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9th Mini Conference on Vehicle system Dynamics, Identification and Anomalies, VSDIA 2004

作者： Kulcsár, Balázs Varga, István Bokor, József Department of Transport Automation Budapest University of Technology and Economics Bertalan L. u. 2 H-1111 Budapest Hungary System and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences Kende u. 13-17 H-1111 Budapest Hungary

ISBN: (纸本)9634208754

The paper proposes the estimation of unmeasured variables of traffic systems for traffic control purposes. The presented method proposes constrained state estimation of unmeasured traffic variable such as turning rates. The weighted constrained state approach uses moving horizon along the state trajectory, which permits to handle equality and inequality constraints belonging to nature of the traffic model. A numerical example illustrates the importance of constrained estimation and shows a possible switch control system between two control light stages.

关键词： State estimation

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Clustering-based framework for comparing fMRI analysis methods

Clustering-based framework for comparing fMRI analysis metho...

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IEEE International Symposium on Biomedical Imaging

作者： G.-A. Hossein-Zadeh A.-M. Golestani H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In this paper, a cluster-based framework is introduced for comparing analysis methods of functional magnetic resonance images (fMRI). In the proposed framework, fMRI data is replaced with a feature space and each method considered as a clustering method in the new space. As a result, different methods can be compared by means of a cluster validity measure. The feature space is computed using a non-parametric method (principal component analysis-PCA). Four subjects have been analyzed with three methods and the proposed cluster-based framework has evaluated performance of the methods. The results are identical to those of the modified receiver operating characteristics (ROC). This validates the proposed approach.

关键词： Clustering algorithms Algorithm design and analysis Image analysis Clustering methods Magnetic analysis Testing Accuracy Reproducibility of results Radiology Magnetic resonance

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Challenge of human factor influence for car safety

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Neural Network World 2004年第1期14卷 37-47页

作者： Novák, Mirko Votruba, Zdenëk Jt. Laboratory of System Reliability Faculty of Transportation Sciences Czech Technical University Prague Institute of Computer Science Acad. of Sci. of the Czech Republic Prague Department of Control Engineering Faculty of Transportation Sciences Czech Technical University Prague

The challenge of human factor influence for analyzing the reliability and safety of car transportation is discussed. An analysis of the density and the kind of use among the drivers in different regions and seasons of the year is made. The possibilities to detect the driver falling into a relaxant, somnolent or micro-sleep stage by the use of suitable combination of secondary factors are investigated. The operation of the driver in the moving car is an example of very complicated interaction between several very heterogeneous systems.

关键词： Motor transportation

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Multiresolution automatic segmentation of T1-weighted brain MR images

Multiresolution automatic segmentation of T1-weighted brain ...

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IEEE International Symposium on Biomedical Imaging

作者： M. Zeydabadi R.A. Zoroofi H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Automatic segmentation of brain tissues is crucial to many medical imaging applications. We use a multi-resolution analysis and a power transform to extend the well-known Gaussian mixture model expectation maximization based algorithm for segmentation of white matter, gray matter, and cerebrospinal fluid from T1-weighted magnetic resonance images (MRI) of the brain. Experimental results with near 4000 synthetic and real images are included. The results illustrate that the proposed method outperforms six existing methods.

关键词： Image resolution Image segmentation Biomedical imaging Multiresolution analysis Image analysis Magnetic analysis Algorithm design and analysis Brain modeling Magnetic liquids Magnetic resonance

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MRSI brain tumor characterization using wavelet and wavelet packets feature spaces and artificial neural networks

MRSI brain tumor characterization using wavelet and wavelet ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： A. Yazdan-Shahmorad H. Soltanian-Zadeh R.A. Zoroofi Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique for assessing biochemical fingerprint of tissue composition. The need to differentiate between normal and abnormal tissues and determine type of abnormality before biopsy or surgery motivated development and application of MRSI. There are several technical reasons that make the brain easier than other organs to be examined with MRSI. This work presents our proposed methods and results for the analysis of the brain spectra of patients with three tumor types (malignant glioma, astrocytoma, and oligodendroglioma). After extracting features from MRSI data using wavelet and wavelet packets, we use artificial neural networks to determine the abnormal spectra and the type of abnormality. We evaluated the proposed methods using clinical and simulated MRSI data and biopsy results. The MRSI analysis results were correct 97% of the time when classifying the spectra of the clinical MRSI data into normal tissue, tumor, and radiation necrosis. They were correct 72% and 83% of the time when determining tumor types using the clinical and simulated MRSI data, respectively.

关键词： Wavelet packets Neoplasms Artificial neural networks Biopsy Magnetic resonance Spectroscopy Magnetic resonance imaging Fingerprint recognition Surgery Cancer

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Activation detection in multi-subject studies of fMRI using GLRT

Activation detection in multi-subject studies of fMRI using ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： S.M. Shams G.A. Hossein-Zadeh H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Institute for Studies in Theoretical Physics and Mathematics University of Tehran School of Cognitive Sciences Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Institute for Studies in Theoretical Physics and Mathematics and Medical Image Analysis Laboratory Henry Ford Health System University of Tehran School of Cognitive Sciences Iran

A new method based on generalized likelihood ratio test (GLRT) for activation detection in multi-subject studies of functional MRI (fMRI) is proposed. In this method, we test the correlation between the fMRI time series of different subjects and the bases of a signal subspace which increases the flexibility of method in detecting different shapes of hemodynamic response. The proposed multivariate method can be applied to group studies where the conventional cross-correlation method cannot be used due to its univariate property. This method is applied to both experimental and simulated fMRI data and the results are compared to those of general linear model (GLM). We show that the proposed method detects more significant activated regions in analyzing experimental data and more true voxels in simulated data

关键词： Testing Hemodynamics Brain modeling Process control Intelligent control Physics computing Mathematics Time series analysis Magnetic resonance imaging Data analysis

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Implementation of a neural network based system for estimating the strength of a board using mixed signals

Implementation of a neural network based system for estimati...

