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

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An Optimized BCI System Based on P300 and Visual Mismatch Negativity

An Optimized BCI System Based on P300 and Visual Mismatch Ne...

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International Conference on Information Science and control Engineering (ICISCE)

作者： Sijie Zhou Jing Jin Yu Zhang Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781467368513

This paper presents a brain-computer interface (BCI) in which the face paradigm was optimized for the visual mismatch negativity (MMN). There were 12 cells in a LCD monitor. A single letter was at the bottom of each cell. In the new paradigm, a color face appeared above each of the 12 cells randomly while the gray faces appeared in others 11 cells. A traditional face paradigm with single character pattern was compared. Three healthy subjects participated in the experiment. Results showed that the new paradigm elicited larger N200 and N400 components than traditional face paradigm and had better performance in online session. The results demonstrated the advantages of the new paradigm in our P300 speller system.

关键词： Face Frequency modulation Visualization Accuracy Brain-computer interfaces Electroencephalography Electrodes

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Dynamic optimization of chemical engineering problems using affinity propagation based estimation of distribution algorithm

Dynamic optimization of chemical engineering problems using ...

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World Congress on Intelligent control and Automation (WCICA)

作者： Na Luo Wei Feng Xiaoqiang Wang Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

Dynamic optimization has attracted much attention for its wide applications in engineering problems. However, it is still a challenge for high nonlinear, multi-dimensional and multimodal problems. Estimation of Distribution Algorithm was proposed in which probabilistic models extracted relevant features of the complex search space and then generated new individuals during optimization. In order to decrease the dependences among control variables in dynamic optimization, affinity propagation was applied to cluster the individuals in evolutionary iterations. In each cluster, the probabilistic density function of Gaussian mixture model refined the promising spaces with high quality solutions and avoided the random combination of different control variables. To evaluate the performance of the new approach, three dynamic optimization problems of chemical process are used as cases comparing with three state-of-the-art global optimization methods. The results showed that the new approach could achieve the best solution in most cases with less computational effort and higher efficiency.

关键词： Statistics Probabilistic logic optimization Sociology Algorithm design and analysis Robustness Genetic algorithms

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Distribution Adaptation Local Outlier Factor for Multimode Process monitoring

Distribution Adaptation Local Outlier Factor for Multimode P...

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Chinese control Conference (CCC)

作者： Yutang Xiao Yang Tao Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In modern industrial processes, the production process includes multiple operating modes, due to changes in production goals and conditions. And the data generated in this process is a mixture of Gaussian and non-Gaussian distributions. Therefore, the data distribution of multimode processes is uncertain and complex. It is difficult to monitor multimode processes accurately by using traditional Multivariate Statistical Process Monitoring (MSPM) methods. In this case, the absence of labeled data is considered more valuable. This paper proposes a new unsupervised domain adaptive method called the Distribution Adaptation Local Outlier Factor (DALOF). This method can extract useful information from multimode data through domain adaptation, which can reduce the distribution difference of training data and improve the accuracy of modeling. In the DALOF model, data from different modes in the training set are defined as the source domain and the target domain. Weight the training data according to their correlation, and then project these data into a low-dimensional subspace. In this space, the distribution distance between the source and target domain is minimum. To solve the nonlinear characteristics of multimode data, this paper uses LOF to build a model and formed a density-based monitoring index. Numerical examples and Tennessee Eastman (TE) process simulation demonstrate the effectiveness of DALOF.

关键词： Monitoring Training data Adaptation models Data models Training Production Process control

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IPSO: An Immune based PSO Supervised Learning System for Incremental Learning

IPSO: An Immune based PSO Supervised Learning System for Inc...

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The 8th World Congress on Intelligent control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Xuan Zhou Jin Yu Rongbin Qi Feng Qian Zhenlei Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

PSO has been proved as an effective supervised learning system in recent years, but it's not an effective method for incremental learning problems. Aiming at the incremental learning target for classification, a hybrid algorithm of Particle Swarm optimization (PSO) and Artificial Immune System (AIS) called Immune based PSO (IPSO) is presented in this paper. IPSO inherits the incremental learning ability of AIS. In IPSO, training data is presented to the algorithm one by one, and the training proceed is a one-shot incremental algorithm. Besides, the swarm does not converge to a single solution;instead, each particle is a part of the classifier, and the whole memory population is taken as the integral classifier to the problem. Compared the results of standard PSO and IPSO in several benchmark problems from the UCI data sets, we found that IPSO achieved a better classification accuracy than standard PSO in most cases. It is also competitive with some of the algorithms most commonly used for classification.

关键词： Classification algorithms Particle swarm optimization Accuracy Training Machine learning Diabetes Iris

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Explicit MPC for multi-rate control systems

Explicit MPC for multi-rate control systems

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作者： Jiang, Yali Zou, Yuanyuan Niu, Yugang Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai 200237 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai 200240 China

ISBN: (纸本)9780956715708

In this paper, explicit model predictive control (MPC) schemes for discrete-time linear-invariant multi-rate systems with constraints on inputs and states are studied. The optimization problem of multi-rate predictive control systems can be formulated by solving a multiparametric linear program (mp-LP), and the explicit state feedback control laws are developed. Further, based on the closed-loop multi-rate piecewise affine (PWA) structure, stability analysis is presented by linear matrix inequality (LMI). The simulation results show the effectiveness of the explicit MPC for multi-rate system. © 2011 Shanghai Jiaotong Univ.

