# 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