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IEEE International Conference on Electric Machines and Drives (IEMDC)

作者： E. Daryabeigi Gh. Arab Markadeh C. Lucas Electrical Engineering Department of Islamic Najafabad branch Islamic Azad University슠 Isfahan Iran Faculty of engineering Department Shahrekord University Shahrekord Iran Iranian Control & Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran

In this paper, a modern intelligent control based on computational model of mammalian limbic system and emotional processes (BELBIC) is presented for speed control of interior magnet synchronous motor (IPMSM). In this work, a novel and simple model of IPMSM motor drive control plant is achieved by using the intelligent control system, which controls motor speed accurately, without needing to using any conventional controllers and also, quite independent of motor parameters. This work was successfully implemented in real time for a laboratory 1-hp IPMSM drive, but this time it is done in simulation by a new control plant for that, which compared with together. Achieved results of the proposed plant show better operating control than the previous plant. The results show excellent control characteristics especially very fast response, simple implementation and robustness with respect to disturbances and manufacturing imperfections. This type of intelligent controllers that has high auto learning speed with simple structure shows superior praxis for industrial scale utilization.

关键词： Permanent magnet motors Synchronous motors intelligent control Computational intelligence Computational modeling Velocity control Motor drives control system synthesis Laboratories Robust control

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A new method for characterization of coronary plaque composition via IVUS images

A new method for characterization of coronary plaque composi...

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IEEE International Symposium on Biomedical Imaging

作者： Arash Taki Alireza Roodaki Olivier Pauly S. K. Setarehdan Gozde Unal Nassir Navab Department of Computer Aided Medical Procedures (CAMP) Technical University Munich Munich Germany Department of Signal Processing and Electronic Systems Supelec Gif-sur-Yvette France Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Faculty of Engineering and Natural Sciences Sabanci University Turkey

ISBN: (纸本)9781424439317

IVUS-derived virtual histology (VH) permits the assessment of atherosclerotic plaque morphology by using radiofrequency analysis of ultrasound signals. However, it requires the acquisition to be ECG-gated, which is a major limitation of VH. Indeed, its computation can only be performed once per cardiac cycle, which significantly decreases the longitudinal resolution of VH. To overcome this limitation, the introduction of an image-based plaque characterization is of great importance. Current IVUS image processing techniques do not allow adequate identification of the coronary artery plaques. This can be improved by defining appropriate features for the different kinds of plaques. In this paper, a novel feature extraction method based on Run-length algorithm is presented and used for improving the automated characterization of the plaques within the IVUS images. The proposed feature extraction method is applied to 200 IVUS images obtained from five patients. As a result an accuracy rate of 77% was achieved. Comparing this to the accuracy rates of 75% and 71% obtained using co-occurrence and local binary pattern methods respectively indicates the superior performance of the proposed feature extraction method.

关键词： Feature extraction Gray-scale Biomedical engineering Radio frequency Image resolution Signal processing Signal analysis Ultrasonic imaging Signal resolution Arteries

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Particle filtering of point processes observation with application on the modeling of visual cortex neural spiking activity

Particle filtering of point processes observation with appli...

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International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Yousef Salimpour Hamid Soltanian-Zadeh Computational Neuroscience in School of Cognitive Sciences Institute of Studies in Fundamental Sciences Tehran Iran Department of Radiology Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence(CIPCE) Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Recording of neural response to specific stimulus in a repeated trial is very common in neuroscience protocol. The perstimulus time histogram (PSTH) is a standard tool for analysis of neural response. However it could not capture the non-deterministic properties of the neuron especially in higher level cortical area such as inferior temporal cortex. The stochastic state point process filter theory is used for the estimation of the conditional intensity of the point process observation as a time varying firing rate and the particle filter is used to numerically estimate this density in time. The particle filters were applied to the results of the point process observation for compensating the Gaussian assumption. The results of applying point process modeling on a real data from inferior temporal cortex of macaque monkey indicates that, based on the assessment of goodness-of-fit, the neural spiking activity and biophysical property of neuron could be captured more accurately in compare to conventional methods.

关键词： Filtering Brain modeling Neurons Stochastic processes State estimation Particle filters Histograms Mathematical model Neural engineering Neuroscience

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Polynomial kernel function and its application in locally polynomial neurofuzzy models

Polynomial kernel function and its application in locally po...

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International CSI Computer Conference, CSICC

作者： A. Shirvani H. Chegini S. Setayeshi C. Lucas Tehran Science and Research Branch Computer Engineering Department Islamic Azad University Iran Faculty of Nuclear Engineering and Physics Amirkabir University of Technology슠 Tehran Iran Electrical and Computer Engineering Department Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran

Polynomials are one of the most powerful functions that have been used in many fields of mathematics such as curve fitting and regression. Low order polynomials are desired for their smoothness, good local approximation and interpolation. Being smooth, they can be used to locally approximate almost any derivable function. This means that when linear functions fail in approximation (e.g. where the first order Taylor expansion equals zero) polynomial functions can be used in local approximation, such that one can achieve better estimations at extremums. In this paper, application of polynomial kernel functions in locally linear neurofuzzy models is shown. Using polynomial kernels in local models, better local approximations in prediction of chaotic time series such as Mackey-Glass is achieved, and the capability of the neurofuzzy network is enhanced.