关键词： Linear matrix inequalities

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Dynamic optimization with an improved θ-PSO based on memory recall

Dynamic optimization with an improved θ-PSO based on memory...

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World Congress on Intelligent control and Automation (WCICA)

作者： Weimin Zhong Jianliang Xing Yi Liang Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

A comparative study of θ-PSO and its improved model with partial particles randomization strategy on their abilities of tracking extrema in dynamic environments was carried out in our earlier work. And the results shown that θ-PSO has better performance in dynamic optimization than standard PSO. In this paper, an improved θ-PSO with memory recall and varying scale randomization strategy (θ-PSO-MR) is put forward. The eligible memory particles are recalled when the landscape changes. And the vary scale randomization is introduced through the evolution to maintain the swarm diversity. The offline error in the non-trivial multimodal dynamic functions MPB indicates that this improved θ-PSO deals well with the complex dynamic tracking and optimization. And in some cases, θ-PSO-MR outperforms θ-PSO-Rn for the introduction of memory recall.

关键词： optimization Particle swarm optimization Heuristic algorithms IEEE services Laboratories

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On-demand state estimation with sampling time skew in power systems

On-demand state estimation with sampling time skew in power ...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Xinjun Ma Chensheng Liu Jing Wu Chengnian Long Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781479958306

With new dynamics and uncertainties in today's power grids, traditional fixed-interval State Estimation (SE) may be unable to track the variability and monitor the power grid effectively. This paper presents a new architecture to transfer data and execute SE on demand. A list of situations are summarized to direct the SE-demand generator in system control center. As SCADA and PMU measurements are co-exist in realistic power systems, time skew problem is inevitable. To mitigate the influence of time skew, a state estimator based on time skew oriented weight adaptation is considered. In each SE circle, the weights assigned to the measurements not only correspond to their noise, but also the time offsets relative to the SE-demand point. Numerical examples demonstrate the improved accuracy of our estimator compared with the conventional hybrid SE when measurements time skew is present.

关键词： Phasor measurement units Time measurement Measurement uncertainty Current measurement Computer architecture State estimation Uncertainty

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Real-time control signal extraction based on instantaneous power of surface electromyogram

Real-time control signal extraction based on instantaneous p...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Yujue Wang Bei Wang Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

The previous studies on Human-machine Interface (HMI) based on surface electromyogram (SEMG) control seldom considered individual difference. In this paper, we proposed a novel method to distinguish the handedness of subjects, and extract the real-time control signals from SEMG. SEMG signals recorded at left calf, right calf, left shoulder and right shoulder were analyzed. The instantaneous power of SEMG signals were calculated based on Filter technique and short-time Fourier transformation (STFT).The handedness was discriminated to determine the proper threshold for different subjects. The real-time control signals were generated automatically by comparing the instantaneous power and the thresholds preset.

关键词： Muscles Electrocardiography Band pass filters Time frequency analysis Real time systems Robots Wheelchairs

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Weighted least square support vector machine integrated with parameter optimization and its application in chemical process

Weighted least square support vector machine integrated with...

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International Conference on Natural Computation (ICNC)

作者： Wen-Tong Cui Xue-Feng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai China

Considering that outliers can disrupt the correlation structure of least square support vector machine (LS-SVM), and that the parameters of LS-SVM play an important role in the performance, a novel weighted least square support vector machine integrated with parameter optimization is proposed to obtain the optimal parameters and to eliminate the effect of outliers. Several LS-SVM variants are applied in simulation experimentation and chemical process respectively to demonstrate the satisfactory performance of the proposed method.

关键词： optimization Training Support vector machines Oxidation Adaptation models Mathematical model Educational institutions

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Containment control for the cooperative output regulation of linear multi-agent systems

Containment control for the cooperative output regulation of...

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Chinese control Conference (CCC)

作者： Yanchao Shen Huaicheng Yan Hao Zhang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education Shanghai China Tongji University Shanghai Shanghai CN

In this paper, the containment control problem is studied for the cooperative output regulation of linear multi-agent systems(MASs). The output regulation problem initiated from single-leader-follower region is extended to the multi-leadersfollower region. The purpose of this paper is to design a containment control algorithm such that all the followers can move into the convex hull formed by the leaders. A sufficient and necessary condition in term of a linear matrix inequality (LMI) is derived for the existence of a containment controller protocol that guarantees the followers are capable to follow the trail of leader and the output error will converge to zero with the time elapsing. Then, a controller design procedure is given to construct the feedback gain matrix and select the proper gain matrix for achieving final consensus of the MASs. Compared with the existing algorithms, the proposed one requires less information and calculation of the velocities measurement of the agents. Finally, a numerical example is illustrated to show the usefulness of the designed containment controller.

关键词： Eigenvalues and eigenfunctions Educational institutions Lead Topology Laplace equations Multi-agent systems Algorithm design and analysis

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