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Chinese control Conference (CCC)

作者： Xianwei Li Yang Tang Shaoyuan Li Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

This paper focuses on designing dynamic event-triggered (DET) protocols for consensus control of general linear multi-agent systems (MASs). Design algorithms are constructed for DET protocols with static and adaptive coupling, respectively. The designed event-triggering conditions involve internal dynamic variables that depend on the error information of the system, which provide more flexibility to reduce the number of triggered events than the existing results on linear MASs. The protocol with adaptive coupling is of the fully distributed nature. It is proved that the proposed protocols can ensure consensus and are not Zeno. A numerical example is provided to illustrate the effectiveness and advantages of the designed protocols.

关键词： Protocols Couplings Laplace equations Multi-agent systems Lyapunov methods Closed loop systems optimization

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Research on online multiple model soft-sensor

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chemical Engineering Transactions 2017年 61卷 1795-1800页

作者： Wang, Shijie Wang, Zhenlei Wang, Xin Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai200237 China Center of Electrical and Electronic Technology Shanghai Jiao Tong University Shanghai200240 China

Offline updating is a method that most of multiple model soft-sensors used to adapt the new operating conditions. Replacing online models with offline ones is bound to affect the efficiency of soft-sensors, and it costs manpower as well as time simultaneously. It takes maintenance staffs some time to re-train complete models, which requires a lot of historical data, and then the existing models will be changed with new ones. A soft-sensor that can be added or subtracted models online is proposed in this paper. Density-based spatial clustering of applications with noise (DBSCAN) is employed for clustering analysis. Compared with traditional kernel fuzzy clustering method (KFCM), DBSCAN improves the ability of filtering out noise and enhance the ability to decide whether there is a new working condition. However, the clustering results of DBSCAN are extremely sensitive to the input parameters. In this study, kernel density estimation (KDE) is applied to determine the number of subsets and a novel method is proposed to determine the parameters. The new sub-models can be directly added to the online models after trained. The results of soft-sensor achieved by a number of models according to the switching or weighted way. The method proposed in this paper is applied to the measurement of cracking depth of ethylene cracking furnace, which proves the practicability and effectiveness. Copyright © 2017, AIDIC Servizi S.r.l.

关键词： Ethylene

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Cost-quality adaptive active learning for chinese clinical named entity recognition

arXiv

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arXiv 2020年

作者： Cai, Tingting Zhou, Yangming Zheng, Hong School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education China

Clinical Named Entity Recognition (CNER) aims to automatically identity clinical terminologies in Electronic Health Records (EHRs), which is a fundamental and crucial step for clinical research. To train a high-performance model for CNER, it usually requires a large number of EHRs with high-quality labels. However, labeling EHRs, especially Chinese EHRs, is time-consuming and expensive. One effective solution to this is active learning, where a model asks labelers to annotate data which the model is uncertain of. Conventional active learning assumes a single labeler that always replies noiseless answers to queried labels. However, in real settings, multiple labelers provide diverse quality of annotation with varied costs and labelers with low overall annotation quality can still assign correct labels for some specific instances. In this paper, we propose a Cost-Quality Adaptive Active Learning (CQAAL) approach for CNER in Chinese EHRs, which maintains a balance between the annotation quality, labeling costs, and the informativeness of selected instances. Specifically, CQAAL selects cost-effective instance-labeler pairs to achieve better annotation quality with lower costs in an adaptive manner. Computational results on the CCKS-2017 Task 2 benchmark dataset demonstrate the superiority and effectiveness of the proposed CQAAL. Copyright © 2020, The Authors. All rights reserved.

关键词： Records management

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Adaptive strategy for time window length in SSVEP-based brain-computer interface

Adaptive strategy for time window length in SSVEP-based brai...

