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Challenge of human factor influence for car safety

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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Human systems integration and advanced technology in engineering department workload and manpower reduction

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NAVAL ENGINEERS JOURNAL 2003年第1期115卷 57-65页

作者： Lively, KA Seman, AJ Kirkpatrick, M KENNETH A. LIVELY graduated from the University of Colorado with a BS in applied mathematics and an MS in mathematics in 1976 and from the Massachusetts Institute of Technology with an MS in electrical engineering and the degree ocean engineer in naval architecture and marine engineering in 1984. He retired from the U.S. Navy in 1989 after 23 years of service. Assignments included electrical officer on the USS Constellation (CV 64) project engineer for the DDG 51 machinery control system (NAVSEA) and DDG 51 Technical Director (NAVSEA). He was vice president of the PDI Division of Bird-Johnson Company from July 1989 to November 1998 where he managed various gas turbine and machinery controls related development projects. He joined Anteon Corporation's Systems Engineering Group as senior controls engineer in December 1998 where he provided technical support to the integrated power systems program (NAVSEA PMS 510) and managed the Office of Naval Research Afloat Laboratory. DR. MARK KIRKPATRICK is currently an independent consultant in human factors and work-load/manning analysis and modeling. He holds a Ph.D. degree in experimental psychology from The Ohio State University and has 34 years of experience in applied human factors. From 1982 through 2000 Dr. Kirkpatrick served as the senior vice president of Carlow International. Prior to joining Carlow in 1982 Dr. Kirkpatrick served as a member of the technical staff at North American Rockwell's Missiles Division and as a project director and vice president for Essex Corporation. His areas of expertise include workload simulation task analysis operator-in-the-loop simulation human performance experimentation statistical analysis and human factors T&E. He has directed and/or participated in human factors projects for the U.S. Navy U.S. Army NASA Department of Transportation the U.S. Nuclear Regulatory Commission and private industry. ANTHONY J. SEMAN III is the technical manager for the reduced ship's crew by virtual presence (RSVP) advanced technology d

Aboard current ships, such as the DDG 51, engineering control and damage control activities are manpower intensive. It is anticipated that, for future combatants, the workload demand arising from operation of systems under conditions of normal steaming and during casualty response will need to be markedly reduced via automated monitoring, autonomous control, and other technology initiatives. Current DDG 51 class ships can be considered as a manpower baseline and under Condition III typical engineering control involves seven to eight watchstanders at manned stations in the Central control Station, the engine rooms and other machinery spaces. In contrast to this manning level, initiatives such as DD 21 and the integrated engineering plant (IEP) envision a partnership between the operator and the automation system, with more and more of the operator's functions being shifted to the automation system as manning levels decrease. This paper describes some human systems integration studies of workload demand reduction and, consequently, manning reduction that can be achieved due to application of several advanced technology concepts. Advanced system concept studies in relation to workload demand are described and reviewed including. Piecemeal applications of diverse automation and remote control technology concepts to selected high driver tasks in current DDG 51 activities. Development of the reduced ship's crew by virtual presence system that will provide automated monitoring and display to operators of machinery health, compartment conditions, and personnel health. The IEP envisions the machinery control system as a provider of resources that are used by various consumers around the ship. Resource needs and consumer priorities are at all times dependent upon the ship's current mission and the availability of equipment pawnbrokers.

关键词： Naval warfare

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Modeling and simulation of hybrid dynamical systems with generalized differential Petri nets

Modeling and simulation of hybrid dynamical systems with gen...

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IEEE International Symposium on Intelligent control (ISIC)

作者： Yali Wu Weimin Wu Jianchao Zeng Guoji Sun Hongye Su Jian Chu System Engineering Institute Xi''an Jiaotong University Xi'an China Institute of Advanced Process Control National Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Division of System Simulation and Computer Application Taipan Heavy Machinery Institute Taipan China

There has been considerable interest in the modeling and simulation of hybrid dynamic systems (HDS). As an effective modeling tool of discrete event dynamic systems, Petri nets have a dual nature of a graphical tool and a mathematical object. In this paper, an extension of Petri net named generalized differential Petri nets (GDPN) is proposed to model HDS that have a combination of discrete-event evolution and continuous state evolution. Differential place and differential transition defined by Demongodin et al. (1998) are used to model the numeral simulation of the continuous dynamic process, and the weights defined on the directed arcs that connects the differential transitions and places are extended from real numbers to real matrices. The marking of differential place are also enlarged from real numbers to real vectors. Then the evolution rules and modeling method of HDS with GDPN are introduced. Two examples are used to illustrate the modeling power of GDPN in HDS.

关键词： Petri nets Power system modeling Supervisory control Discrete event systems Process control Computational modeling computer simulation Mathematical model Chemical processes Continuous time systems

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