fast consensus Convex optimization

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Weighted least square support vector machine integrated with parameter optimization and its application in chemical process

Weighted least square support vector machine integrated with...

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2012 8th International Conference on Natural Computation, ICNC 2012

作者： Cui, Wen-Tong Yan, Xue-Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781457721311

Considering that outliers can disrupt the correlation structure of least square support vector machine (LS-SVM), and that the parameters of LS-SVM play an important role in the performance, a novel weighted least square support vector machine integrated with parameter optimization is proposed to obtain the optimal parameters and to eliminate the effect of outliers. Several LS-SVM variants are applied in simulation experimentation and chemical process respectively to demonstrate the satisfactory performance of the proposed method. © 2012 IEEE.

关键词： Vectors

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Improving the performance of online classifier by removing the error samples from offline training data

Improving the performance of online classifier by removing t...

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IEEE International Conference on Computer and Communications, ICCC 2015

作者： Zhang, Hanhan Jin, Jing Zhou, Sijie Zhang, Yu Wang, Xingyu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781467381253

Brain-computer interface (BCI) plays an important role in helping the people with severe motor disability. In event-related potential (ERP) based BCIs, subjects were asked to count the target stimulus in the offline experiment, the recorded electroencephalogram (EEG) data was used to train the classification mode. However, subjects may make mistakes in counting the target stimulus or be affected by the non-target stimulus. The target trials may not contain expected ERPs and the non-target trials may contain unexpected ERPs, which was called error samples. This paper intends to survey whether the classification accuracy could be improved after removing these error samples from offline training data. The result showed that the online performance of BCI system could be improved after selecting the offline samples for training the classification mode. © 2015 IEEE.

关键词： Brain computer interface

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Improved shuffled frog leaping algorithm for permutation flow shop scheduling

Improved shuffled frog leaping algorithm for permutation flo...

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2013 International Conference on Information and Communication Technology for education, ICTE 2013

作者： Xu, Zhenhao Xu, Xiao Gu, Xingsheng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

This paper studies the scheduling problem in a permutation flow shop with the objective of makespan, which is known as one of major problems in the field of scheduling. In order to solving the corresponding model, an improved shuffled frog leaping algorithm (ISFLA) is put forward for this kind of scheduling problem. A new leaping rule based on the characteristic of the problem is devised to improve the performance of the ISFLA. And it can not only produce feasible solutions but also maintain the structure characteristic of the SFLA. Computational experiments on benchmark sets show that the proposed ISFLA is greatly effective to generate good solutions relative to the other two algorithms. © 2014 WIT Press.

关键词： Scheduling

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A hybrid method for identifying T-S fuzzy models

A hybrid method for identifying T-S fuzzy models

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Wang, Honggang Zhao, Liang Du, Wenli Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781612841816

This paper presents a hybrid approach to extract compact Takagi-Sugeno fuzzy models from numeric data, using subtractive clustering (SC), particle swarm optimization (PSO) and least square method. The feature of this method lies in the following: (1) the input space is partitioned and initial fuzzy rule bases are extracted by SC;(2) the optimal parameters of the membership functions are evolved by PSO, based on the initial fuzzy rule bases;(3) the consequent parameters of the rule base are analytically derived by the Moore-Penrose pseudo inverse, instead of being iteratively tuned. Simulation results in Mackey-Glass chaotic time series prediction and nonlinear plant modeling problems show that the proposed method is effective and robust in finding compact and accurate T-S fuzzy models. © 2011 IEEE.

关键词： Membership functions

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On-line Signature Verification Based on Gaussian Mixture Models 29

On-line Signature Verification Based on Gaussian Mixture Mod...

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第29届中国控制与决策会议

作者： Lou Yang Mandan Liu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology East China University of Science and Technology

ISBN: (纸本)9781509046584

In this paper for on-line signature verification,wavelet packet analysis will be used to extract dynamic local features,combining global features to keep distortionless in signature *** importantly,in order to overcome shortcomings that the traditional expectation maximization algorithm seriously depends on parameters initialization and easily falls into local optimum when used to train Gaussian Mixture Models,we first employ an improved Splitting-EM algorithm based on Bayesian Ying-Yang learning system to train Gaussian Mixture ***-EM algorithm can search for optimal number of Gaussian components so that a unique,user-dependent signature model can be established to ensure a better *** show that the verification accuracy based on wavelet packet analysis to extract features and Splitting-EM algorithm training Gaussian Mixture Models reaches 95.8%,which is a satisfactory verification result.

关键词： On-line Signature Verification Gaussian Mixture Models Splitting-EM Algorithm Bayesian Ying-Yang Learning System

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Prediction of Cracking Gas Compressor Performance and Its Application in Process Optimization

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Chinese Journal of chemical Engineering 2012年第6期20卷 1089-1093页

作者：李绍军李凤 Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Science and Technology) Ministry of Education

Cracking gas compressor is usually a centrifugal compressor. The information on the performance of a centrifugal compressor under all conditions is not available, which restricts the operation optimization for compressor. To solve this problem, two back propagation (BP) neural networks were introduced to model the performance of a compressor by using the data provided by manufacturer. The input data of the model under other conditions should be corrected according to the similarity theory. The method was used to optimize the system of a cracking gas compressor by embedding the compressor performance model into the ASPEN PLUS model of compressor. The result shows that it is an effective method to optimize the compressor system.

关键词： compressor characteristic curve neural-network modeling

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Plant-wide optimization for high value added products - A steam cracking case study 30

Plant-wide optimization for high value added products - A st...

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30th Ethylene Producers Conference 2018 - Topical Conference at the 2018 AIChE Spring Meeting and 14th Global Congress on Process Safety

作者： Van Geem, Kevin M. Marin, Guy B. Muñoz, Andrés E.G. Zhang, Yu Du, Wenli Qian, Feng Laboratory for Chemical Technology Ghent University Belgium AVGI Syria Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology China

ISBN: (纸本)9781510864375

Recent progress in accurate modeling of steam cracking plants has enabled global optimization to be performed at a plant-wide level. This paper presents a real time optimization (RTO) system developed by East China University of Science and Technology (ECUST) to maximize the net profit of the ethylene plant of Sinopec Shanghai Petrochemical Company (SSPC). The system has a three-layer structure consisting of the global RTO, the local RTO and the advanced process control (APC) layer from top to bottom, among which the APC layer has already been applied to SSPC and is currently in service. COILSIM1D-RTO is used to simulate the steam cracking reactors. A simplified radiant box model and a convection section model are developed to account for the fuel consumption and superheated high pressure steam generation. The downstream compression and separation units are modeled in Aspen Plus to evaluate the maximum allowed process gas load and energy consumption. As the plant-wide RTO system is currently under implementation and no real time optimization results can be obtained, a case study is performed using historical operating data from year 2017 to illustrate the benefit that this plantwide RTO system can deliver. Optimization results show that most of the crackers should be operated under higher cracking severity to improve yields of the high value added products. By optimizing the cracking severity and dilution steam ratio, the estimated increase of net profit of the ethylene plant in Case 1 and Case 2 is about 29.50 million and 30.75 million yuan per year, respectively (i.e. approx. 4.7 million USD and 4.9 million USD, at current exchange rates). Copyright © American Institute Of chemical Engineers. All rights reserved.

关键词： Global optimization

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