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International Conference on Mechatronics and control (ICMC)

作者： Yu Zhang Hehe Ma Jing Jin Xingyu Wang Key Laboratory for Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China Shanghai Electric Group Co Ltd Shanghai Shanghai CN

ISBN: (纸本)9781479925391

Most of the existed methods for steady-state visual evoked potential (SSVEP) recognition require a specified time window length (TWL) to estimate the dominant frequency components in EEG signals. Typically, the TWL is manually predetermined and then fixed during running of the SSVEP-based brain-computer interface (BCI), which may not give the optimal information transfer rate (ITR). This study proposes an adaptive strategy and integrates it into multivariate synchronization index for SSVEP recognition with the automatically determined TWL. The optimal TWL is adaptively selected according to the estimated synchronization index, which provides the relatively higher ITR for the SSVEP recognition than a fixed TWL does. Experimental results evaluated on nine healthy subjects demonstrated effectiveness of the proposed adaptive strategy for the TWL in SSVEP-based BCI.

关键词： Electroencephalography Synchronization Accuracy Brain-computer interfaces Indexes Correlation Frequency synchronization

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Initial-Parameter-Criterion based Gaussian Mixture Model Monitoring Method for Non-Gaussian Process

Initial-Parameter-Criterion based Gaussian Mixture Model Mon...

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Chinese control Conference

作者： Ying Tian Wenli Du Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai

Gaussian mixture model (GMM) monitoring method is commonly used for non-Gaussian process monitoring. In GMM monitoring method, the final number of Gaussian components and the statistical distribution parameters are estimated through the improved Figueiredo-Jain (F-J) algorithm in the modeling phase. However, the data dimensionality of modern industry is high, the number of initial Gaussian components is large for the accuracy of the algorithm. Under this condition, the relative small number of samplings makes the denominator in the weight estimation formula of the improved F-J algorithm become zero and the algorithm suspends. To make the algorithm work well in an acceptable modeling time, we deduce the Initial-Parameter-Criterion among initial Gaussian components, data dimension, sampling number and propose the Initial-Parameter-Criterion based GMM (IPC-GMM) monitoring method. The proposed monitoring method is used for TE process monitoring, simulation results reveal the effectiveness and necessity of IPC-GMM monitoring method.

关键词： Monitoring Gaussian mixture model Density functional theory Industries Data models Process control

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External Analysis and Moving Window Lo OP based process monitoring for a class of multi-mode time-varying process

External Analysis and Moving Window Lo OP based process moni...

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第26届中国过程控制会议（CPCC2015）

作者： Yong Gao Xin Wang Zhenlei Wang Liang Zhao Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology) Ministry of Education Center of Electrical & Electronic Technology Shanghai Jiao Tong University

Moving window local outlier probability(MWLo OP)is an outlier detecting method which was proposed for monitoring time-varying industrial processes;however,for the practical industrial processes,besides the time-varying characters caused by deactivation of catalyst,measuring instrument drifting and so on,the operation mode is often switched as the adjusting of the feedstock,changes in market demands and so *** the WMLo OP algorithm can deal with the time-varying process data,the multi-mode process data will lead to a mass of fault *** solve this problem,an external analysis moving window local outlier probability(EA-MWLo OP)algorithm is proposed in this *** external analysis is employed to eliminate the influence of operation mode change on the process data,then the MWLo OP method can deal with complex distribution time-varying data,and give an outlier ***,the corresponding statistic and control limit are constructed to detect the process *** addition,while the monitoring model updated,the control limit is not necessary to *** performance of this method is evaluated through a case study of a non-isothermal continuous stirred tank reactor(CSTR).

关键词： multi-mode time-varying extern analysis non-Gaussian MWLo OP

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A valve stiction and time delay control method based on fuzzy smith internal model principle

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chemical Engineering Transactions 2017年 61卷 577-582页

作者： Zhang, Hao Wang, Xin Wang, Zhenlei Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai200237 China Center of Electrical and Electronic Technology Shanghai Jiao Tong University Shanghai200240 China

In the process of chemical production, the phenomenon of control valve's stiction is very common. This phenomenon causes control loop oscillation and has a negative effect on the quality and cost of the product. Time delay exists widely in industrial process, which leads to the variation of the dynamic response of the system. In this paper, taking the stiction model proposed by Kano as an example, then puts forward a method of smith internal model control based on fuzzy theory. This paper try to solve the stiction problem by controller designed. This control method can realize the parameter self-tuning and overcome the stiction. Simulation results show that the method proposed can effectively overcome the loop oscillation and improve the performance of the system. Copyright © 2017, AIDIC Servizi S.r.l.

