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Hybrid Consensus-based Algorithm for Distributed Economic Dispatch Problem

IFAC-PapersOnLine 2017年第1期50卷 177-182页

作者： Liu X.-K. Wang Y.-W. Yan H. Wang X. Hu X. School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education China Key Laboratory of Advanced Control Optimization for Chemical Process of Ministry of Education China School of Information Science and Engineering East China University of Science and Technology 130 Meilong Road Shanghai 200237 China

Economic dispatch is one of the fundamental problems in the power system research. The existing algorithms are either discrete iterative algorithms or continuous-time dynamical algorithms. By virtue of the hybrid techniques, a hybrid consensus-based algorithm is proposed in this paper, which consists of a finite-time continuous-time algorithm and a discrete-time interacted scheme. Moreover, inspired by the cluster synchronization and the benefit of the projection operator, a supervised strategy is carried out to ensure that each generator unit will work within its power generation constraint. For a better understanding of the supervised hybrid distributed algorithm, the detailed pseudo-code is presented. Finally, a numerical example is given to illustrate the effectiveness of the algorithm. © 2017

关键词： consensus protocol distributed optimization economic dispatch hybrid system smart grid

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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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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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Fault detection based on polygon area statistics of transformation matrix identified from combined moving window data

Fault detection based on polygon area statistics of transfor...

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第26届中国过程控制会议（CPCC2015）

作者： Bei Wang Xuefeng Yan Yongfei Jin Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Department of Mathematics East China University of Science and Technology

As a classic multivariate statistical method, principal component analysis(PCA) has been widely used in monitoring industrial processes, but it is still necessary to make improvements in having a timely and effective access to variation information. It is known that the transformation matrix generated from real-time PCA model indicates inner relations between original variables and new produced components, so this matrix would be different when some variables deviate from the original values area due to the change of the operating condition. Based on this theory, this paper proposes a novel real-time monitoring approach which utilizes polygon area method to measure the variation degree of the transformation matrices and then constructs a statistic for monitoring purpose. The on-line data are collected through a combined moving window(CMW), which contain normal and monitored data at the same time in order to distinguish the faulty data. To evaluate the performance of the proposed method, a simple numerical simulation and the classic Tennessee Eastman process are employed for illustration, with some PCA-based methods used for comparison.

关键词： principal component analysis combined moving window real-time process monitoring fault detection

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Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition

Cost-Quality Adaptive Active Learning for Chinese Clinical N...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

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 issue is active learning, where a model asks labelers to annotate data which is beneficial to model performance improvement. 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 cost 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 cost and the informativeness of selected instances. Specifically, our proposed CQAAL method selects cost-effective instance-labeler pairs to achieve better annotation quality with lower cost in an adaptive manner. Computational results on the CCKS2017 dataset demonstrate the superiority and effectiveness of CQAAL.

关键词： Annotations Uncertainty Labeling Task analysis Entropy Manuals Neural networks

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Leader-follower synchronization for complex dynamical networks via sampled-data control

Leader-follower synchronization for complex dynamical networ...

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Chinese control Conference

作者： Wangli He Feng Qian Qing-Long Han The Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Centre for Intelligent and Networked Systems Central Queensland University

ISBN: (纸本)9781467325813

This paper is concerned with leader-follower synchronization of complex dynamical networks with sampled-data control. By sampling the signal from the leader at some discrete time instants and using a zero-order hold function, synchronization is achieved between the network and a desired orbit, known as the leader. By applying Lyapunov functional approach and the property of the network topology matrix, a delay-dependent criterion is derived. It is shown that synchronization of N coupled dynamical systems with a leader can be recast into the stability of N decoupled systems, in which eigenvalues of the network topology matrix are involved. Finally, a chaotic neural network is used to illustrate the effectiveness of the proposed method.

关键词： Synchronization Sampled-data control Complex networks

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L2- L∞ State Estimation for Discrete-time Delayed Neural Networks with Missing Measurements and Randomly Occurring Sensor Linearity

L2- L∞ State Estimation for Discrete-time Delayed Neural Ne...

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第35届中国控制会议

作者： Hao Zhang Huaicheng Yan Hongbo Shi Hao Zhang Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education Department of Control Science and Engineering Tongji University

ISBN: (纸本)9781509009107

This paper is concerned with the L-L state estimation problem for discrete-time delay neural networks with missing measurements and randomly occurring sensor *** phenomena of missing measurements and randomly occurring sensor linearity are constructed with two sequences of random variables,which obey the partial Bernoulli distribution.A sufficient condition is firstly given such that the augmented filtering error system is stochastically stable with a guaranteed optimal L-L performance by solving a set of linear matrix inequalities(LMIs).Finally,a simulation example is given to show the effectiveness of the proposed method.

关键词： L2-L∞ state estimation discrete-time delayed neural networks missing measurements randomly occurring sensor linearity

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Evolutionary algorithm using kernel density estimation model in continuous domain

Evolutionary algorithm using kernel density estimation model...

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Asian control Conference

作者： Na Luo Feng Qian Key Laboratory of Advanced Control and Optimization of Chemical Process Ministry of Education State Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai China Key Laboratory of Advanced Control and Optimization of Chemical Process Ministry of Education State-Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai China

ISBN: (纸本)9781424454402

Estimation of distribution algorithm (EDA) is a kind of evolutionary algorithm which updates and samples from probabilistic model in evolutionary course. The key of EDA is the construction of probability model suitable for real distribution. Gaussian distribution is widely used in EDAs but the assumption of normality is not realistic for many real-life problems. In this paper, a new EDA using kernel density estimation (KEDA) is introduced. Adaptive change strategy of kernel width is presented and selection scheme, sampling method are also given cooperated with KEDA. The results of 5 benchmark functions show that results of KEDA outperform PBIL C , UMDA C , EDA G , H-EDA.

关键词： Evolutionary computation Kernel Electronic design automation and methodology Laboratories Histograms Density functional theory Gaussian distribution Automatic control chemical processes control engineering education

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _optimization -norm usually achieve better performance than the convex case (e.g., \ell _optimization -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： optimization

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