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IEEE International Instrumentation and Measurement Technology Conference

作者： Zhili Xiao Yan Fu Chao Tan Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University Tianjin China

ISBN: (纸本)9781479961153

In current applications of magnetic induction tomography (MIT) in brain functional imaging, brain tissue is assumed as medium with homogeneous conductivity, and the electroencephalogram (EEG) is neglected. In order to study the effects of EEG signals on MIT detection results, a 2-D four-layer brain model is established by finite element simulation. Izhikevich neuron model is employed to simulate electrical activities of neurons. Three kinds of typical neurons electrical activities are discussed as internal signals. They are regular spiking, fast-spiking, thalamo-cortical for depolarization, respectively. Wavelet energy combined with FFT method is used to analyze the identification of the detection results for the signal with different patterns. The results show that, EEG is detectable by MIT. According to processing results by the wavelet energy combined with FFT method, the patterns of neurons activity signals can be identified through the frequency components.

关键词： magnetic induction tomography signal recognition electroencephalogram Izhikevich neuron model wavelet energy

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Robust output feedback NMPC with guaranteed constraint satisfaction

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IFAC-PapersOnLine 2015年第14期48卷 326-331页

作者： Sankaranarayanan Subramanian Sergio Lucia Radoslav Paulen Sebastian Engell Process Dynamics and Operations Group TU Dortmund Otto-von-Guericke-University Magdeburg Laboratory for Systems Theory and Automatic Control Institute of Automation Engineering Germany

Nonlinear Model Predictive control (NMPC) employs a plant model to compute a sequence of optimal control inputs for a finite horizon. As, in reality, there always exists a plant-model mismatch and not all states of the plant can be measured, the NMPC scheme must be robust to plant uncertainties and to estimation errors. Different robust NMPC strategies have been proposed to deal with these uncertainties. Among them, a multi-stage NMPC, which is based upon a scenario tree of future plant evolutions, is less conservative compared to worst-case open-loop approaches because the presence of feedback at future sampling instants is explicitly considered. In multi-stage output feedback NMPC, additional scenarios are created by sampling the innovations that are used to estimate the future states of the plant along the scenario tree. In this paper, we refine our previously published approach to include state estimation errors. Moreover we extend the scheme to guarantee robust constraint satisfaction by calculating reachable sets using Taylor models. The method is demonstrated for a nonlinear chemical process example.

关键词： Predictive control Robust control Output feedback Nonlinear control systems

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Multi-source information fusion model in rule-based Gaussian-shaped fuzzy control inference system incorporating Gaussian density function

Multi-source information fusion model in rule-based Gaussian...

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作者： Li, Zairan He, Ting Cao, Luying Wu, Tunhua McCauley, Pamela Balas, Valentina E. Shi, Fuqian Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University Tianjin China Wenzhou Vocational and Technical College Wenzhou China College of Information and Engineering Wenzhou Medical University Wenzhou325035 China Department of Industrial Engineering and Management Systems University of Central Florida OrlandoFL United States Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Romania

An increasing number of applications require the integration of data from various disciplines, which leads to problems with the fusion of multi-source information. In this paper, a special information structure formalized in terms of three indices (the central presentation, population or scale, and density function) is proposed. Single and mixed Gaussian models are used for single source information and their fusion results, and a parameter estimation method is also introduced. Furthermore, fuzzy similarity computing is developed for solving the fuzzy implications under a Mamdani model and a Gaussian-shaped density function. Finally, an improved rule-based Gaussian-shaped fuzzy control inference system is proposed in combination with a nonlinear conjugate gradient and a Takagi-Sugeno (T-S) model, which demonstrated the effectiveness of the proposed method as compared to other fuzzy inference systems. © 2015 - IOS Press and the authors. All rights reserved.

