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International Conference on computer and Information technology (CIT)

作者： Hirokazu Ozaki Atsusi Kara Zixue Cheng Broadband Networks Operations Unit NEC Corporation Limited Abiko Japan Nara National College of Technology Koriyama Japan Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan

In this paper we investigate the user-perceived availability and Mean Time to Failure (MTTF) of M-for-N shared protection systems with a finite number of repairers. We assume that there are M backup units, N mutually independent users' (working) units, and C repairers (1 les Cles M + N)We investigate a Markov-chain model (with failure rate lambda per unit, repair rate mu per repairer) that is subject to an FCFS (First Come, First Served) protection switching and FCFS unit re-housing discipline. When a working unit fails, the service for the user of the unit is protected by one of the M protection units if available. If the service is not protected, the user enters a queue waiting for the service restoration. On the other hand, a failed unit immediately enters another queue waiting for repairs. Namely, there are two kinds of queues in the system. An analysis based on state transition diagrams gives a closed-form solution of the user-perceived availability and a recurrence computation method of the use-perceived MTTF. Our numerical examples reveal the effects of the number of repairers.

关键词： Closed-form solution Time sharing computer systems Availability Protection switching Switches computer networks Information technology computer network reliability Broadband communication National electric code

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Enhancement of the classification of multichannel chromosome images using support vector machines

Enhancement of the classification of multichannel chromosome...

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

作者： P. S. Karvelis D. I. Fotiadis I. Georgiou P. Sakaloglou Department of Computer Science University of Ioannina (UoI) Ioannina Greece Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina (UoI) Ioannina Greece Department of Obsetetrics and Gynecology Medical School Ioannina Greece

Color chromosome classification (karyotyping) allows simultaneous analysis of numerical and structural chromosome abnormalities. The success of the technique largely depends on the accuracy of pixel classification. In this paper we present a method for multichannel chromosome image classification based on support vector machines. First, the image is segmented using a multichannel watershed segmentation method. Classification of the pixels of the segmented regions using support vector machines is then employed. The method has been tested on images from normal cells, showing the improvement in classification accuracy by 10.16% when compared to a Bayesian classifier. The increased classification improves the reliability of the M-FISH imaging technique in identifying subtle and cryptic chromosomal abnormalities for cancer diagnosis and genetic disorders research.

关键词： Biological cells Support vector machines Support vector machine classification Image segmentation Cancer Image color analysis Image classification Testing Bayesian methods Genetics

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Development of an automatic polarized microscopic imaging system for asbestos qualitative analysis

Development of an automatic polarized microscopic imaging sy...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Kuniaki Kawabata Yugo Tsubota Hirokazu Yamazaki Taketoshi Mishima Kazuhiro Hotta Hajime Asama Hiroshi Mizoguchi Haruhisa Takahashi Kawabata Intelligent System Research Unit RIKEN Wako Saitama Japan Department of Information and Computer Science Saitama University Japan Department of Information and Communication Engineering University of Electro-Communications Japan RACE University of Tokyo Japan Faculty of Science and Technology Tokyo University of Science Japan

This paper describes about an automated microscopic imaging system for supporting asbestos qualitative analysis. By JIS (Japanese Industrial Standards), the dispersion staining method is designated as a visual qualitative analysis for asbestos in the construction materials. In the analysis process using the microscope, the expert monitors the particles including asbestos fibers and count them. For supporting such observation process, we developed an automated microscopic image collecting system. It realizes to take the images of the target area and store them to the database automatically. In this paper, we introduce and report the prototype system and its performance.

