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

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Automatic Sleep Stage Classification by a Density - Distance- Based K - means Clustering Algorithm with Amendments 12

Automatic Sleep Stage Classification by a Density - Distance...

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12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Yu, Ying Wang, Bei Jin, Jing 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)9781728148526

Sleep staging has important significance for the monitoring, prevention and treatment of sleep disorders. In this study, the clustering algorithm of K-means is investigated in order to realize the automatic sleep stage classification. Unlike the traditional K-means clustering algorithm, density-distance-based processing procedures were presented to determine the cluster centers. Additionally, the clustering results were amended based on the regularity of sleep stages during one's overnight sleep process. The presented algorithm was tested and analyzed in detail with six subjects from the Sleep-EDF database. The averaged accuracy is about 74%. Comparing with the traditional K-means clustering algorithm, the developed algorithm achieved more accurate and reasonable sleep staging results which can be an assistant tool for clinical inspection. © 2019 IEEE.

关键词： Sleep research

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A hybrid differential evolution algorithm integrated with particle swarm optimization 3

A hybrid differential evolution algorithm integrated with pa...

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Fan, Qinqin Yan, Xuefeng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

In order to implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization (PSODE) is proposed. In PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. Differential evolution operators are applied to evolve the original population. And, PSO is applied to co-evolve the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the real-time optimum control parameters are obtained. To illustrate the performance of PSODE, DE/rand/1, DE/best/1, DE/rand-to-best/1, DE/rand/2, DE/best/2, self-adaptive Pareto DE (SPDE), self-adaptive DE (SDE) and PSODE are applied to optimize 9 benchmark functions. The results show that the average performance of PSODE is the best.

关键词： Particle swarm optimization (PSO)

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Graph-based semisupervised learning for icing fault detection of wind turbine blade

Graph-based semisupervised learning for icing fault detectio...

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作者： Yi, Huaikuan Jiang, Qinchao Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

The detection of blade icing faults in wind farms is an important task in improving the reliability and safety of wind power systems. Detection is primarily achieved through supervised learning, using labeled samples. However, labeling a large volume of data often consumes lots of manpower, which means that semisupervised learning is usually required. This study proposes a graph-based semisupervised learning system to achieve efficient fault detection in wind turbines (WTs). The proposed method detects abnormal samples by optimizing a small amount of labeled and a large amount of unlabeled data. Firstly, feature selection based on mutual information is performed on supervisory control and data acquisition data, so as to eliminate redundant variables. Secondly, a graph is constructed, using the limited labeled data to indicate the connections between samples. Finally, the initial class information is propagated to the entire data set by iteratively updating the input of the graph model. Experimental results based on a selection of benchmark data sets and real WT data demonstrate the feasibility of the proposed method. © 2020 IOP Publishing Ltd.

关键词： Wind turbines

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An improved particle swarm optimization algorithm based on two kinds of chaotic maps 3

An improved particle swarm optimization algorithm based on t...

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Li, MingHao Wang, XueWu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Particle swarm optimization algorithm tends to fall into local optimum sometimes. To resolve this problem, an improved particle swarm optimization algorithm based on two kinds of different chaotic maps is proposed. The algorithm produces primitive chaotic particle swarm using the uniform distribution of Tent map and improves the diversity of search. When the particle swarm evolves to a local optimum, the chaotic mutation operator produced by Logistic map is adopted to form a disturbance on the swarm to drive particle swarm jump out of local optimum and approach the global optimum. Meanwhile, an adaptive inertia weight factor is introduced to adjust particles inertia weight factor adaptively, which forms a new 2-chaotic maps embedded adaptive particle swarm optimization algorithm (2-CMEAPSO) that can fully utilize the randomness and ergodicity of the chaotic motion to enhance optimization capability. Experimental results show that the improved algorithm can efficiently overcome the premature of standard particle swarm optimization algorithm. Besides, it has stronger global optimization ability and higher accuracy than the basic particle swarm optimization algorithm.

关键词： Global optimization

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control Performance Assessment of Multivariable System Based on Multi-time-variant-disturbances Mixing GMV Method 29

Control Performance Assessment of Multivariable System Based...

