Coordinated machine learning and decision support for situation awareness.

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Description

For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario.

Physical Description

46 p.

Creation Information

Draelos, Timothy John; Zhang, Peng-Chu.; Wunsch, Donald C. (University of Missouri, Rolla, MO); Seiffertt, John (University of Missouri, Rolla, MO); Conrad, Gregory N. & Brannon, Nathan Gregory September 1, 2007.

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Description

For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario.

Physical Description

46 p.

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Identifier

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  • Report No.: SAND2007-6058
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/920460 | External Link
  • Office of Scientific & Technical Information Report Number: 920460
  • Archival Resource Key: ark:/67531/metadc897226

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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Creation Date

  • September 1, 2007

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

Description Last Updated

  • Dec. 9, 2016, 7:53 p.m.

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Draelos, Timothy John; Zhang, Peng-Chu.; Wunsch, Donald C. (University of Missouri, Rolla, MO); Seiffertt, John (University of Missouri, Rolla, MO); Conrad, Gregory N. & Brannon, Nathan Gregory. Coordinated machine learning and decision support for situation awareness., report, September 1, 2007; United States. (digital.library.unt.edu/ark:/67531/metadc897226/: accessed November 18, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.