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第30届中国控制与决策会议

作者： Bo Zhao Bing Song Shuai Tan Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Specific index-related process monitoring covers a wide range of requirements from industrial production. At present, it is still a challenge to divide into the specific index-related information and the specific index-unrelated information more accurately. In this paper, a two-step information extraction method is presented to solve this problem. In the first step, the overall process variables are separated into a strongly related variable set and a weakly related variable set. In the second step, the two sets are further divided into a specific index-related subset and a specific index-unrelated subset, respectively. Two specific index-related subsets form a new subspace related to the specific index. Two specific index-unrelated subsets are combined into a specific index-unrelated subspace. Two 2 T statistics are built in the specific index-related subspace and the specific index-unrelated subspace, respectively. Finally, the proposed method is applied for Tennessee Eastman(TE). The results indicate the effectiveness of the proposed method.

关键词： specific index process monitoring subspace information extraction

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Soft sensor based on recursive kernel partial least squares for 4-carboxybenzaldehyde of an industrial terephthalic acid hydropurification process

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chemical Engineering Transactions 2017年 61卷 463-468页

作者： Li, Zhi Zhong, Weimin Peng, Xin Du, Wenli Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Terephthalic acid is a raw material for polyester and textile industry. However, the by-product 4-Carboxybenzaldehyde in TA is harmful to the polymer process since it can lower the polymerization rate and the average molecular weight. Thus, a hydropurification process is built to decrease 4-CBA. In this process, 4-CBA in TA is purified by hydrogen in water at 270 °C-290 °C under 7.9 MPa pressure over 0.5 wt. % carboncoated palladium catalyst in a fixed-bed reactor. The activity of the catalyst will gradually decrease with the process running. The most important quality index for this process is the content of 4-CBA in the product. However, in real plant, the content of 4-CBA is analysed every two hours in a laboratory. It is a very large delay for control system so the operation conditions of the process could not be adjusted in time. These may results in a maximum two hours product failure. In this paper, a first principle model of the process is developed based on Aspen Plus. The accuracy of the model is verified by the model results and the actual plant results. Based on this model, a series of sensitive analysis are performed. Six variables include 4-CBA content in TA, react temperature, react pressure, hydrogen flow rate, catalyst activity and Slurry concentration are the main factors influencing the 4-CBA content in the product. Though the aspen model is accurate, these parameters in aspen model are not easy to adjust quickly. In order to predict 4-CBA content quickly, a soft sensor for 4-CBA is developed using recursive kernel partial least squares considering the nonlinearity and slow time-varying of the process. Results show that the prediction accuracy of this method is very high and it is easy for engineers to handle. Copyright © 2017, AIDIC Servizi S.r.l.

关键词： chemical reactors

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Modeling Method of Data-driven Model Combined with A Priori Knowledge and Its Application in Average Particle Size Estimation of Composite Colloidal Sols

Modeling Method of Data-driven Model Combined with A Priori ...

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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： Yang Zhou Wenxi Xu Shaojun Li Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and TechnologyMinistry of Education

We present a universally applicable hybrid modeling method for nonlinear industrial processes that combine the a priori process knowledge with a data-driven *** method constructs a unified framework for the modeling process by integrating a data-driven modeling technique,sampling detection technique,constraint optimization problem,and an evolutionary *** the modeling process,a swarm intelligence algorithm is used to optimize the model parameters under the circumstances of satisfying the constraints of a priori *** adding the constraints of process a priori knowledge,we can obtain more information about the actual process and avoid the over-fitting problem to some extent,especially when modeling a system with a small quantity of *** order to show the effectiveness of the method proposed in this paper,two general data-driven models,the polynomial regression model and radial basis function network model,are used as case ***,a function simulation experiment is designed to test effectiveness,and applied to estimate average particle size of ZrO2-TiO2 composite colloidal sols.