关键词： Fuzzy control

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Robust Adaptive control Design for Rotorcraft Unmanned Aerial Vehicles Based on Sliding Mode Approach

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Transactions of Tianjin University 2014年第6期20卷 393-401页

作者：郭建川鲜斌 Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and AutomationTianjin University

This paper presents a nonlinear robust control design method for a generic rotorcraft unmanned aerial vehicle(RUAV). The control objective is to let the RUAV track some pre-defined time-varying position and heading trajectories. The proposed controller employs feedback linearization process to realize the dynamic decoupling control and applies adaptive sliding mode control to compensate for the parametric uncertainties and external disturbances. The global asymptotical stability is proved via stability analysis. Compared with the cascaded controller, the proposed controller demonstrates a superior tracking performance and robustness through numerical simulation in the presence of parametric uncertainties and unknown disturbances.

关键词： rotorcraft unmanned aerial vehicle (RUAV) nonlinear robust control sliding mode control parametricuncertainty wind gust disturbance

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Fuzzy Inference Algorithm based on Quantitative Association Rules

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Procedia Computer Science 2015年 61卷 388-394页

作者： Ling Wang Ji-Yuan Dong Shu-Lin Li School of Automation & Electrical Engineering University of Science and Technology Beijing 100083 China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing 100083 China

In order to develop a data mining system to extract the fuzzy inference rules from the data, in this paper a fuzzy inference algorithm based on quantitative association rule (FI-QAR) is proposed. First, a discretization algorithm based on an improved clustering for each dimension data is adopted, and then the quantitative results are represented in the form of a Nominal variables matrix to compute the support and confidence level in the Apriori algorithm for quantitative association rules mining. On the basis of this, the quantitative association fuzzy rules are reconstructed by combing with TS fuzzy model to realize fuzzy inference, which can be applied to predict the output class and precise output. Experiment results demonstrated that the proposed algorithm is feasible and practical.

关键词： quantitative association rules apriori algorithm discretization TS fuzzy rules

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Sensitivity matrix construction based on ultrasound modulation for electrical resistance tomography

Sensitivity matrix construction based on ultrasound modulati...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Xizi Song Yanbin Xu Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University Tianjin P.R. China

Image reconstruction of electrical resistance tomography (ERT) is an inherently nonlinear inverse problem. Sensitivity matrix is widely adopted to make this inverse problem to be a linear one. Usually, the sensitivity matrix employed is calculated under a uniform conductivity distribution. However, it is different from the sensitivity matrix of the measured object with inclusions, and the difference between them affects the quality of reconstructed image. Aiming at improving the resolution of reconstructed images, a novel way of constructing the sensitivity matrix is proposed based on ultrasound modulation. With focused ultrasound perturbing the measured object, the conductivity in each focal region will be altered according to the acousto-electric effect. Measuring the boundary voltage change with the corresponding change of the conductivity, then the sensitivity matrix of the measured object will be approximately constructed. The sensitivity matrices constructed based on the ultrasound modulation and on the Geselowitz's sensitivity theorem are presented and analyzed. With the proposed construction method, sensitivity matrix is more sensitive to the inclusion, even in the center of the measured object. Besides, image reconstruction is carried out by sensitivity algorithm using these two kinds of sensitivity matrix. Simulation results indicate that the reconstructed images with higher quality can be obtained with sensitivity matrix constructed based on the ultrasound modulation.

关键词： Sensitivity Image reconstruction Ultrasonic imaging Conductivity Modulation Ultrasonic variables measurement Conductivity measurement

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A modified L-curve method for choosing regularization parameter in electrical resistance tomography

A modified L-curve method for choosing regularization parame...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Yang Pei Yanbin Xu Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University Tianjin P.R. China

Regularization methods are widely used in dealing with the ill-posed inverse problem of electrical resistance tomography (ERT), especially the Tikhonov method. A proper choice of regularization parameter is crucial to produce an efficient regularization solution. As a regularization parameter choice method, L-curve method is popular for its simpleness and intuition. However there is no guarantee that the method can always determine a proper parameter for all situations of ERT. The investigation on those failed situations shows that a new corner point often appears on the curve and the parameter corresponding to the new corner point can obtain a better solution than the one corresponding to the traditional global corner point. Therefore a modified L-curve method is proposed based on the new corner point. Moreover two strategies are provided to implement the modified method: one computes the new corner point by means of the second-order differential of L-curve, and another computes the new corner point with the help of the curvature of L-curve. The modified L-curve method is verified by numerical simulations of some typical distributions in ERT. And the results indicate that the modified method can achieve an efficient solution and implement the image reconstruction where the traditional L-curve method cannot. The modified L-curve method extends the application of L-curve in the field of choosing regularization parameter and can also be applied in other kinds of tomography.