关键词： Microscopy Image analysis Optical fiber polarization Building materials Inspection Dispersion Prototypes Lungs X-ray diffraction Optical imaging

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Model for Predictive Analysis of Heat Absorbed by Oxalic Acid Solution Relative to the Solution Temperature during Leaching of Iron Oxide Ore

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Journal of Minerals and Materials Characterization and Engineering 2009年第7期8卷 531-539页

作者： C. I. Nwoye C. S. Nwobodo C. Nlebedim U.C. Nwoye R. A. Umana G. C. Obasi 1Department of Materials and Metallurgical Engineering Federal University of Technology Owerri Nigeria. Data Processing Modelling and Simulation Unit Weatherford Nig. Ltd. Port-Harcourt Nigeria. Department of Material Science Aveiro University Portugal. Department of Material Science University of Cadiff Wales United Kingdom Department of Materials and Metallurgical Engineering Federal University of Technology Owerri Nigeria. Department of Mathematics and Computer Science Federal University of TechnologyOwerri Nigeria.

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Data-driven fault diagnosis of oil rig motor pumps applying automatic definition and selection of features

Data-driven fault diagnosis of oil rig motor pumps applying ...

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IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

作者： Estefhan Dazzi Wandekokem Frederico Thomaz de Aquino Franzosi Thomas Walter Rauber Flavio Miguel Varejao Rodrigo Jose Batista Department of Computer Science Technology Center Federal University Vitoria Spain Espirito santo Exploration and Production Business Unit Automation Inspection and Maitenance Engineering Petróleo Brasileiro S.A. Petrobras Vitoria Spain

We report about fault diagnosis experiments to improve the maintenance quality of motor pumps installed on oil rigs. We rely on the data-driven approach to the learning of the fault classes, i.e. supervised learning in pattern recognition. Features are extracted from the vibration signals to detect and diagnose misalignment and mechanical looseness problems. We show the results of automatic pattern recognition methods to define and select features that describe the faults of the provided examples. The support vector machine is chosen as the classification architecture.

关键词： Fault diagnosis Petroleum Shafts Vibrations Fault detection Frequency domain analysis Feature extraction Wavelet domain Signal generators Narrowband

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Precision deployment algorithm in mobile sensor network

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Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of technology 2009年第1期29卷 27-31页

作者： Yang, Ming-Hua Cao, Yuan-Da Tan, Li Yin, Lei Beijing Laboratory of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China Unit 96627 of PLA Beijing 100085 China

To implement precision deployment of mobile sensor network in an unknown environment, a virtual force based precision self-deployment algorithm (VFPSA) is proposed. By introducing the concept of "attracting force line" and constructing virtual attracting force between attracting force lines and repulsive force among nodes, the paths between sinks and targets are created automatically. Simulation results showed that the proposed algorithm, when compared to similar methods, provides a short average moving distance with a short maximum moving distance and limited convergence time.

关键词： Wireless sensor networks

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PERFORM: A system for the monitoring and management of persons with chronic motor neurodegenerative disorders: A study on Parkinson's Disease and Amyotrophic Lateral Sclerosis

PERFORM: A system for the monitoring and management of perso...

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International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth

作者： Dina Baga Dimitrios I. Fotiadis Spiros Konitsiotis Metin Akay Department of Computer Science Unit of Medical Technology and Intelligent Information Systems University of Ioannina (UoI) Ioannina Greece Medical School Department of Neurology University of Ioannina (UoI) Ioannina Greece Harrington Dept. of Bioengineering Arizona State University USA

In this work we describe a system for the monitoring and management of patients with neurodegenerative diseases, focusing on Parkinson's Disease and Amyotrophic Lateral Sclerosis. The system exploits a single wearable sensors' setting to detect and quantify all patient symptoms. An easy to use touchscreen interface allows patients and caregivers to provide additional useful information and assist patients to perform standard predefined tests which otherwise are performed in the clinician's office. The system exploits patient information to suggest appropriate treatment changes based on accumulated medical knowledge. In this paper the architecture of the system, as well as, its innovative features are presented.