关键词： Data-driven modeling A priori knowledge Constraint handling Intelligence algorithm Hybrid model

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Semi-Global Containment control of Linear Agents with Actuator Position and Rate Saturation:The State Feedback Case

Semi-Global Containment Control of Linear Agents with Actuat...

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第37届中国控制会议

作者： Zhiyun Zhao Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes （East China University of Science and Technology） Ministry of Education

This paper studies semi-global containment control problem for a multi-agent system. Each follower agent in the system is described by a general linear system in the presence of both actuator position and rate saturation. A linear state feedback containment control law is constructed for each follower agent by using low gain approach such that the states of all follower agents will converge to the convex hull formed by the leader agents asymptotically when the communication topology among follower agents is a connected undirect graph and each leader agent is a neighbor of at least one follower agent. Simulation results illustrate the theoretical results.

关键词： Containment control multi-agent systems actuator saturation rate saturation

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Semi-Global Output Consensus of Multi-Agent Systems Subject to Actuator Saturation: A Low and High Gain Approach

Semi-Global Output Consensus of Multi-Agent Systems Subject ...

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第三十八届中国控制会议

作者： Zhiyun Zhao Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology) Ministry of Education

This paper studies semi-global output consensus problem for multi-agent systems. The dynamics of each follower agent is described by a linear system subject to both actuator saturation and external disturbances. The dynamics of leader agent is also described by a linear system which generate both the tracking signal and disturbance as the exosystem does in output regulation problem for individual system. Solvability conditions are established based on the agent dynamics and the communication topology. For the follower agents, low-and-high gain based linear state feedback control laws are constructed such that all the outputs of agents achieve output consensus when the communication topology among the follower agents is a connected undirected graph, and the leader is a neighbor of at least one follower. Simulation results are given to illustrate the theoretical results.

关键词： Output consensus low-and-high gain feedback multi-agent systems actuator saturation

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Research on zero-wait scheduling problems in multiproduct processes with due dates

Communications in Computer and Information Science

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Communications in Computer and Information Science 2013年 355卷 322-332页

作者： Xu, Zhenhao 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

Production scheduling is an important aspect of batch process operations to achieve high productivity and operability. In this paper, we considered a zero-wait multiproduct scheduling with due dates under uncertainty, where the total weighted earliness/tardiness penalty is to be minimized. The imprecise processing time is expressed with the triangle fuzzy variable and the model has been established based on fuzzy cut-set theory. A new improved shuffled frog-leaping algorithm (ISFLA) is introduced to search optimal objective for the given problem, which has a new updating rule in memeplexes. In order to enhance the ability to search the global optimum, the strategy of Forced Moving of the worst frog in each sub-memeplex is proposed to increase the diversity. Simulated results demonstrate that ISFLA has outperformed the conventional SFLA, which is effective and robust in solving the zero-wait scheduling with due-date window. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Scheduling

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Comparison of CNN-Uni-LSTM and CNN-Bi-LSTM based on single-channel EEG for sleep staging

Comparison of CNN-Uni-LSTM and CNN-Bi-LSTM based on single-c...

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International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Qianyu Li Bei Wang Jing Jin Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

Sleep staging is an effective method for diagnosing sleep disorder and monitoring sleep quality. With the rapid development of machine learning technology, the automatic staging methods of sleep gradually replace the traditional manual interpretation which can improve the efficiency on sleep staging for medical research. LSTM networks can save the historical information as a reference for the current moment, which is undoubtedly a good way to improve sleep staging performance. In this paper, a convolutional neural network (CNN) is constructed to extract the features from a single-channel EEG. The Uni-directional Long Short-Term Memory (Uni-LSTM) network and Bi-directional Long Short-Term Memory (Bi-LSTM) network are combined with CNN to realize automatic sleep staging. The obtained results showed that the two presented network frameworks are effective and feasible on sleep staging. The Bi-LSTM which has more enriched sequence information got better classification performance than the Uni-LSTM.

关键词： Sleep Manuals Machine learning Feature extraction Electroencephalography Monitoring Medical diagnostic imaging

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

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

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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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