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第29届中国控制与决策会议

作者： Yupeng Du Zhenlei Wang Xin Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Center of Electrical & Electronic Technology Shanghai Jiao Tong University

ISBN: (纸本)9781509046584

The disturbance in chemical process is complex and has the multiple characteristics,and the control performance assessment of multivariable system with multiple disturbances is one of the hot *** this paper,the control performance assessment method of multivariable systems,based on multi-time-variant-disturbances mixing generalized minimum variance(MMGMV),is ***,the generalized minimum variance control is introduced into the multivariable system performance assessment,and the weight matrix is designed according to the time-varying control ***,the multivariable MMGMV controller is designed combining with the idea of multi-model weights mixing for all multi-time-varying ***,the output variance of each controlled variable is obtained using MMGMV *** average variance of controlled variable in the MMGMV controller acts as the criterion of performance assessment,and combining with the output variance of actual controller for the controller performance *** with the minimum variance benchmark,the developed method is more reasonable and practical for the control performance assessment of multivariable *** developed approach is demonstrated by a numerical simulation and a heavy oil fractionation of process control system.

关键词： Multivariable Multi-time-variant-disturbance-mixing Generalized minimum variance Time-varying weight matrix Mixing weight

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Fault diagnosis of CTA hydrogenation process based on KPCA 3

Fault diagnosis of CTA hydrogenation process based on KPCA

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Li, Bo Li, Zhi Luo, Na Zhong, Weimin East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai200237 China

As the large amounts of operate data collected from Distributed control System (DCS) often contain outliers and these data are more complexity and nonlinearity. They can't be used directly to model, optimization and fault diagnosis. In fault diagnosis, the existence of outliers can destroy the covariance structure of Kernel Principal Component Analysis (KPCA), which cause the model can't really reflect the actual normal condition. In this paper, KPCA method is adopted to establish the normal statistic monitor model from the historical data which can represent the normal industrial operate condition. First, the outlier detection algorithm is used to eliminate outliers among normal work condition. Then the primary statistic model for fault diagnosis of the Squared Prediction Error (SPE) and T2 are established according to the data exclude outliers. The effectiveness of this fault diagnosis is demonstrated by the operate data of industrial Crude Terephthalic Acid (CTA) hydrogenation process, and simulation results show that this method can identify the industrial failure condition.

关键词： Failure analysis

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Research on electromagnetic coupling artificial neural network with spatial topology

Research on electromagnetic coupling artificial neural netwo...

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6th International Conference on Brain Inspired Cognitive Systems, BICS 2013

作者： Wang, Ziyin Liu, Mandan Ren, Xiang Cheng, Yicheng Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai 200237 China

ISBN: (纸本)9783642387852

In this paper, an emerging artificial neural network (ECANN) is proposed. Abstracting from a latest research in neuroscience, electromagnetic coupling among neuron activities is introduced into the model. Besides, the overall network can be viewed as a system with physical significance of circuitry, and each neuron is presented as differential equation. At the mean time, the spatial grid topology is employed in order to develop its parallelism. This artificial neural network is designed for fitting and predicting dynamic data, and has successfully worked in simulation part of this paper. © 2013 Springer-Verlag.

关键词： Neural networks

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A novel approach for moving object detection based on improved particle swarm optimization algorithm

A novel approach for moving object detection based on improv...

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2010 8th IEEE International Conference on control and Automation, ICCA 2010

作者： Yu, Jin Zhou, Xuan Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

ISBN: (纸本)9781424451951

In order to extract from the video sequence in a complete and consistent moving target, a novel algorithm for video object segmentation based on improved particle swarm optimization (IPSO) is presented. The algorithm fuses brightness segmentation and color information at 'region level', as to make up for conventional 'pixel level' approaches. The IPSO is taken into account for spatial segmentation of the video frame, which combines the mixture Gaussian model of temporal framework in achieving better segmentation. Adapting to the real-time video surveillance, the proposed algorithm can speed up the process of image segmentation, and make background modeling accurately to update. Comparisons were performed with other method that the proposed algorithm can detect intact moving objects even when objects appear and disappear suddenly. The experiment across different types of video shows the efficiency and stability of video object segmentation by the novel approach. © 2010 IEEE.

关键词： Image segmentation

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A class of nonlinear discrete-time system adaptive control based on second level multiple models 36

A class of nonlinear discrete-time system adaptive control b...

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第36届中国控制会议

作者： Yong Chen Xin Wang Zhenlei Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Center of Electrical and Electronic Technology Shanghai Jiao Tong University

ISBN: (纸本)9781538629185

Using multiple models to deal with transients for adaptive systems which is large parametric uncertainties was introduced in the 1990 *** common method is using switching ***,these methods has some potential *** this paper,a new multiple models scheme is proposed for a class strict-feedback nonlinear discrete-time ***,we use feedback linearization and backstepping method to develop adaptive indirect schemes with an observer-based identifier to ***,we design a controller based on the convex hull of parameter *** the number of necessary models is less than switching scheme,and can use the information of all identification ***,simulation have demonstrated that this method is effectively to a class of strict-feedback nonlinear discrete-time system.

关键词： convex hull multiple models backstepping nonlinear system strict-feedback

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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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