Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Assessment of External Force Acting on Ship Using Big Data in Maritime Traffic

해상교통 빅데이터에 의한 선박에 작용하는 외력영향 평가에 관한 연구

  • Kim, Kwang-Il (Department of Maritime Transportation System, Mokpo National Maritime University) ;
  • Jeong, Jung Sik (Division of International Maritime Transportation Science, Mokpo National Maritime University) ;
  • Park, Gyei-Kark (Division of International Maritime Transportation Science, Mokpo National Maritime University)
  • 김광일 (목포해양대학교 대학원 해상운송시스템학부) ;
  • 정중식 (목포해양대학교 국제해사수송과학부) ;
  • 박계각 (목포해양대학교 국제해사수송과학부)
  • Received : 2013.08.14
  • Accepted : 2013.09.17
  • Published : 2013.10.25
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Abstract

For effective ship management in VTS(Vessel Traffic Service), it needs to assess the external force acting on ship. Big data in maritime traffic can be roughly categorized into two groups. One is the traffic information including ship's particulars. The other is the external force information e.g., wind, sea wave, tidal current. This paper proposes the method to assess the external force acting on ship using big data in maritime traffic. To approach Big data in maritime traffic, we propose the Waterway External Force Code(WEF code) which consist of wind, wave, tidal and current information, Speed Over the Water(SOW) of each ship, weather information. As a results, the external force acting a navigating ship is estimated.

해상교통관제센터(Vessel Traffic Service, VTS)에서 항해중인 선박의 효과적인 관리를 위해 선박에 영향을 주는 외력에 대한 평가가 필요하다. 해상교통 빅데이터는 크게 선박 제원 및 통항정보 등 선박에 의하여 수집되는 정보가 있으며, 다른 하나는 해역에 관련된 바람, 파고, 조류흐름의 외력정보가 있다. 본 연구에서는 이러한 해상교통 빅데이터를 활용하여 선박에 영향을 주는 외력영향을 평가하는 방법에 대해 제안한다. 해상교통 빅데이터를 활용하기 위하여 바람, 파도, 조류 정보, 대수속력(Speed Over Water, SOW)에 대한 정보로 구성되는 해역외력코드(Waterway External Force Code)를 사용하였다. 해역외력코드를 데이터베이스로 하여 그 결과로서 선박에 작용하는 외력영향을 추정하였다.

Keywords

References

  1. J. S. Kim, A Study on The Utilization and Analysis Method of Big Data, Master Thesis, Korea University, 2013.
  2. J. D. Yun, The Theory and Practice of Ship Handling, Sejong Publishing Co. Ltd., pp. 192-204, 2013.
  3. K. I. Kim, J. S. Jeong, G. K. Park, "Extimation of External Force on Waterways Using Big Data in VTS,"13th Asian Conference on Maritime System and Safety Research, pp.165-169, 2013.
  4. IALA Guideline No. 1028, On The Automatic Ientification System (AIS) Volume 1, Part I Operational Issues, AISM IALA, December 2004.
  5. Jindo Marine Transport Facilities Management Office, Marine Weather and Tidal Current Information, Available : http://jindo.mltm.go.kr, 2010, [Accessed : Jul 25, 2013]
  6. H. S. Lee, D. H. Choi, J. C. Park, "Study on Tidal Current Simulation and its Application to Speed Trial around Straits of Korea," Journal of Ocean Engineers, Vol. 24, no. 6, pp 23-29, 2010.
  7. J. S. Jeong, K. I. Kim, K. K. Park, "A quantitative collision probability analysis in port waterway," Journal of Korean Institute of Intelligent Systems, vol. 22, no. 3, pp. 373-378, 2012. https://doi.org/10.5391/JKIIS.2012.22.3.373
  8. K. I. Kim, J. S. Jeong, K. K. Park, "A study on development of maritime traffic assessment model," Journal of Korean Institute of Intelligent Systems, vol. 22, no. 6, pp. 761-767p, 2012. https://doi.org/10.5391/JKIIS.2012.22.6.761
  9. W. J. Jhong, J. S. Lee, J. W. Ko, P. S. Park, "Fuzzy Logic Based Prediction of Link Travel Velocity Using GPS Information," Journal of Korean Institute of Intelligent Systems, vol. 12, no. 3, pp. 342-347p, 2003. https://doi.org/10.5391/JKIIS.2003.13.3.342
  10. D. Y. Kim, D. W. Jo, M. R. Yi, G. K. Park, "Intelligent Navigation Safety Information System based on Information-Fusion Techonology," Journal of Korean Institute of Intelligent Systems, vol. 20, no. 2, pp. 226-233p, 2010. https://doi.org/10.5391/JKIIS.2010.20.2.226
  11. E. K. Kim, J. S. Jeong, G. K. Park, N. K. Im, "Characteristics of Ship Movements in a Fairway," International Journal of Fuzzy Logic and Intelligent Systems, vol. 12, no. 4, pp. 285-289, 2012. https://doi.org/10.5391/IJFIS.2012.12.4.285

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