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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 comparison of three electrode channels selection methods applied to SSVEP BCI

A comparison of three electrode channels selection methods a...

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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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Peak Traffic Forecasting Using Nonparametric Approaches

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上海交通大学学报（英文版） 2012年第1期17卷 76-81页

作者： ZHANG Yang WANG Meng-ling Shanghai Municipal Transportation Information Center Shanghai 200032 China Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237 China

States of traffic situations can be classified into peak and nonpeak periods. The complexity of peak traffic brings more difficulty to forecasting models. Travel time index (TTI) is a fundamental measure in transportation. How to master the characteristics and provide accurate real-time forecasts is essential to intelligent transportation systems (ITS). Cooperating with state space approach, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem in this paper. To the best of our knowledge, it is the first time to apply the technique and analyze the forecast performance in the domain. For comparison purpose, other two nonparametric predictors are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising.

关键词： traffic forecasting peak traffic nonparametric techniques

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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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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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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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Enhancing the Performance of JADE Using Two-phase Parameter control Scheme and Its Application

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International Journal of Automation and computing 2018年第4期15卷 462-473页

作者： Qin-Qin Fan Yi-Lian Zhang Xue-Feng Yan Zhi-Huan Wang Logistics Research Center Shanghai Maritime UniversityShanghai 201306China Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237China Key Laboratory of Marine Technology and Control Engineering Ministry of Communications Shanghai Maritime UniversityShanghai 201306China

The search efficiency of differential evolution （DE） algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters for the DE, most techniques are based on pop- ulation information which may be misleading in solving complex optimization problems. Therefore, a self-adaptive DE （i.e., JADE） using two-phase parameter control scheme （TPC-JADE） is proposed to enhance the performance of DE in the current study. In the TPC-JADE, an adaptation technique is utilized to generate the control parameters in the early population evolution, and a well-known empirical guideline is used to update the control parameters in the later evolution stages. The TPC-JADE is compared with four state-of-the-art DE variants on two famous test suites （i.e., IEEE CEC2005 and IEEE CEC2015）. Results indicate that the overall performance of the TPC-JADE is better than that of the other compared algorithms. In addition, the proposed algorithm is utilized to obtain optimal nutrient and inducer feeding for the Lee-Ramirez bioreactor. Experimental results show that the TPC-JADE can perform well on an actual dynamic optimization problem.

关键词： Differential evolution(DE)algorithm evolutionary computation dynamic optimization control parameter adaptation chemical processes.

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Experimental and theoretical insights into copper phthalocyanine-based covalent organic frameworks for highly efficien radioactive iodine capture

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Chinese chemical Letters 2022年第7期33卷 3549-3555页

作者： Xuewei Liu Anrui Zhang Ran Ma Bo Wu Tao Wen Yuejie Ai Mingtai Sun Jie Jin Suhua Wang Xiangke Wang MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environment and Chemical EngineeringNorth China Electric Power UniversityBeijing 102206China Guangdong Provincial Key Laboratory of Petrochemical Pollution Process and Control School of Environmental Science and EngineeringGuangdong University of Petrochemical TechnologyMaoming 525000China

Exploring efficient materials for capturing radioactive iodine in nuclear waste is of great significance for the progress of nuclear energy as well as the protection of ecological *** organic frameworks(COFs)have emerged as promising adsorbents because of their predesignable and functionalizable skeleton ***,it remains a grand challenge to achieve large scale preparation of *** this work,we developed a mild and efficient microwave irradiation method instead of the traditional solvothermal method to prepare copper phthalocyanine-based covalent organic frameworks(Cu_(x)Pc-COFs)within only 15 *** nitrogen-rich 1,2,4,5-tetracarbonitrilebenzene(TCNB)was selected as the solely organic ligand to construct copper phthalocyanine-based 2D conjugated *** resultant Cu_(x)Pc-COFs exhibited excellent iodine enrichment with 2.99 g/g for volatile iodine and 492.27 mg/g for iodine-cyclohexane solution,respectively,outperforming that of many porous *** indicated by spectroscopic analysis and DFT calculations,this impressive adsorption performance can be attributed to the charge transfer arising from nitrogen-rich phthalocyanine structures and electron-richπ-conjugated systems with iodine ***,the strong electrostatic interaction between Cu(Ⅱ)on chelate centers and polyiodide anions(I_(x)^(-))also play an important role in the firmly trapping radioactive ***,this study provides a facile and intelligent approach to implement metal-based COFs for the remediation of toxic radioactive iodine.

关键词： Cu_(x)Pc-COFs Microwave Iodine adsorption Charge transfer Electrostatic interaction

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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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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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