Knowledge fusion: Time series modeling followed by pattern recognition applied to unusual sections of background data

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Description

This report describes work performed during FY 95 for the Knowledge Fusion Project, which by the Department of Energy, Office of Nonproliferation and National Security. The project team selected satellite sensor data as the one main example to which its analysis algorithms would be applied. The specific sensor-fusion problem has many generic features that make it a worthwhile problem to attempt to solve in a general way. The generic problem is to recognize events of interest from multiple time series in a possibly noisy background. By implementing a suite of time series modeling and forecasting methods and using well-chosen alarm ... continued below

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39 p.

Creation Information

Burr, T.; Doak, J.; Howell, J.A.; Martinez, D. & Strittmatter, R. March 1, 1996.

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This report is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this report can be viewed below.

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Description

This report describes work performed during FY 95 for the Knowledge Fusion Project, which by the Department of Energy, Office of Nonproliferation and National Security. The project team selected satellite sensor data as the one main example to which its analysis algorithms would be applied. The specific sensor-fusion problem has many generic features that make it a worthwhile problem to attempt to solve in a general way. The generic problem is to recognize events of interest from multiple time series in a possibly noisy background. By implementing a suite of time series modeling and forecasting methods and using well-chosen alarm criteria, we reduce the number of false alarms. We then further reduce the number of false alarms by analyzing all suspicious sections of data, as judged by the alarm criteria, with pattern recognition methods. This report describes the implementation and application of this two-step process for separating events from unusual background. As a fortunate by-product of this activity, it is possible to gain a better understanding of the natural background.

Physical Description

39 p.

Notes

INIS; OSTI as DE96008300

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  • Other Information: PBD: Mar 1996

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  • Other: DE96008300
  • Report No.: LA--13075-MS
  • Grant Number: W-7405-ENG-36
  • DOI: 10.2172/215313 | External Link
  • Office of Scientific & Technical Information Report Number: 215313
  • Archival Resource Key: ark:/67531/metadc671434

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Creation Date

  • March 1, 1996

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

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  • Feb. 29, 2016, 3:51 p.m.

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Burr, T.; Doak, J.; Howell, J.A.; Martinez, D. & Strittmatter, R. Knowledge fusion: Time series modeling followed by pattern recognition applied to unusual sections of background data, report, March 1, 1996; New Mexico. (digital.library.unt.edu/ark:/67531/metadc671434/: accessed August 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.