关键词： Polynomials Kernel Neurons Predictive models Power engineering and energy Chaos Application software Physics computing Power engineering computing Taylor series

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intelligent-adaptive flight control with a physiologically motivated algorithm

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International Journal of Modelling and Simulation 2009年第1期29卷 12-18页

作者： Mehrabian, A.R. Lucas, C. School of Mechanical Engineering Department of Engineering University of Tehran P.O. Box: 14875 - 347 Tehran Iran Center of Excellence for Control and Intelligent Processing School of Electrical and Computer Engineering Department of Engineering P.O. Box: 14395 - 515 Tehran Iran School of Cognitive Science IPM Tehran Iran

This paper presents a now intelligent approach for adaptive control of non-linear dynamic systems. Based on past researches on emotionally intelligent-control systems, the introduced method is a modified version of Brain Emotional Learning Based intelligent controller (BELBIC), a physiologically motivated system previously developed and successfully used in control applications. The proposed approach is able to quickly respond to change in environmental and dynamic system and realizes online diroct-adaptivc-control scheme. Showing very good robustness, BELBIC is very simple in implementation and imposes very low computation load on the control system. This modified algorithm has boon successfully applied to real-time longitudinal control of an agile air-to-air missile which has over-changing non-linear dynamics.

关键词： intelligent systems

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Multi-mode Narrow-band Thresholding with Application in Liver Segmentation from Low-contrast CT Images

Multi-mode Narrow-band Thresholding with Application in Live...

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International Conference on intelligent Information Hiding and Multimedia Signal processing (IIHMSP)

作者： Amir H. Foruzan Yen-Wei Chen Reza A. Zoroofi Akira Furukawa Yoshinobu Sato Masatoshi Hori College of Information Science and Engineering Ritsumeikan University Shiga Japan Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran Ritsumeikan Daigaku Kyoto Kyoto JP Department of Radiology Shiga University of Medical Science Shiga Japan Department of Radiology Osaka University Osaka Japan

Segmentation of liver in CT images is regarded as a challenge in image processing due to low-contrast of datasets, variety of liver shape, and its non-uniform texture; especially for abnormal cases. In this paper, we deal with normal and abnormal datasets as images containing two or more Gaussian components. We threshold a slice in a narrow band of each mode, find liver pixels based on a priori knowledge, prepare a probability map, and threshold the map to find initial liver border. Final boundary of liver is obtained through a few iterations of `Geodesic Active Contour'. The proposed method was tested on 30 normal and 17 abnormal datasets each containing 159-263 slices; acquired from different CT machines. The results for normal and abnormal datasets are completely acceptable, according to the evaluation done by a specialist. However, for severely abnormal datasets, the proposed method is regarded as a promising algorithm for liver segmentation.

关键词： Narrowband Liver Image segmentation Computed tomography Biomedical engineering Data engineering Biomedical imaging Active contours Robustness Educational institutions

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Multi-area integrated E/MEG and fMRI modeling

Multi-area integrated E/MEG and fMRI modeling

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Babajani-Feremi, Abbas Bowyer, Susan Moran, John Elisevich, Kost Podell, Kenneth Soltanian-Zadeh, Hamid Image Analysis Lab. Radiology Department Henry Ford Hospital Detroit MI 48202 United States Neuromagnetism Lab. Neurology Department Henry Ford Hospital Detroit MI 48202 United States Department of Neurology Henry Ford Hospital Detroit MI 48202 United States Division of Neuropsychology and Sports Concussion Safety Program Henry Ford Hospital Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran 14395-515 Iran

ISBN: (纸本)9780819475138

Functional magnetic resonance imaging (fMRI) has complementary spatiotemporal resolution compared to Electroencephalography (EEG) as well as Magnetoencephalography (MEG). Thus, their integrated analysis should improve the overall resolution. To integrate analysis of E/MEG and fMRI, we extend our previously proposed integrated E/MEG and fMRI neural mass model to a multi-area model by defining two types of connections: the Short-Range Connections (SRCs) between minicolumns within the areas and Long-Range Connections (LRCs) between inter-areas minicolumns. The nonlinear input/output relationship in the proposed model is derived from the state space representation of the multi-area model. The E/MEG signals are originated from the overall synaptic activities of the pyramidal cells of all minicolumns and can be calculated using the lead field matrix (i.e., forward electromagnetic model). The fMRI signal is extracted from the proposed integrated model by calculating the overall neural activities in the areas and using it as the input of the extended balloon model (EBM). Using the simulation results, the capabilities of the proposed model to generate E/MEG and fMRI signals is shown. In addition, changes in the dynamics of the model to variations of its parameters were evaluated and lead us to the appropriate ranges for the parameters. In conclusion, this work proposes an effective method to integrate E/MEG and fMRI for the more effective use of these techniques in functional neuroimaging. © 2009 SPIE.

