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Stiction Quantification based on Time and Frequency Domain Criterions ∗

IFAC-PapersOnLine 2015年第8期48卷 635-640页

作者： Chen Li Feng Qian M.A.A. Shoukat Choudhury Wenli Du Key Laboratory of Advanced Control and Optimization Ministry of Education East China University of Science and Technology Shanghai 200237 China Department of Chemical Engineering Bangladesh University of Engineering and Technology BUET Dhaka-1000 Bangladesh

Valve stiction is one of the most common causes for poor performance in industrial control loops. Therefore, a non-invasive method which can detect and quantify stiction is urgently needed in the process industry. Most of the current stiction estimation methods use time domain criterion, e.g. Mean Square Error, to jointly identify the stiction and process model parameters. However, stiction induced oscillation is a phenomenon which has some specific characteristics in the frequency domain. Thus, extracting frequency domain information in the routine operation data will provide a more reliable and accurate stiction estimation. In this work, under the framework of Hammerstein model identification and global optimization, a new stiction quantification method based on time and frequency domain criterions is proposed. Several simulation case studies are demonstrated to validate the proposed method.

关键词： process monitoring Valve stiction Frequency domain analysis Global optimization

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Improved RCSP and AdaBoost-based classification for Motor-Imagery BCI

Improved RCSP and AdaBoost-based classification for Motor-Im...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Yangyang Miao Feiyu Yin Cili Zuo Xingyu Wang Jing Jin Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai P. R. China

ISBN: (数字)9781538683446

ISBN: (纸本)9781538683453

One of the popular feature extraction algorithms for motor imagery (MI)-based brain-computer interface (BCI) is common spatial pattern (CSP). However, CSP is also very susceptive to the selection of the filter bands, the time windows, and the channels. In this paper, we proposed a novel regularized CSP (RCSP) method to optimize feature extraction in MI-BCI. Then, a robust classifier based on AdaBoost algorithm was presented to perform the classification of MI tasks. Finally, the framework was verified on two public BCI datasets (dataset 1 from the BCI Competition IV and dataset IVa from BCI Competition III). The results suggest the proposed approach achieved superior performance compared with classical CSP and other competing methods. Overall, this method not only improved classification performance, but also reduced the data requirements of other subjects.

关键词： Feature extraction Covariance matrices Support vector machines Electroencephalography Classification algorithms Matrix decomposition optimization

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Adaptive synchronization of complex dynamical networks with neural networks nodes and unmatched parameters

Adaptive synchronization of complex dynamical networks with ...

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第28届中国控制与决策会议

作者： Yi Qu Yijun Zhang Wangli He School of Automation Nanjing University of Science and Technology The Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

ISBN: (纸本)9781467397155

This letter is on the design problem of global asymptotical adaptive synchronization for a class of complex dynamical *** the considered networks,the dynamics of each node are approximated by a class of neural networks(NNs).The parameters of the NNs in the complex dynamical networks are unmatched with the ones in the desired dynamical ***,the coefficient matrix of the adaptive controller under consideration is an arbitrary matrix instead of an identity *** adaptive nonlinear controller is *** using Lyapunov method and some properties of Kronecker product,some sufficient conditions are proposed to ensure the dynamics of the considered network globally asymptotically synchronize with the desired *** particular,the proposed criteria for network synchronization are in terms of linear matrix *** two variables are used in each criterion and the variables are not inside of any Kronecker ***,the conditions are easy to check.A numerical example is presented to show the effectiveness and applicability of the proposed approach.

关键词： Complex dynamical networks adaptive synchronization neural networks parameter unmatched

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Concurrent Modeling and Monitoring of Multi-unit Dynamic processes Based on GA-regularized Dynamic Canonical Correlation Analysis

Concurrent Modeling and Monitoring of Multi-unit Dynamic Pro...