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IEEE Pacific Rim Conference on Communications, computers and Signal Processing

作者： H. Saboksayr A. Saravi P.D. Lawrence F. Lam IEEE Control System Society Vancouver Canada Robotics and Control Laboratoryratory Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Robotics and Control Laboratory Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Department of Wood Science University of British Columbia Vancouver Canada

An intelligent lumber grading system was developed to provide a new way for estimating the strength of a board by posing the estimation problem as an empirical learning problem. This system processed the X-ray image, extracted geometric features (of 1000 boards that eventually underwent destructive strength testing), and predicted the strength of the lumber by using a neural network. The X-ray image was passed through a threshold filter to separate the knots based on the fact that a denser knot produces a local maximum (as a rounded protrusion in an otherwise almost flat density surface) of the X-ray image. Each knot was modeled by a three-dimensional-cone with seven parameters. Information on all the detected knots such as volume, and knot-area-ratio were fed to a processor to generate 16 geometrical features (such as; the average of knot area ratio, and the number of knots detected in each board), which characterize each board. Then by using back-propagation as the training method, cross correlation as the measure of accuracy, and actual strength of a thousand boards as the empirical data set, a neural network was trained to estimate the strength of each board. The learning system consisted of three layers, with 1, 5, 16 neurons in output, hidden and input layer respectively. Ten-fold cross validation was used to produce an unbiased accuracy of the estimation problem. The learning and testing sets comprised of 900 and 100 boards respectively. By repeating the learning and testing for ten times and averaging the results, a coefficient of determination of 0.4059 was reached in this study for using X-ray images alone. The same methodology was applied to MOE (modulus of elasticity) and a coefficient of determination of 0.56 was reached. The results were improved by fusing the X-ray image and MOE using a learning system consisting of three layers, with 1, 5, 40 neurons in output, hidden and input layer respectively. Ten-Fold cross validation resulted in a coefficient o

关键词： Neural networks X-ray imaging Feature extraction Learning systems Neurons Intelligent systems system testing Filters Character generation Elasticity

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Fuzzy classification system for outdoor video databases retrieval

Fuzzy classification system for outdoor video databases retr...

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ACS/IEEE International Conference on computer systems and Applications

作者： S.F. Khelifi M.E. Boudihir R. Nourine Department Computer sciences and information system King Faisal University Dammam Saudi Arabia Department Computer sciences and information system M. Ibn Saoud University Er Riad Morocco Intelligent Control and Electrical Power Systems Laboratory Research Centre Dr. Djillali Liabes University Algeria

Summary form only given. We propose a content-based video database retrieval system. The aim is to extract moving objects from video sequences in order to classify them into predefined categories according to image-based properties, and then robustly index them. The principal idea is to exploit useful properties of fuzzy geometry classification in order to provide a robust method to classify motion regions into three generic categories: pedestrian, vehicle or no identified object. The classified motion regions are used as templates for metrical training algorithms and as keys for tree indexing technique.

关键词： Fuzzy systems Information retrieval Image databases Robustness Spatial databases Content based retrieval Video sequences Geometry Vehicles Classification tree analysis

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A concept for On-Line Testing of Distributed Safety-Critical Supervisory systems

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IFAC Proceedings Volumes 2003年第3期36卷 175-180页

作者： István Varga Tamás Bartha Alexandros Soumelidis Béla Katics System and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences H-15l8 Budapest POB 63 Hungary Reactor Refutrbishment Project Nuclear Power Plant Ltd. H-7031 Paks POB 71 Hungary

This paper presents the concept and practical realization of the testing methodology of safety-critical supervisory systems. The test concept utilizes the highly redundant nature of these systems to conduct test during the normal operation of the monitored process. The described principles have been implemented in the form of microcomputer-based test systems.

关键词： Automatic testing Availability Error detection Implementation Nuclear plants

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Reinforcement learning algorithms for robotic navigation in dynamic environments

Reinforcement learning algorithms for robotic navigation in ...

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International Joint Conference on Neural Networks (IJCNN)

作者： G. Yen T. Hickey Intelligent System and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

An issue of primary importance in the field of reinforcement learning (RL) is the tradeoff between exploration and exploitation. Balancing exploration and exploitation becomes even more crucial in an intelligent agent designed to operate in dynamic environments. Three methods are proposed to improve the performance of traditional RL methods such as Q-learning. The proposed improvements are: the addition of a forgetting mechanism, the use of feature-based state inputs, and the addition of a hierarchical structure to an RL agent. Experimental results are presented and utilized in evaluation of the proposed methods. The proposed algorithms are compared to established methods and to theoretically optimal solutions.

关键词： Navigation Robot sensing systems Automatic control control systems Mobile robots Intelligent robots Machine learning algorithms Optimal control Orbital robotics Robot control

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