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International Conference on Computer and Communications (ICCC)

作者： Hanhan Zhang Jing Jin Sijie Zhou 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

Brain-computer interface (BCI) plays an important role in helping the people with severe motor disability. In event-related potential (ERP) based BCIs, subjects were asked to count the target stimulus in the offline experiment, the recorded electroencephalogram (EEG) data was used to train the classification mode. However, subjects may make mistakes in counting the target stimulus or be affected by the non-target stimulus. The target trials may not contain expected ERPs and the non-target trials may contain unexpected ERPs, which was called error samples. This paper intends to survey whether the classification accuracy could be improved after removing these error samples from offline training data. The result showed that the online performance of BCI system could be improved after selecting the offline samples for training the classification mode.

关键词： Electroencephalography Feature extraction Training Classification algorithms Electrodes Band-pass filters Brain modeling

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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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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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Corrigendum to “Research on data reconciliation based on generalized T distribution with historical data” [Neurocomputing 175PA (2015) 808–815]

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Neurocomputing 2016年 177卷 670-670页

作者： Shengxi Wu Qiang Ye Cheng Chen Xingsheng Gu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology No. 130 of Meilong Road Shanghai 200237 China

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optimization of Catalytic Naphtha Reforming Process Based on Modified Differential Evolution Algorithm ∗

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IFAC-PapersOnLine 2015年第8期48卷 373-378页

作者： Min Wei Minglei Yang Feng Qian Wenli Du Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education Shanghai CO 200237 PRC Tel: ***

Catalytic naphtha reforming is one of the most important processes for high octane gasoline manufacture and aromatic hydrocarbons production. In this article, a modified differential evolution (DE) algorithm is proposed to optimize an actual continuous catalytic naphtha reforming (CCR) process. The optimization problem considers to minimize the energy consumption and maximize the aromatics yield. The CCR process model is established by adopting the 27-lumped kinetics reaction network, and all parameters are adjusted based on the actual process data. The DE algorithm is modified to maintain the diversity of the population. In this mechanism, individuals further from the best individual have larger possibilities to be selected in the mutation operator. The modified DE is evaluated by solving 6 benchmark functions, and the performance is compared with classic DEs. The results demonstrate that the modified DE has better global search ability and higher computation efficiency. Furthermore, the optimization results of catalytic naphtha reforming process indicate that the proposed algorithm has the ability of locating the optimal operating points, in which the aromatics yield is improved, while energy consumption is reduced. Meanwhile, the optimal operating points and results are discussed at the end of the article.

关键词： Catalytic naphtha reforming process Energy consumption Differential evolution Global search

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Hellinger distance based probability distribution approach to performance monitoring of nonlinear control systems

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Chinese Journal of chemical Engineering 2015年第12期23卷 1945-1950页

作者：李晨黄彪钱锋 Key Laboratory of Advanced Control and Optimization Ministry of EducationEast China University of Science and Technology Department of Chemical and Materials Engineering University of Alberta

control performance monitoring has attracted great attention in both academia and industry over the past two decades. However, most research efforts have been devoted to the performance monitoring of linear control systems, without considering the pervasive nonlinearities(e.g. valve stiction) present in most industrial control systems. In this work, a novel probability distribution distance based index is proposed to monitor the performance of non-linear control systems. The proposed method uses Hellinger distance to evaluate change of control system performance. Several simulation examples are given to illustrate the effectiveness of the proposed method.

关键词： control performance monitoring Kernel density estimation Hellinger distance Nonlinear system

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Majorization Theory in Sensor Scheduling

Majorization Theory in Sensor Scheduling

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IEEE Annual Conference on Decision and control

作者： Chao Yang Wen Yang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes Dept. of Automation East China University of Science and Technology Shanghai China 200237

ISBN: (纸本)9781479978878

This paper presents that the majorization theory plays an essential role in a class of sensor scheduling problems, whose solutions all have periodic or uniformly distributed patterns. This paper revisits the problem of communication time scheduling for a single sensor with local computation capability, and strengthens its original result by the majorization theory. The scheduling for a single normal sensor in a general-order system is also studied, and the optimal schedules for minimizing the upper bound of the objective function is provided. Examples are provided at the end.

关键词： Sensors Objective function Reinforcement Optimal scheduling

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Convergence analysis for an identifier-based adaptive dynamic programming algorithm

Convergence analysis for an identifier-based adaptive dynami...

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Chinese control Conference (CCC)

作者： Jun Song Yugang Niu Yuanyuan Zou Key Laboratory of Advanced Control and Optimization for Chemical Process East China University of Science and Technology China Key Laboratory of Advanced Control and Optimization for Chemical Process East China University of Science and Technology Shanghai China

This paper investigates the uniformly ultimate boundedness (UUB) of an identifier-based adaptive dynamic programming (ADP) algorithm proposed in [7]. It is demonstrated that the estimation errors of weights in both critic and action networks are UUB during iteration learning. Moreover, a selection method on learning rates is also given.

关键词： Markov processes Artificial neural networks Heuristic algorithms Optimal control Algorithm design and analysis Estimation error Convergence

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Dynamic Multi-objective optimization of chemical processes Using Modified BareBones MOPSO Algorithm

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Journal of Donghua University(English Edition) 2014年第2期31卷 184-189页

作者：杜文莉王珊珊陈旭钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population ***, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.

关键词： dynamic multi-objective optimization bare-bones particle swarm optimization(PSO) algorithm chemical process

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Locust Behaved Particle Swarm optimization Technique

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Journal of Donghua University(English Edition) 2014年第2期31卷 190-196页

作者：钟伟民谢雪勤梁毅罗娜张娟钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density,which is controlled by a serotonin called 5-hydroxytryptamine( 5HT). In this paper,based on the mechanism of the locusts' collective behavior,a new particle swarm optimization technique called LBPSO is studied. The number of swarms is selfadaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT,which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO,social particle swarm optimization( SPSO), improved SPSO and the standard particle swarm optimization( StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark( MPB)show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.

关键词： evolutionary algorithm particle swarm optimization(PSO) locust collective behavior

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