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检索条件"机构=Key Laboratory of Advanced Control and Optimization for Chemical Processes"

共 631 条记录，以下是281-290 订阅

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Denoising and rhythms extraction of EEG under +Gz acceleration based on wavelet packet transform

Denoising and rhythms extraction of EEG under +Gz accelerati...

引用

2013 7th ICME International Conference on Complex Medical Engineering, CME 2013

作者： Li, Yifeng Zhang, Tao Deng, Lue Wang, Bei Nakamura, Masatoshi Department of Automation Tsinghua University Beijing 100084 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Shanghai 200237 China Air Force Institute of Aeromedicine Beijing 100142 China Research Institute of Systems Control Saga University Saga 840-0047 Japan

ISBN: (纸本)9781467329699

Filtering and feature extraction are very important in the analysis and study of EEG signal under +Gz acceleration. In this study, a new filter of different frequency characteristics of EEG signal under +Gz acceleration is constructed and four kinds of rhythms of EEG signal are extracted by using wavelet packet transformation. EEG under different G loads is analyzed and compared, and then EEG dynamic characteristics are studied in order to analyze its advantages and disadvantages. Experimental results show that wavelet packet method can effectively suppress interference bands in EEG, such as EMG, power and so on, and effectively reflect the dynamic characteristics of different rhythms, exhibiting good characteristics. The proposed method is also applicable for analyzing and studying other dynamic biomedical signals. © 2013 IEEE.

关键词： Extraction

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Quantized H_∞ control for Networked Systems with Randomly Multi-Step Transmission Delays

Quantized H_∞ Control for Networked Systems with Randomly M...

引用

Annual Conference of the IEEE Industrial Electronics Society

作者： Yilian Zhang Fuwen Yang Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

ISBN: (纸本)9781479940318

In this paper, a quantized H_∞ control problem for networked control systems (NCSs) subject to randomly multi-step transmission delays is investigated. A quantizer is used before the measurement signal enters the communication network and the randomly multi-step transmission delays which are described by a mathematical model are considered during the transmission through the network. Sufficient conditions are derived for the considered system to satisfy the H_∞ norm constraint subject to the randomly multi-step transmission delays. Simulation results demonstrate the effectiveness of the proposed method.

关键词： Networked control systems Communication networks H infinity control Linear feedback control systems Quantization Robust control Delay systems Stochastic processes

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Shrinkage Common Spatial Pattern for Feature Extraction in Brain-Computer Interface

Shrinkage Common Spatial Pattern for Feature Extraction in B...

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2013年中国智能自动化学术会议

作者： Yu Zhang Jing Jin Bei Wang Xingyu Wang Key Laboratory for Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

Common spatial pattern(CSP) has been one of the most popular methods for EEG feature extraction in brain-computer interface(BCI) application. Although the CSP usually provides good discriminant features for classification,it is also known to be sensitive to overfitting and *** study introduces a shrinkage technique to regularize estimation of the covariance matrices in the CSP and hence a novel shrinkage CSP(SCSP) method,which could effectively alleviate the effects of small training sample size and unbalanced data on classification. The proposed SCSP is validated on feature extraction of P300 that has been widely adopted for BCI *** accuracies are evaluated by using linear discriminant analysis(LDA) with experimental EEG data from seven *** results indicate that the proposed SCSP extracts more effective features that yield higher classification accuracy than that by the traditional CSP.

关键词： Brain-computer interface(BCI) Common spatial pattern(CSP) Electroencephalogram(EEG) P300 Shrinkage

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Modeling and optimization for the Molecular Weight Distribution of Polyethylene based on the Multi-output Support Vector Machine

Modeling and Optimization for the Molecular Weight Distribut...

引用

第25届中国过程控制会议

作者： Ye Zhencheng Li Yun Luo Na Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

the Molecular weight distribution(MWD)is an important quality index for the polymer material,but detecting the MWD in real-time is still difficult by *** rapid real-time detection method for MWD with good accuracy is the current hotspot in the polymer *** from the method other literatures have described,in this work the reaction mechanism and the industrial information will be merged to build a hybrid model for the MWD to solve the prediction accuracy and real-time problems,using the weighted superposition of the distribution function on each active center of catalyst to fit the MWD and applying the multi-output support vector machine regression(MSVR)algorithm to describe the relationship between process conditions and the parameters of distribution *** the unconstrained nonlinear optimization method has been used to optimize the process conditions based on the hybrid ***,the application of the above-mentioned approach in the ethylene polymerization process has verified the feasibility.

关键词： polyethylene,molecular weight distribution,modeling,optimization,multi-output support vector machine

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GTAW Procedure Expert System Based on Neural Network

GTAW Procedure Expert System Based on Neural Network

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2013 3rd International Conference on Mechanical Materials and Manufacturing Engineering(ICMMME 2013)

作者： Xuewu Wang Shangyong Yang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Intelligent procedure expert system was developed to select appropriate GTAW procedure in this ***,the function design and implementation methods of the welding procedure expert system were *** expert system can present the welding procedure card,multimedia display of welding process,and output function to makes the data sharing more ***,the database design of the welding procedure expert system based on C/S mode was presented where the expert knowledge was *** last,the neural network model was established to realize procedure selection based on the neural network learning ability and the welding case from the *** the BPNN model,the welding parameters can be obtained based on the input welding conditions.

关键词： Welding procedure Expert system Database Multimedia Neural network

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Real Time optimization of the Gasoline Blending Process with Unscented Kalman Filter

Real Time Optimization of the Gasoline Blending Process with...