关键词： Parkinson's disease Medical treatment Biomedical monitoring Patient monitoring Testing Performance evaluation technology management Wearable sensors Legged locomotion Signal analysis

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Correspondence to Sand et Al. "Critical reappraisal of a catechol-o-methyltransferase transversion variant in schizophrenia"

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Biological psychiatry 2010年第7期67卷 e45-8页

作者： Christina M Lill Brit-Maren M Schjeide Johannes T Roehr Ute Zauft Nicole C Allen Frauke Zipp Matthew B McQueen Fotini K Kavvoura John P A Ioannidis Muin J Khoury Rudolph E Tanzi Lars Bertram Department of Vertebrate Genomics Max-Planck Institute for Molecular Genetics Berlin Germany Department of Neurology University Medicine Mainz Johannes Gutenberg University Mainz Germany Department of Computer Science Free University of Berlin Berlin Germany New York College of Medicine New York NY Department of Integrative Physiology Institute for Behavioral Genetics University of Colorado Boulder CO Clinical and Molecular Epidemiology Unit Department of Hygiene and Epidemiology University of Ioannina School of Medicine Ioannina Greece Biomedical Research Institute Foundation for Research and Technology – Hellas Ioannina Greece Department of Medicine Tufts University School of Medicine Boston MA Harvard School of Public Health Boston MA National Office of Public Health Genomics Centers for Disease Control and Prevention Atlanta GA Genetics and Aging Research Unit Massachusetts General Hospital Charlestown MA

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Automatic feature definition and selection in fault diagnosis of oil rig motor pumps

Automatic feature definition and selection in fault diagnosi...

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IEEE Symposium on Industrial Electronics & Applications (ISIEA)

作者： E. D. Wandekokem T. W. Rauber F. M. Varejão R. J. Batista Department of Computer Science Technology Center Federal University of Espírito Santo Vitoria Espirito Santo Brazil Espírito Santo Exploration and Production Business Unit Automation Inspection and Maintenance Engineering Petróleo Brasileiro S. A. Petrobras Vitoria Espirito Santo Brazil

We present a collection of pattern recognition techniques applied to fault detection and diagnosis of motor pumps. Vibrational patterns are the basis for describing the condition of the process. We rely on the data-driven approach to the learning of the fault classes, i.e. supervised learning in pattern recognition. Our work is motivated by the diversity of the studied defects, the availability of real data from operational oil rigs, and the use of statistical pattern recognition techniques usually not explored sufficiently in similar works. We show the results of automatic methods to define, select and combine features that describe the process and to classify the faults on the provided examples. The support vector machine is chosen as the classification architecture.

关键词： Fault diagnosis Petroleum Fault detection Pumps Pattern recognition Vibrations Industrial electronics Supervised learning Support vector machines Support vector machine classification

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PERFORM: First steps in the assessment of patient motion status and support to treatment changes

PERFORM: First steps in the assessment of patient motion sta...

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IEEE EMBS International Conference on Information technology Applications in Biomedicine (ITAB)

作者： George A. Rigas Alexandros T. Tzallas Dina A. Baga Themis P. Exarchos Christos D. Katsis Dimitra A. Chaloglou Spiros Th. Konitsiotis Dimitrios I. Fotiadis Department of Computer Science University of Ioannina Ioannina Greece Unit ofMedical Technology and Intelligent Information Systems Department of Material Sciences and Engineering University of Ioannina (UoI) Ioannina Greece Department of Neurology Medical School University of Ioannina (UoI) Ioannina Greece ANCO S.A. Athens Greece

In the current work, a system for the monitoring, assessment and management of patients with chronic movement disorders such as Parkinson's disease (PD) is presented. The so called PERFORM system consists of the patient and the healthcare center subsystem. PERFORM monitors patient's motion status in daily activities, using a set of light wearable sensors. Based on the analysis of the acquired signals, PERFORM assesses PD symptoms and their severity, integrates patient's demographic, clinical and history data and proposes treatment plans based on advanced data mining algorithms. In this work we present two main modules of PERFORM system, the tremor assessment module and the data miner module.

关键词： Medical treatment Patient monitoring Parkinson's disease Medical services Wearable sensors Signal analysis Performance analysis Algorithm design and analysis Demography History

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