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A framework for implementation of adaptive autonomy for intelligent electronic devices

Journal of Applied Sciences 2008年第20期8卷 3721-3726页

作者： Fereidunian, A. Lesani, H. Lucas, C. Lehtonen, M. Control and Intelligent Processing Center of Excellence School of ECE University of Tehran 14395-515 Iran Power Systems and High Voltage Laboratory Helsinki University of Technology FI-02015 HUT Finland

This research letter introduces a novel framework for the implementation of Adaptive Autonomy for intelligent Electronic Devices (TEDs). The study aims at achieving an optimum function allocation between IEDs and humans in automation systems. The function allocation should be adapted to the changes in environmental conditions, thus referring to as Adaptive Autonomy (AA). Performance Shaping Factors (PSFs) concept is utilized to represent the environmental conditions. Moreover, Experts' Judgment method is used, to tackle the complex issue of human reliability assessment. The framework is implemented to the power distribution automation system of the Greater Tehran Electricity Distribution Company (GTEDC) and the obtained results are discussed then. Furthermore, the trends of the IED autonomy levels are investigated versus the situation criticality and the automation stages. Apart from introducing a novel implementation framework for IEDs' AA, this letter discusses on the application-oriented issues, due to the context-based nature of the Human-Automation Interaction (HAT). The developmental relevance of this study is significant, as it is performed in a Metropolitan area of an Asian/Middle Eastern developing country, thus generalize-able to the similar applications to a reasonable extend. © 2008 Asian Network for Scientific Information.

关键词： Developing countries

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Lipschitz Numbers: A Medium for Delay Estimation

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IFAC Proceedings Volumes 2008年第2期41卷 7468-7473页

作者： Iman Makaremi Alireza Fatehi Babak Nadjar Araabi Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Eng. K.N. Toosi University of Technology Tehran Iran Control & Intelligent Processing Center of Excellence School of Electrical & Computer Eng. University of Tehran Tehran Iran

The paper deals with problem of estimating input channel delay in nonlinear system with a model-free approach. The proposed method is based on Lipschitz theory. It is an extension to the Lipschitz method which was proposed for determining the order of a model. Our algorithm consists of two parts which in the first one estimation is made on the proper number of dynamics on the input and in the second part the pure delay of the input is obtained. The method is applied for estimation of the delay of two different models and the estimation was as accurate as possible.

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Real-Time Level Plant control Using Improved BELBIC

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IFAC Proceedings Volumes 2008年第2期41卷 4631-4635页

作者： M. Masoudinejad R. Khorsandi A. Fatehi C. Lucas S. Fakhimi Derakhshan M.R. Jamali Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Center of Excellence for Control and Intelligent Processing Electrical and Computer engineering Department University of Tehran Iran

Brain emotional learning based intelligent controller (BELBIC) is based on computational model of limbic system in the mammalian brain. In recent years, this model was applied in many linear and nonlinear control applications. Previous studies show that this controller has fast response, simple implementation and robustness with respect to disturbances. It is also possible to define emotional signal based on control application objectives. But in the previous studies, internal instability of this controller was not considered and control task were done in limited time period. In this article mathematical description of BELBIC is investigated and improved to avoid internal instability. Simulation and implementation of improved model was done on level plant. The obtained results showed that instability of model has been solved in the new model without loss of performance by using Integral Anti Windup (IAW).

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Identification and Abnormal Condition Detection of a Cement Rotary Kiln

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IFAC Proceedings Volumes 2008年第2期41卷 7233-7238页

作者： Iman Makaremi Alireza Fatehi Babak N. Araabi Morteza Azizi Ahmad Cheloian Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Eng. K.N. Toosi University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical & Computer Eng. University of Tehran Tehran Iran Saveh Cement Company Saveh Iran

In this paper, we use system identification methods for abnormal condition detection of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. A novel approach is used in order to estimate the delays of the input channel of the kiln. By means of that, the identification task gets easier and the results are more accurate. To identify the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Finally, a model for the healthy mode of the kiln is obtained through which it is possible to detect abnormal conditions in the process. We distinguished two common abnormal conditions in kiln and another one which was not characteristically known for cement experts as well.

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A Neuro-Fuzzy controller for Rotary Cement Kilns

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IFAC Proceedings Volumes 2008年第2期41卷 13259-13264页

作者： Maryam Fallahpour Alireza Fatehi Babak N. Araabi Morteza Azizi Control & Intelligent Processing Center of Excellence School of Electrical & Computer Eng. University of Tehran Tehran Iran Advanced Process Automation & Control (APAC) Research Group Electrical Engineering Faculty K. N. Toosi University of Technology Tehran Iran Saveh Cement Co. Saveh Iran

In this paper, we design a neurofuzzy controller to control several variables of a rotary cement kilns. The variables are back-end temperature, pre-heater temperature, oxygen content and CO2 gas content of the kiln. The fuzzy control system, as an advanced control option for the kilns, is intended to minimize the operator interaction in the control process. The proposed fuzzy controller uses a neural network to optimize TSK-type fuzzy controller. Since there is no generally applicable analytical model for cement kilns, we use the real data derived from Saveh cement factory for the plant identification. A model, which is very similar to the real plant, is identified then; and the identified model is used for control design and simulations. Extensive simulation studies justify the effectiveness and applicability of the proposed control scheme in intelligent control of cement plant.