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2011 International Conference on Internet Computing and Information Services （ICICIS 2011）

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

Gasoline blending is a critical process in petroleum refineries. Real-time optimization(RTO) techniques have been popular with the applications for the blending process for optimization purpose. However the dependency of RTO on the measurement of the component impairs its applicability. Therefore how to utilize the blending model and the product measurement to free RTO from the component measurement is the major research topic in this paper. Unscented Kalman Filter, due to its ability to estimate the parameter for nonlinear model, is chosen to estimate component properties based on the product measurement. The RTO strategy is then proposed with the UKF method for the recipe calculation periodically. Furthermore, the proposed RTO is tested with the gasoline blending benchmark problem, while the results are compared with the ideal blending case. The accuracy of the component estimation and the efficiency of the RTO are verified with the results.

关键词： Gasoline Blending Unscented Kalman Filter Real-Time optimization(RTO)

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Weighted Semi-supervised Orthogonal Factor Analysis Model for Quality-Related Process Monitoring

Weighted Semi-supervised Orthogonal Factor Analysis Model fo...

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第37届中国控制会议

作者： Xiaohui Cui Jian Yang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Probabilistic model has already been widely used for process monitoring. However, the obtained factors may contain quality-unrelated information, which is harmful to the quality-related process monitoring. Meanwhile, considering the situation of unequal sample rates of process and quality variables, a semi-supervised orthogonal factor analysis(Semi-SOFA) model is presented, further, to improve robustness, Semi-SOFA is extended to weighted form(WSemi-SOFA). This paper performs orthogonal decomposition on the obtained factors, which divides them into two parts: quality-related one and quality-unrelated one. Based on it, the corresponding T statistics are designed to offer quality-related process monitoring, respectively. Besides, SPE statistics are constructed as supplement to monitor residuals. For effectiveness demonstration of the proposed method, TE benchmark is utilized.

关键词： orthogonal decomposition of factor weighted semi-supervised quality-related process monitoring

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Non-Gaussian dynamic batch process monitoring based on ICA-JITL-MSSVDD

Non-Gaussian dynamic batch process monitoring based on ICA-J...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Zhaomin Lv Xuefeng Yan Qingchao Jiang .Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

To solve the non-Gaussian and time-varying dynamics problems of batch process,a new monitoring method called Independent component analysis and Just-in-time learning multiple subspace support vector data description(ICA-JITL-MSSVDD) is *** offline,a new multiple variable subspaces division method used for batch process based on Independent component analysis(ICA) is *** proposed multiple subspace division method is used as a global non-Gaussian modeling *** online,Just-in-time learning multiple(JITL) is employed to extract similar samples,and multiple subspace support vector data description(MSSVDD) models are *** method can solve the time-varying dynamics problem and MSSVDD is used as a local non-Gaussian modeling *** the integration of global and local non-Gaussian modeling methods,the non-Gaussian and time-varying dynamics problems of batch process can be *** fault detection effectiveness of ICA-JITL-MSSVDD can be demonstrated by simulating the fed-batch penicillin fermentation process.

关键词： Batch process monitoring Independent component analysis Just in time learning Multiple subspace Support vector data description

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Joint Probability Density and Weighted Probabilistic PCA Based on Coefficient of Variation for Multimode Process Monitoring

Joint Probability Density and Weighted Probabilistic PCA Bas...

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2016 International Conference on Artificial Intelligence: Techniques and Applications(AITA 2016)

作者： Tian-xian ZHU Jian HUANG Xue-feng YAN Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

For probabilistic monitoring of multimode processes,this paper introduced a monitoring scheme that integrates joint probability density and weighted probabilistic principal component analysis based on coefficient of variation(CV-WPPCA). A joint probability based on 2 statistic was constructed for mode identification. After it concentrated maximum fault-relevant information into dominant subspace by identifying and extracting important noise factors from the residual subspace,the new approach utilized a weighting strategy based on coefficient of variation method to highlight the useful information in the reconstructed dominant subspace. A case study on the Tennessee Eastman process was applied to demonstrate the efficiency of the proposed method.

关键词： Multimode process monitoring Joint probability Weighted probabilistic PCA Coefficient of variation

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A novel fault diagnosis approach using hidden Markov model for a multimode process

A novel fault diagnosis approach using hidden Markov model f...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Fan Wang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

chemical processes with multiple operational modes are common in modern process *** fault diagnosis methods meet challenges when facing multimodal complex data *** order to tackle this challenge,a new fault diagnosis approach utilizing hidden Markov model(HMM) is developed in this paper.A HMM trained by normal process data with multimodal feature has several hidden *** operating mode is represented by a hidden state which should be described via a finite Gaussian mixture *** multivariate Gaussian mixture distribution targets complex data distribution within each individual ***,for a monitored sample,the Mahalanobis distance(MD) inside each Gaussian component is constructed as monitoring *** contribution index is obtained through the differential decomposition along every variable direction to MD-based monitoring *** last,the hidden state Gaussian component probability strategy is used to integrate local monitoring index and local variable contribution index across all Gaussian components into a global monitoring indicator and a global variable contribution *** derived variable contribution index with probabilistic feature is employed to locate faulty *** to a numerical simulation and the Tennessee Eastman(TE) process display the fault detection and diagnosis performance of our proposed method.

关键词： Hidden Markov model Fault diagnosis Multimode process Fault detection

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