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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： Qingchao Jiang Furong Gao Biao Huang Xuefeng Yan Department of Chemical and Biomolecular Engineering The Hong Kong University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Modern chemical processes are usually characterized by large scale,multiple operation units,and high dynamic,and monitoring of these processes is *** paper proposes a genetic algorithm(GA)-regularized dynamic canonical correlation analysis(DCCA)-based concurrent modeling and monitoring scheme for multi-unit dynamic ***,the DCCA modeling is performed between each unit and the entire process to characterize both cross-correlation and auto-correlation among ***,the GA regularization is performed for each unit to explore communicating variables and reduce the communication *** a residual-based local fault detector is established for each unit,which performs fault detection by considering both the variables of the local unit and the information provided by its related units,and therefore exhibits superior monitoring *** proposed monitoring scheme is applied on a numerical example and an industrial tail gas treatment *** results demonstrate the feasibility and efficiency of the proposed approach.

关键词： Fault detection Multi-unit dynamic processes Canonical correlation analysis process monitoring

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Intelligent scheduling and flexible operation for the commercial-scale coal-fired power plant integrated with post-combustion carbon capture

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IFAC-PapersOnLine 2022年第9期55卷 553-558页

作者： Peizhi Liao Xiao Wu Meihong Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and Environment Southeast University Nanjing 210096 China

This paper develops a two-level control structure for commercial-scale coal-fired power plant integrated with post-combustion carbon capture (CFPP-PCC). In high-level scheduler, an economic objective function with the consideration of grid demand, price conditions and system constraints is developed. Deep belief network is utilized to develop the steady-state model for CFPP-PCC process. Intelligent scheduling of electricity generation and capture level is then implemented using Bayesian optimization. Given the optimal setpoints, model predictive controller (MPC) is applied to achieve flexible operation in the low-level supervisory control. The simulation results show that the proposed control structure specifies an economically attractive operation profile for the CFPP-PCC process.

关键词： Coal-fired power plant Post-combustion carbon capture Deep belief network Optimal scheduling Model predictive control

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Factored Trace Lasso Based Linear Regression Methods: optimizations and Applications 5th

Factored Trace Lasso Based Linear Regression Methods: Optimi...

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5th International Conference on Cognitive Systems and Signal processing, ICCSIP 2020

作者： Zhang, Hengmin Du, Wenli Liu, Xiaoqian Zhang, Bob Qian, Feng School of Information Science and Engineering Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Department of Computer and Information Science University of Macau 999078 China

ISBN: (纸本)9789811623356

Consider that matrix trace lasso regularized convex p -norm with p= 1, 2 regression methods usually have the higher computational complexity due to the singular value decomposition (SVD) of larger size matrix in big data and information processing. By factoring the matrix trace lasso into the squared sum of two Frobenius-norm, this work studies the solutions of both adaptive sparse representation (ASR) and correlation adaptive subspace segmentation (CASS), respectively. Meanwhile, the derived models involve multi-variable nonconvex functions with at least two equality constraints. To solve them efficiently, we devise the nonconvex alternating direction multiplier methods (NADMM) with convergence analysis satisfying the Karush-Kuhn-Tucher (KKT) conditions. Finally, numerical experiments to the subspace clustering can show the less timing consumptions than CASS and the nearby performance of our proposed method when compared with the existing segmentation methods like SSC, LRR, LSR and CASS. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Singular value decomposition

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Masked GAN for unsupervised depth and pose prediction with scale consistency

arXiv

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arXiv 2020年

作者： Zhao, Chaoqiang Yen, Gary G. Sun, Qiyu Zhang, Chongzhen Tang, Yang The Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74075 United States