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Automatic segmentation of brain structures from MRI integrating atlas-based labeling and level set method

Automatic segmentation of brain structures from MRI integrat...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Matineh Shaker Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA

The purpose of this study is to segment hippocampus, amygdala and entorhinal cortex in magnetic resonance images (MRI) of temporal lobe epilepsy (TLE) patients. The proposed method consists of two separate parts. First, we use an atlas-based segmentation method to obtain initial segmentation results for desired structures. Using additional preprocessing steps for image registration and gray matter (GM) segmentation is the specification of this stage of the work. Then, all of the GM voxels are labeled using an anatomical atlas. In the next stage, variational level set formulation without re-initialization is applied on the images. We use the boundaries obtained by atlas-based segmentation as the contour for initialization of level set function. Automatic generation of initial contour makes the final segmentation results operator-independent. The proposed approaches are evaluated by comparing automatic and expertpsilas segmentation results and confirming their similarity.

关键词： Brain Magnetic resonance imaging Labeling Level set Image segmentation Hippocampus Magnetic resonance Temporal lobe Epilepsy Image registration

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Atlas based segmentation of white matter fiber bundles in DTMRI using fractional anisotropy and principal eigen vectors

Atlas based segmentation of white matter fiber bundles in DT...

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IEEE International Symposium on Biomedical Imaging

作者： Esmaeil Davoodi-Bojd Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineeringr University of Tehran Tehran Iran Radiology Department Henry Ford Hospital Image Analysis and Communications Laboratory Detroit MI USA

In this work, we develop an atlas based method for automatic segmentation of white matter fiber bundles. To this end, we propose a new method for registration of diffusion tensor (DT) images using DTI information which is also used in the fiber tracking process, and we also propose a strategy for segmenting the fiber bundles using the new registration method and a probabilistic white matter atlas. We apply the registration method to 13 real DTI data sets and evaluate the results by comparing the level of alignment of all fibers. Then, we use the proposed strategy to segment 10 major fiber bundles in one of the subjects. One of the advantages of such a method is the robustness of the results thanks to using prior knowledge. The segmented results can be used for comparing and evaluating other fiber bundle segmentation methods.

关键词： Anisotropic magnetoresistance Image segmentation Image registration Tensile stress Diffusion tensor imaging Cost function Robustness Automatic control Radio control Process control

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Reduced Reference Watermark-Based Image Transmission Quality Metric

Reduced Reference Watermark-Based Image Transmission Quality...

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3rd International Symposium on Communications, control, and Signal processing (ISCCSP 2008), vol.1

作者： Mohammad Hosain Kayvanrad Shabnam Sodagari Alireza Nasiri Avanaki Hossein Ahmadi-Noubari Embedded Video (EV) Laboratory Department of ECE National University of Singapore Singapore Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Iran Department of ECE University of British Columbia University of Tehran Iran

A novel reduced reference (RR) method of channel estimation for image transmission through lossy channels is presented. Estimation is based on the amount of distortion the low frequency content of the image or its histogram suffers during transmission. This distortion is computed by sending the low frequency content of the original image or the corresponding histogram to the receiver by means of a robust watermark. Simulations for different types of images and different channel losses yield large amounts of correlation between the estimated Mean Square Error (MSE) and the structural similarity (SSIM) index.

关键词： Image transmission Reduced reference quality assessment Watermarking Two dimensional wavelet transform Histogram

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Feature fusion as a practical solution toward noncooperative iris recognition

Feature fusion as a practical solution toward noncooperative...

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International Conference on Information Fusion

作者： Nima Tajbakhsh Babak N. Araabi Hamid Soltanianzadeh Control & Intelligent Processing Center of Excellence School of Electrical & Computer Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel approach for iris recognition is proposed so that extracted features of NIR and VL images are fused to improve the recognition rate. When the images do not have enough quality due to focus, contrast, etc., effects of feature fusion is more pronounced. This is the situation in UTIRIS database, which is used in our experiments. Experimental results show that the proposed approach, especially in small training samples, leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.

关键词： Iris Feature extraction Discrete cosine transforms Image recognition Databases Training Humans

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Constrained optimization of nonparametric entropy-based segmentation of brain structures

Constrained optimization of nonparametric entropy-based segm...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

We propose a constrained, three-dimensional, nonparametric, entropy-based, coupled, multi-shape approach to segment subcortical brain structures from magnetic resonance images (MRI). The proposed method uses PCA to develop shape models that capture structural variability. It integrates geometrical relationship between different structures into the algorithm by coupling them (limiting their independent deformations). On the other hand, to allow variations among coupled structures, it registers each structure separately when building the shape models. It defines an entropy-based energy function, which is minimized using quasi-Newton algorithm. To this end, probability density functions (pdf) are estimated iteratively using nonparametric Parzen window method. In the optimization algorithm, constraints are used to improve segmentation quality. These constraints are extracted from training data. Sample results are given for the segmentation of caudate, hippocampus, and putamen, illustrating highly superior performance of the proposed method compared to the most similar methods in the literature.

关键词： Constraint optimization Brain Iterative algorithms Image segmentation Shape Couplings Magnetic resonance Magnetic resonance imaging Principal component analysis Buildings

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