关键词： Image reconstruction Conductivity Inverse problems Tomography Resistance Jacobian matrices Voltage measurement

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Intelligent Setting control Method Based on CBR and iterative learning for Laminar Cooling process

Intelligent Setting Control Method Based on CBR and iterativ...

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第26届中国过程控制会议（CPCC2015）

作者： Pian Jinxiang Zhu Yunlong Liu Jinxin Chai Tianyou Qi Aiwei School of Information and Control engineering Shenyang Jianzhu University Key Laboratory of Networked Control System Chinese Academy of Sciences State Key Laboratory of Integrated Automation for Process Industry Northeastern University

The hot rolled strip laminar cooling system is a complex industrial process, associated with features of strong nonlinear and changing operating conditions. So, the process is hard to control with traditional close loop control methods. But fortunately, there is the repeated characteristic in the laminar cooling process, which is very suited to apply the iterative learning between the strips. So, PI iterative learning method is proposed in this paper to learn the inner knowledge between the strips with similar working condition. On the other hand, in order to improve the control effect,CBR(case-based reasoning) technology is applied to adjust the parameters P and I according to the changing working conditions. The experiments are conducted with industrial operating data. The results show that, with the proposed method, it is effective to find the right operating point quickly and the strip coiling temperature can be controlled in the suitable range.

关键词： laminar cooling iterative learning CBR(case based reasoning) strip coiling temperature

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Prediction of Single Neural Firings for Hodgkin-Huxley Neuron by Fitting Generalized Linear Model

Prediction of Single Neural Firings for Hodgkin-Huxley Neuro...

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第三十四届中国控制会议

作者： Xile Wei Dingtian Shi Meili Lu Bin Deng Haitao Yu Jiang Wang Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University School of Informational Technology and Engineering Tianjin University of Technology and Education

ISBN: (纸本)9781467374439

At the single neuron level, neural information processing involves the transformation of input stimulation into an output spike train. Here a generalized linear model(GLM is used to reconstruct the mapping from stimulation to firing trains of single neuron for Hudgkin-Huxley(H-H) model. Firstly, H-H model is stimulated by the white noise to generate the input-output data samples used to construct GLM. Then, the parameters of GLM are estimated according to the maximum likelihood of the spike time serial of spike trains extracted from action potential of H-H. After that, the input-output mapping of spike trains evoked by white noise for H-H is successfully reconstructed. Through comparing the inter spike interval(ISI) and Pearson's correlation coefficient, it also proves that the established GLM provides a good reproduction and prediction of the firing information for H-H. These studies provide us a new insight into coding processes and information tansfer of single neural.

关键词： Generalized linear model,Hodgkin-Huxley Neuron,Spike trains,Prediction

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Frequency-window effects of subthreshold electric fields on neuronal discharges of active neurons

Frequency-window effects of subthreshold electric fields on ...

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第三十四届中国控制会议

作者： Xile Wei Bingjie Li Meili Lu Jiang Wang Bin Deng Haitao Yu Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University School of Informational Technology and Engineering Tianjin University of Technology and Education

ISBN: (纸本)9781467374439

with a two-compartment model, we investigate how subthreshold sinusoidal electric fields(EFs) affect neuronal discharges of active neurons by adjusting the field intensity and the field frequency. It's found that apparent fluctuations on neuronal discharges only appear within several stimulus frequency windows. And when the field frequency is adjusted to be close to the multiple of the intrinsic spiking rate, the fluctuations are maximized. Compared with the change in neuronal spiking rate, the modulation of neuronal spike timing induced by subthreshold EFs is stronger. Additionally, field effects on neuronal discharges are enhanced by the increasing stimulus intensity. Our results provide useful guidance to understand how subthreshold sinusoidal EFs modulate neuronal discharges, which may help to improve the future electromagnetic therapy.

关键词： subthreshold sinusoidal electric fields,spike timing,spiking rate

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