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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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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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Octane model based on principal component analysis and neural network by near-infrared spectroscopy and its application in online gasoline blending process 3

Octane model based on principal component analysis and neura...

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

作者： He, Kaixun Cheng, Hui Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

For the gasoline pipeline blending process, recipe optimization system is greatly dependent on the near-infrared spectroscopy online analyzer, whose spectral model plays an important role in the measurement. The sepectral model's accuracy and adaptability directly affect the applicability of the entire online blending system. This paper studies how to establish model for gasoline octane number for the gasoline pipeline blending process with near-infrared spectroscopy online analyzer. It is proposed using principal component analysis (PCA) together with Artificial Neural Network (ANN) method to establish spectral-model for octane number. Multivariate linear regressions(MLR) and partial least squares (PLS) method have also been used to establish gasoline octane model for comparison purpose. The results show that the model established by PCA and ANN has strong anti-jamming capability and suitable for gasoline online blending application.

关键词： Principal component analysis

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Event-triggered distributed predictive control for constrained large-scale linear systems 35

Event-triggered distributed predictive control for constrain...

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第35届中国控制会议

作者： SU Xu ZOU Yuanyuan NIU Yugang JIA Tinggang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Automation Division Shanghai Electric Group Co.Ltd

ISBN: (纸本)9781509009107

This paper investigates the problem of event-triggered dual-mode distributed predictive control(DPC) for constrained large-scale linear systems subject to bounded *** on input-to-state stability(ISS) theory,the event-triggering condition involving information of the subsystem itself is derived.A dual-mode predictive control scheme is designed to reduce information exchanges with neighboring *** upper bound of disturbances for ensuring the recursive feasibility and closed-loop stability are ***,a simulation example is given to show that the presented method is able to save computation resources and communication resources while guaranteeing the desired control performance.

关键词： Large-Scale Systems Distributed Predictive control(DPC) Event-Triggered

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A new co-evolutionary immune algorithm for flow shop with zero wait

A new co-evolutionary immune algorithm for flow shop with ze...

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5th FTRA International Conference on Computer Science and its Applications, CSA 2013

作者： Xu, Zhenhao Zhang, Shun Gu, Xingsheng 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)9783642416736

The flow shop scheduling problems with zero wait is considered as one of the most challenging problems in the field of scheduling. This paper deals with the problem considering the makespan minimization as the objective. In order to solve the corresponding model, a new co-evolutionary immune algorithm (NCIA) is proposed based on the '80/20' rule and a designed local search strategy. Moreover, a global crossover operator is presented to preserve excellent genes fragment and increase the diversity of population during the process of evolution. to confirm the performance of the proposed algorithm, in terms of solution quality, the algorithm is applied to various test instances of benchmark problems available in the literature. The computational results show that the proposed algorithm outperforms other algorithms. © 2014 Springer-Verlag Berlin Heidelberg.

关键词： Scheduling

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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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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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Statistical Monitoring of chemical processes Based on Sensitive Kernel Principal Components

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Chinese Journal of chemical Engineering 2013年第6期21卷 633-643页

作者： JIANG Qingchao YAN Xuefeng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education EastChina University of Science and Technology Shanghai 200237 China

The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.

关键词： statistical process monitoring kernel principal component analysis sensitive kernel principal compo-nent Tennessee Eastman process

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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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Second Level Adaptive control Based on Combination Multiple Model 29

Second Level Adaptive Control Based on Combination Multiple ...

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第29届中国控制与决策会议

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)9781509046584

Since all of adaptive models converge to the same point in parameter space,classical second level multiple model adaptive control method cannot deal with linear time-varying *** order to deal with this drawback,this paper proposes a modified second level adaptive control method based on combining the advantages of adaptive model and fixed model for a class linear discrete time-varying plant where only a little prior information which is ***,multiple adaptive models are selected in the space of parameters,and the parameters are identifying and the controller is designed based on second level adaptive ***,when one of sub-model of the adaptive models are closed to the real parameters of the system,all of these adaptive models are fixed,and then the controller is designed based on multiple fixed ***,simulations have demonstrated that this method is effectively to a class of linear time-varying system.

关键词： adaptive model fixed model second level adaptive control multi-model

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