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

共 633 条记录，以下是291-300 订阅

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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...

引用

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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Activated sludge model parameters calibration based on multi-objective PSO

Activated sludge model parameters calibration based on multi...

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2014 International Conference on Environmental Engineering and Computer Application (ICEECA 2014)

作者： R.R.Wang L.L.Cao M.D.Liu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

The model of an Orbal oxidation ditch activated sludge process was set up based on ASM3 and Takacs' s double index settlement rate of secondary sedimentation tank model in this paper. According to the condition of the multi-objective output simulation model, multi-objective particle swarm optimization algorithm(MOPSO) is used to calibrate part of the high sensitivity parameters in the activated sludge process model. Simulation results show that the effectiveness of MOPSO for parameter calibration is obvious, which can further improve the accuracy of the model.

关键词： ASM MOPSO Activated sludge model parameters calibration based on multi-objective PSO

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

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.

关键词： permutation flow shop SFLA new leaping rule

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Multimode Operating Performance Visualization and Nonoptimal Cause Identification

Multimode Operating Performance Visualization and Nonoptimal...

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第30届中国过程控制会议（CPCC 2019）

作者： Yuhui Ying Zhi Li Mingle Yang Wenli Du Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

In the traditional performance assessment method, different modes of data are classified mainly by expert ***, human interference is highly *** traditional method is also incapable of distinguishing transition data from steady-state data, which reduces the accuracy of the monitor *** solve these problems, this paper proposes a method of multimode operating performance visualization and nonoptimal cause ***, multimode data identification is realized by subtractive clustering algorithm, which can reduce human influence and eliminate transition ***, the multispace principal component analysis is used to characterize the independent characteristics of different datasets, which enhances the robustness of the model with respect to the performance of independent ***, a self-organizing map is used to train these characteristics and map them into a twodimensional plane, by which the visualization of the process monitor is *** the online assessment, the operating performance of the current process is evaluated according to the projection position of the data on the visual ***, the cause of the nonoptimal performance is ***, the Tennessee Eastman process is used to verify the effectiveness of the proposed method.

关键词： Multimode process Performance assessment Subtractive clustering Multi-space principal component analysis Self-organizing map

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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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Distributed Energy Seheduling of Islanded Microgrid Based on Potential Game

Distributed Energy Seheduling of Islanded Microgrid Based on...

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2020 Chinese Automation Congress, CAC 2020

作者： Chen, Lei Niu, Yugang Jia, Tinggang East China University of Science and Technology The Key Laboratory of Advanced Control and Optimization for Chemical Process Shanghai China Shanghai Electric Automation Group Shanghai200023 China

ISBN: (纸本)9781728176871

Microgrid is an advanced application of distributed renewable energy utilization. With the development of energy Internet technology, microgrid is characterized by strong decentralization and high intelligence. The operation optimization of microgrid requires higher individual autonomy and rational ability, and the traditional centralized optimization is difficult to meet this development trend. In this paper, in order to better reflect the high autonomy of microgrid energy management, a distributed optimization based on potential game is proposed by comprehensively considering multiple independent individuals in the microgrid. Under this strategy, each individual makes decisions with the goal of maximizing their own profits, and completes the iterative solution of the strategy through communication with each other. Finally, a simulation example of islanded microgrid is used to verify the results, which shows that the individual autonomy and intelligence of the microgrid were improved, and the feasibility and effectiveness of the strategy are verified. © 2020 IEEE.

关键词： Energy management

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Improved Performance of Process Monitoring Based on Selection of the key Principal Components

Improved Performance of Process Monitoring Based on Selectio...

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第25届中国过程控制会议

作者： Bing Song Yuxin Ma Hongbo Shi Key laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Conventional principal component analysis（PCA）-based methods can conduct dimensionality reduction on process variables and can obtain low-dimensional representations that capture most of the variance information in the original data ***,principal components（PCs）with larger variance of normal data cannot guarantee the capture of the largest variations in fault data since the fault information is complicated and *** other words,the last PCs with smaller variance may be as important as those with larger ***,PCs selection based on variance in the PCA is subjective,which can lead to information loss and poor monitoring *** address both dimension reduction and information preservation simultaneously,this paper proposes a novel PCs selection scheme named full variable expression（FVE）.On the basis of the proposed relevance of variables with each principal component,the key principal components can be *** relevance indicates the expression degree of the original variables on each principal *** the key principal components serve as a low-dimensional representation of the entire original variables,thereby preserving the information of the original data space without undergoing information loss.A squared Mahalanobis distance,which is introduced as the monitoring statistic,is calculated directly in the key principal components space for fault *** order to test the modeling and monitoring performance of the proposed method,a numerical example and the Tennessee Eastman（TE）benchmark case studies are provided.

关键词： Principal Component Analysis Information Loss Fault Detection key Principal Components

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A study on the security of public opinion in social networks

A study on the security of public opinion in social networks

引用

International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Long Gao Wen Yang Mengling Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China

In this paper, we consider the security problem of public opinions when it has terrorists who try to guide people's opinions to malicious ones in social network. First, we propose an opinion dynamics model, which is composed by the suspects, the defenders and the masses. In this model, a set of suspects influenced by the terrorists at the beginning send malicious opinions to their neighbors. Similarly, the defenders spread the government's opinion to resist people's opinions following that of terrorists. Second, we consider the random communication among neighbours, where people can not interact with each other every time. At the last, we provide a sufficient condition to ensure the convergence of the expected opinions of the system. Finally, we provide some numerical examples to illustrate all the derived results.

关键词： Social groups Social network services Security Convergence Mathematical model Government Stability analysis

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Robust optimization for a Multi-Product Integrated Problem of Planning and Scheduling under Products Uncertainty

Robust Optimization for a Multi-Product Integrated Problem o...

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作者： Mengwen Chen Cuiwen Cao Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling (based on the work of Terrazas-Moreno & Grossmann (2011) [1]) under products prices uncertainty. With the objective of maximizing the total profit in planning time horizon, the planning section determines the amount of each product, each product distributed to each market, and the inventory level in each manufacturing site during each scheduling time period;the scheduling section determines the products sequence, start and end time of each product running in each production site during each scheduling time period. The uncertainty sets used in robust optimization model are box set, ellipsoidal set, polyhedral set, combined box and ellipsoidal set, combined box and polyhedral set, combined box, ellipsoidal and polyhedral set. The genetic algorithm is utilized to solve the robust optimization models. Case studies show that the solutions obtained from robust optimization models are better than the solutions obtained from the original integrated planning and scheduling when the prices are changed.

关键词： Uncertainty,Robust optimization,Integrated Problem of Planning and Scheduling,GA

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