关键词： Stiction

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An improved UKPK-PSO algorithm inspired from block chain technology for flexible job shop scheduling problem

An improved UKPK-PSO algorithm inspired from block chain tec...

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第三十八届中国控制会议

作者： Lin Yao Yabin Liu Houbao Zhao Haojie Ding Shanghai Polytechnic University Shanghai Lixin University of Accounting and Finance Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

The paper proposes a new encoding method inspired from the block chain technology, by which an operation sequence information and the corresponding machine information is integrated in an operation node and links all operation nodes to form an operation list with pointer technology of C++ program language. Accordingly, the paper designs an individual unit ID for every possible cases. Subsequently, it describes the process of searching the optimal solution of FJSP by implementing an improved UKPK-PSO algorithm, which hybridizes a varied PSO algorithm by getting the best information from K informants and the method of keeping the elitists for next generation in iterations. With the improved algorithm we can more efficiently traverse the solution space. After obtained some better solutions with the improved PSO, we continuously search the optimal solutions by tentatively changing the chosen machines of the dominant operations. Finally, it outputs the optimal solution through some limited iterations again. By analyzing the experiment results for the instances, the paper furtherly proves that the whole algorithm is effective for FJSP.

关键词： Flexible job shop scheduling problems Particle Swarm Optimal Algorithm Block chain Technology Operations Research

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The Evolution of Cooperation in Multiplayer Snowdrift Games with Environmental Feedback

The Evolution of Cooperation in Multiplayer Snowdrift Games ...

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第43届中国控制会议

作者： Tao Sun Haili Liang Xiaotong Yu Zhao Zhou Yang Xu Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and AutomationShanghai University Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Human societies and natural environments form a complex ecological system,in which human activities can change the ecological environment,the changes in the ecological environment can influence human social activities in *** paper introduces a punitive mechanism as environmental feedback into co-evolutionary systems,and proposes a replicator dynamic model which aims to explain the evolutionary strategies adopted by populations for the multiplayer snowdrift *** obtain the necessary and sufficient condition for the existence and stability of the internal equilibrium point by studying on the Jacobian matrix,and the effectiveness of the theoretical results is examined by numerical *** identify that the threshold parameter in multiplayer snowdrift games determines the evolution of the closed-loop ***,the system exhibits limit cycles when the threshold is small *** the threshold increases,the system converges to a stable internal equilibrium ***,if the threshold reaches a sufficiently large value,the proportion of cooperators in the population is high,while the group of punishers becomes extinct,indicating the loss of environmental feedback.

关键词： Evolutionary Games Multiplayer Snowdrift Games Environmental Feedback Stability Analysis

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Data-driven online operating performance assessment for multi-datasets multivariable industrial processes

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chemical Engineering Transactions 2017年 61卷 1729-1734页

作者： Du, Yupeng Wang, Zhenlei Wang, Xin Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai200237 China Center of Electrical and Electronic Technology Shanghai Jiao Tong University Shanghai200240 China

In this study, a novel online operating performance assessment method based on multi-sets two-step basis vector extraction artificial neural networks (MTBVE-ANN) strategy is proposed for industrial applications. The MTBVE-ANN method focuses on finding common and specific information involved in multi-datasets, which improves the accuracy of data nonlinear characterization with artificial neural networks introduced. The optimality related variations are extracted from each operating performance grade by analysing the common and unique variations over online steady performance grades. The online operating performance assessment method is performed based on the similarities between the optimality related variations of the test data and that of historical training data. Previously, total projection to latent structures (T-PLS) operating performance assessment method must be performed based on the availability of both input and output data. The proposed method in this paper takes the artificial neural network to assess the operating performance grade of the online test data without output. The validity and precision of the proposed operating performance assessment method is illustrated with the industrial data of multi-datasets multivariable industrial processes. Copyright © 2017, AIDIC Servizi S.r.l.

关键词： Neural networks

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