Previous work has shown that adversarial learning can be used for unsupervised monocular depth and visual odometry (VO) estimation, in which the adversarial loss and the geometric image reconstruction loss are utilized as the mainly supervisory signals to train the whole unsupervised framework. However, the performance of the adversarial framework and image reconstruction is usually limited by occlusions and the visual field changes between frames. This paper proposes a masked generative adversarial network (GAN) for unsupervised monocular depth and ego-motion estimation. The MaskNet and Boolean mask scheme are designed in this framework to eliminate the effects of occlusions and impacts of visual field changes on the reconstruction loss and adversarial loss, respectively. Furthermore, we also consider the scale consistency of our pose network by utilizing a new scale-consistency loss, and therefore, our pose network is capable of providing the full camera trajectory over a long monocular sequence. Extensive experiments on the KITTI dataset show that each component proposed in this paper contributes to the performance, and both our depth and trajectory predictions achieve competitive performance on the KITTI and Make3D datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Evolutionary algorithm based on the evolution of Pareto archive and individual migration

Evolutionary algorithm based on the evolution of Pareto arch...

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International Symposium on advanced control of Industrial processes (ADCONIP)

作者： Rongbin Qi Tianyi Ma Fan Sun Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Shanghai Business School Shanghai China

An evolutionary algorithm based on the parallel evolution of multiple single objective populations and Pareto archive population is proposed, which is not only suitable for solving multi-objective optimization, but also effective for multimodal function. For each single objective population, single objective evolutionary algorithm is applied to optimize separately each of multi-objective functions, where individuals generated by tournament selection from the union of single objective and Pareto Archive population form the single objective population of next generation. Especially, individuals in Pareto archive population also join evolutionary operations. Simulations manifest that the proposed method can realize the search from multiple directions to obtain the non-dominated solutions scattered more uniformly over the Pareto frontier with better convergence metric compared to well-known NSGA-II algorithm. Individual migration from Pareto archive population by tournament selection is also proved to have the advantage in improving the converging speed and converging precision. Moreover, for multimodal single objective function, simulations also show that ideal optimizing solution can be obtained by properly separating single objective function into multi-objective function and applying the above method.

关键词： Evolutionary computation optimization Convergence Polynomials Algorithm design and analysis Euclidean distance

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Unbiased Minimum-Variance Filtering for Systems with Randomly Multi-Step Sensor Delays

Unbiased Minimum-Variance Filtering for Systems with Randoml...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Yilian Zhang Fuwen Yang Qing-Long Han Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Centre for Intelligent and Networked Systems Central Queensland University

ISBN: (纸本)9781479940318

In this paper, a linear unbiased minimum-variance filtering problem is considered for a class of systems with randomly multi-step sensor delays. A new mathematical model is established for the multi-step sensor delays. Different from the augmented method for dealing with delayed systems, a linear unbiased minimum-variance filter design method is proposed without augmenting the state vector, which effectively reduces the filter dimensions. A recursive algorithm for calculating the filter gain matrix is developed. The simulation results illustrate the effectiveness of the proposed method.

关键词： Networked control systems Filtering Recursive estimation Kalman filters Delay systems Stochastic processes Sensor system Communication networks

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Target to efficient stimulus presentation sequence for P300 BCI

Target to efficient stimulus presentation sequence for P300 ...

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2011年中国智能自动化会议

作者： WANG Guan-kun JIN Jing WANG Xing-yu Key Laboratory of Advanced Control and Optimization for Chemical Processes (Ministry of Education) School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China

The P300 potential is a positive peak of an event-
　　related potential (ERP) that happens at 300 ms after
　　a stimulus. In this
　　study, both offline and online stages were carried
　　out in the experiment. The motivation of this paper
　　is to compare two different
　　P300 brain-computer interfaces (BCIs) which were
　　evoked by random stimulus and non-random stimulus
　　respectively. The offiine
　　data demonstrated that the random stimulus induced a
　　higher P300 amplitude and classification accuracy
　　than the non-random
　　stimulus. The offline data were used to train the
　　Bayesian linear discriminate analysis (BLDA), which
　　was used in offline experiment.
　　The online results further proved that random
　　stimulus P300 BCI yields better performance than non-
　　random stimulus P300 BCI.

关键词： brain-computer interface P300 non-random stimulus random stimulus

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