关键词： Electroencephalography

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Model based method for determining the minimum embedding dimension from solar activity chaotic time series

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International Journal of Engineering, Transactions A: Basics 2008年第1期21卷 31-44页

作者： Mirmomeni, M. Lucas, C. Center of Excellence of Control and Intelligent Processing Department of Electrical and Computer Engineering University Tehran Iran

Predicting future behavior of chaotic time series system is a challenging area in the literature of nonlinear systems. The prediction's accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. On the other hand the cyclic solar activity as one of the natural chaotic systems has significant effects on earth, climate, satellites and space missions. Several methods have been introduced for prediction of solar activity indices especially the sunspot number, which is a common measure of solar activity. In this paper, the problem of embedding dimension estimation for solar activity chaotic time series based on polynomial models is considered. The optimality of embedding dimension has an important role in computational efforts, Lyapunov exponents' analysis and efficiency of prediction. The method of this paper is based on the fact that the reconstructed dynamics of an attractor should be a smooth map, i.e. with no self intersection in the reconstructed attractor. To check this property, a local general polynomial autoregressive model is fitted to the given data and a canonical state space realization is considered. Then, the normalized one-step forward prediction error for different orders and various degrees of nonlinearity in polynomials is evaluated. Besides the estimation of the embedding dimension, a predictive model is obtained which can be used for prediction and estimation of the Lyapunov exponents. This algorithm is applied to indicate the minimum embedding dimension of sunspot numbers (SSN), Disturbance Storm Time or Dst. and Proton Flux indices are some of the most important among solar activity indices and results depict the power of the proposed method in embedding dimension estimation.

关键词： Polynomials

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Comparison of rotary cement kiln identified models

Comparison of rotary cement kiln identified models

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International Conference on control and Automation (ICCA)

作者： G. Noshirvani A. Fatehi B. Araabi M. Shirvani M. Azizi ACER Markaz Branch Arak Iran Advanced Process Automation & Control Research group Electrical Engineering Department K.N. Toosi University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran Chemical Engineering Department and member of Cement Research Center University of Science and Technology Tehran Iran Saveh Cement Company Saveh Iran

Rotary cement kiln is the main part of a cement plant that clinker is produced in it. Clinker is the main ingredient of cement. Continual and prolonged operation of rotary cement kiln is vital in cement factories. However, prolonged operation of the kiln is not possible and periodic repairs of the refractory lining would become necessary, due to non-linear phenomena existing in the kiln, such as sudden falls of coatings in the burning zone and probability of damages to the refractory materials during production. This is the basic reason behind the needs for a comprehensive model which is severely necessary for better control of this process. Such a model can be derived based on the mathematic analysis with consultation of expert operator experiences. In this paper both linear and nonlinear model are identified for rotary kiln of Saveh white cement factory. The linear model is introduced using Box-Jenkins structure. The results of the obtained model were satisfactory compared to some other linear models and can be used for designing adaptive or robust controllers. Also, nonlinear system identification via Neural Network technique is performed and its result was compared to linear models.

关键词： Kilns Production facilities Coatings Building materials Process control Mathematical model Mathematics Programmable control Adaptive control Robust control

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NEURO-FUZZY BASED FAULT DIAGNOSIS OF A STEAM GENERATOR

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IFAC Proceedings Volumes 2009年第8期42卷 1180-1185页

作者： Roozbeh Razavi-Far Hadi Davilu Vasile Palade Caro Lucas Department of Nuclear Engineering Amirkabir University of Technology P.O. Box 15875-4413 Tehran Iran (Tel:+98-***) Computing Laboratory Oxford University Wolfson Building Parks Road Oxford OX1 3QD UK Center of Excellence on Control and Intelligent Processing Department of Electrical and Computer Engineering University of Tehran P.O. Box 14395-1515 Tehran Iran

This paper focuses on the development and application of a Neuro-Fuzzy (NF) networks-based scheme for Fault Detection and Isolation (FDI) in a U-tube Steam Generator (UTSG). First, a NF network is trained with data collected from a full scale UTSG simulator, and residuals are generated for fault detection. To identify the UTSG, a Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained using the Locally Linear Model Tree (LOLIMOT) algorithm which is an incremental tree structure algorithm. Then, an evolutionary algorithm is used to train a Mamdani type NF network to classify the residuals. The residuals are analyzed by using this NF classifier for fault isolation purposes.

关键词： Fault Diagnosis Neuro-Fuzzy Networks Locally Linear Neuro Fuzzy Model Locally Linear Model Tree Steam Generator

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