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A surveillance system of a military harbour using an automatic identification system

Published:28 June 2013Publication History

ABSTRACT

The goal of our research is the design and implementation of a surveillance system based on an Automatic Identification System (AIS). The AIS system enables operators in a control room to monitor ship movements in and along the harbour. There is a need to support human operators. The automated system is modelled after human operator. The transitions between the four steps Observation-Orientation-Decision-Action (OODA-loop) are realised using a rule based expert system and Bayesian networks. The design, implementation and results of experiments are reported in the paper.

References

  1. Boyd, J., R. "Destruction and Creation" US Army Command and General Staff College, September 3, 1976.Google ScholarGoogle Scholar
  2. Boullart, l., Krijgsman, A., Vingerhoeds, R. Application of Artificial Intelligence in Process Control, Elsevier Science Limited, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Vessel tracking and Automatic Transmitter Identification System (marine), http://en.wikipedia.org/wiki/Automatic_Identification_System.Google ScholarGoogle Scholar
  4. Hameete, P., Leysen, S., van der Laan, T., Lefter, I., Rothkrantz, L. 2012. Intelligent Multi-Camera Video Surveillance. International Journal on Information Technologies and Security (IJITS). 4: 51--62.Google ScholarGoogle Scholar
  5. Klein, G. 1999. Sources of Power: How People Make Decisions Cambridge, MA: MIT Press.Google ScholarGoogle Scholar
  6. Lefter, I., Rothkrantz, L., Somhorst, M. 2012. Automated safety control by video cameras. International Conference on Computer Systems and Technologies (CompSysTech'12).: 298--305. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Wiggers P., Mertens, B., Rothkrantz, L. 2011. Dynamic Bayesian Networks for Situational Awareness in the Presence of Noisy Data. Compsystech'11. 578: 411--416. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. www.maitimesecurityconference.org.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Other conferences
    CompSysTech '13: Proceedings of the 14th International Conference on Computer Systems and Technologies
    June 2013
    365 pages
    ISBN:9781450320214
    DOI:10.1145/2516775

    Copyright © 2013 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 28 June 2013

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    • research-article

    Acceptance Rates

    CompSysTech '13 Paper Acceptance Rate42of89submissions,47%Overall Acceptance Rate241of492submissions,49%

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