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