Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information

Title: Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information

Abstract

Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.

Inventors:: Hively, Lee M.

Issue Date:: 2014-09-16

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1158924

Patent Number(s):: 8,838,519

Application Number:: 13/646,081

Assignee:: UT-Battelle, LLC (Oak Ridge, TN)

DOE Contract Number:: AC05-00OR22725

Resource Type:: Patent

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Hively, Lee M. Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information.  United States: N. p., 2014. 
        Web.

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                    Hively, Lee M. Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information.  United States.

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                    Hively, Lee M. Tue .  
        "Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information".  United States.  https://www.osti.gov/servlets/purl/1158924.

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@article{osti_1158924,

  title        = {Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information},

  author       = {Hively, Lee M.},

  abstractNote = {Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2014},

  month        = {9}

}

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Works referenced in this record:

An optimal graph theoretic approach to data clustering: theory and its application to image segmentation
journal, January 1993

Wu, Z.; Leahy, R.
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, Issue 11
DOI: 10.1109/34.244673

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Title: Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information

Abstract

Citation Formats

An optimal graph theoretic approach to data clustering: theory and its application to image segmentation journal, January 1993

An optimal graph theoretic approach to data clustering: theory and its application to image segmentation
journal, January 1993