International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Pattern Recognition of Ship Navigational Data Using Support Vector Machine

  • Kim, Joo-Sung (Department of Maritime Transportation System, Mokpo National Maritime University) ;
  • Jeong, Jung Sik (Department of Maritime Transportation System, Mokpo National Maritime University)
  • Received : 2015.11.18
  • Accepted : 2015.12.25
  • Published : 2015.12.25
Download PDF
⟨ Previous Next ⟩

Abstract

A ship's sailing route or plan is determined by the master as the decision maker of the vessel, and depends on the characteristics of the navigational environment and the conditions of the ship. The trajectory, which appears as a result of the ship's navigation, is monitored and stored by a Vessel Traffic Service center, and is used for an analysis of the ship's navigational pattern and risk assessment within a particular area. However, such an analysis is performed in the same manner, despite the different navigational environments between coastal areas and the harbor limits. The navigational environment within the harbor limits changes rapidly owing to construction of the port facilities, dredging operations, and so on. In this study, a support vector machine was used for processing and modeling the trajectory data. A K-fold cross-validation and a grid search were used for selecting the optimal parameters. A complicated traffic route similar to the circumstances of the harbor limits was constructed for a validation of the model. A group of vessels was composed, each vessel of which was given various speed and course changes along a specified route. As a result of the machine learning, the optimal route and voyage data model were obtained. Finally, the model was presented to Vessel Traffic Service operators to detect any anomalous vessel behaviors. Using the proposed data modeling method, we intend to support the decision-making of Vessel Traffic Service operators in terms of navigational patterns and their characteristics.

Keywords

References

  1. International Maritime Organization, Guidelines for Vessel Traffic Services [IMO Resolution A.857(20)]. London: International Maritime Organization, 1997.
  2. J. S. Kim, J. S. Jeong, and G. K. Park, "Prediction table for marine traffic for vessel traffic service based on cognitive work analysis," International Journal of Fuzzy Logic and Intelligent Systems, vol. 13, no. 4, pp. 315-323, 2013. http://dx.doi.org/10.5391/ijfis.2013.13.4.315
  3. J. S. Kim, J. S. Jeong, and G. K. Park, "Utilization of planned routes and dead reckoning positions to improve situation awareness at sea," International Journal of Fuzzy Logic and Intelligent Systems, vol. 14, no. 4, pp. 288-294, 2014. http://dx.doi.org/10.5391/ijfis.2014.14.4.288
  4. J. S. Kim, J. S. Jeong, and G. K. Park, "Application of planned routes and dead reckoning positions for supporting decision-making of vessel traffic services," in Proceeding of the International Symposium on Advanced Intelligent Maritime Safety and Technology, Mokpo, Korea, 2014, pp. 121-124.
  5. J. S. Kim, J. S. Jeong, G. K. Park, J. Y. Jung, J. H. Lee, T. Oh, and H. Han, "Extraction of ship's tracking model for improving situation awareness at vessel traffic service areas," in Proceeding of the Korean Institute of Intelligent Systems (KIIS) Spring Conference, Ansan, Korea, 2015, pp. 51-52.
  6. T. Hong, "Development of a system for transmitting a navigator's intention for safe navigation," International Journal of Fuzzy Logic and Intelligent Systems, vol. 14, no. 2, pp. 130-135, 2014. http://dx.doi.org/10.5391/ijfis.2014.14.2.130
  7. E. K. Kim, J. S. Jeong, G. K. Park, and 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. http://dx.doi.org/10.5391/ijfis.2012.12.4.285
  8. J. S. Jeong, K. I. Kim, and G. 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. http://dx.doi.org/10.5391/jkiis.2012.22.3.373
  9. B. T. Moon, Y. H. Ryu, and G. J. Joe, "Study on software architecture for intelligent vessel navigation analysis," Journal of Korean Institute of Intelligent Systems, vol. 22, no. 5, pp. 583-589, 2012. http://dx.doi.org/10.5391/jkiis.2012.22.5.583
  10. K. I. Kim, J. S. Jeong, and G. K. Park, "Assessment of external force acting on ship using big data in maritime traffic," Journal of Korean Institute of Intelligent Systems, vol. 23, no. 5, pp. 379-384, 2013. http://dx.doi.org/10.5391/jkiis.2013.23.5.379
  11. D. Y. Kim, G. K. Park, and H. Y. Kim, "A study on the ship information fusion with AIS and ARPA Radar using by blackboard system," Journal of Korean Institute of Intelligent Systems, vol. 24, no. 1, pp. 16-21, 2014. http://dx.doi.org/10.5391/jkiis.2014.24.1.016
  12. S. R. Gunn, "Support vector machines for classification and regression," School of Electronics and Computer Science, University of Southampton, UK, Technical Report, 1998.
  13. G. H. Kim, Y. W. Kim, S. J. Lee, and G. J. Jeon, "A hierarchical clustering method based on SVM for real-time gas mixture classification," Journal of Korean Institute of Intelligent Systems, vol. 20, no. 5, pp. 716-721, 2010. http://dx.doi.org/10.5391/jkiis.2010.20.5.716
  14. H. H. Choi, G. H. Lee, J. G. Kim, Y. B. Joo, B. J. Choi, K. H. Park, and B. J. Yun, "A defect inspection method in TFT-LCD panel using LS-SVM, " Journal of Korean Institute of Intelligent Systems, vol. 19, no. 6, pp. 852-859, 2009. http://dx.doi.org/10.5391/jkiis.2009.19.6.852
  15. V. N. Vapnik, Statistical Learning Theory. New York, NY: Wiley, 1998.
  16. T. H. Jo, "Modified version of SVM for text categorization," International Journal of Fuzzy Logic and Intelligent Systems, vol. 8, no. 1, pp. 52-60, 2008. http: //dx.doi.org/10.5391/ijfis.2008.8.1.052
  17. M. S. Kim and S. Y. Lee, "Pattern classification for biomedical signal using BP algorithm and SVM," Journal of Korean Institute of Intelligent Systems, vol. 14, no. 1, pp. 82-87, 2004. http://dx.doi.org/10.5391/jkiis.2004.14.1.082
  18. C. W. Hsu, C. C. Chang, and C. J. Lin, "A practical guide to support vector classification," Department of Computer Science, National Taiwan University, Taipei, Taiwan, 2003.
  19. J. S. Kim and J. S. Jeong, "Vessel trajectory and route detection in vessel traffic service areas using machine learning theories," in Proceedings of International Symposium on Advanced Intelligent Maritime Safety and Technology, Daejeon, Korea, 2015, pp. 142-145.
  20. J. S. Kim, J. S. Jeong, and J. Y. Jung, "Recreating ship's reference routes to predict traffic situations in vessel traffic service areas," in Proceeding of the Korean Society of Marine Environment and Safety (KOSOMES) Fall Conference, Busan, Korea, 2014, pp. 295-297.

Cited by

  1. Frequentist and Bayesian Learning Approaches to Artificial Intelligence vol.16, pp.2, 2016, https://doi.org/10.5391/IJFIS.2016.16.2.111
  2. FVID: Fishing Vessel Type Identification Based on VMS Trajectories pp.1993-5021, 2018, https://doi.org/10.1007/s11802-018-3717-1