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Collision Cause-Providing Ratio Prediction Model Using Natural Language Processing Analytics

자연어 처리 기법을 활용한 충돌사고 원인 제공 비율 예측 모델 개발

  • Ik-Hyun Youn (Division of Navigation & Information Systems, Mokpo National Maritime University) ;
  • Hyeinn Park (Department of Maritime Transportation System Mokpo National Maritime University) ;
  • Chang-Hee, Lee (Division of Marine Transportation, Mokpo National Maritime University)
  • 윤익현 (목포해양대학교 항해정보시스템학부 ) ;
  • 박혜인 (목포해양대학교 해상운송시스템학부) ;
  • 이창희 (목포해양대학교 해상운송시스템학부)
  • Received : 2024.01.04
  • Accepted : 2024.02.23
  • Published : 2024.02.28

Abstract

As the modern maritime industry rapidly progresses through technological advancements, data processing technology is emphasized as a key driver of this development. Natural language processing is a technology that enables machines to understand and process human language. Through this methodology, we aim to develop a model that predicts the proportions of outcomes when entering new written judgments by analyzing the rulings of the Marine Safety Tribunal and learning the cause-providing ratios of previously adjudicated ship collisions. The model calculated the cause-providing ratios of the accident using the navigation applied at the time of the accident and the weight of key keywords that affect the cause-providing ratios. Through this, the accuracy of the developed model could be analyzed, the practical applicability of the model could be reviewed, and it could be used to prevent the recurrence of collisions and resolve disputes between parties involved in marine accidents.

현대 해양 산업은 기술적 발전을 통해 신속한 발전을 이루고 있다. 이러한 발전을 주도하는 주요 기술 중 하나는 데이터 처리 기술이며, 이 중 자연어 처리 기법은 사람의 언어를 기계가 이해하고 처리할 수 있도록 하는 기술이다. 본 연구는 자연어 처리 기법을 통해 해양안전심판원의 재결서를 분석하여 이미 재결이 이루어진 선박 충돌사고의 원인 제공 비율을 학습한 후, 새로운 재결서를 입력하면 원인 제공 비율을 예측하는 모델을 개발하고자 하였다. 이 모델은 사고 당시 적용되는 항법과 원인 제공 비율에 영향을 주는 핵심 키워드의 가중치를 이용하여 사고의 원인 제공 비율을 계산하는 방식으로 구성하였다. 이 연구는 이러한 방식을 통해 제작한 모델의 정확도를 분석하고, 모델의 실무 적용 가능성을 검토함과 동시에 충돌사고 재발 방지 및 해양사고 당사자들의 분쟁 해결에 기여할 것으로 기대한다.

Keywords

References

  1. Choe, S. H.(2012), Smart port logistics technology leading the future, The Magazine of the IEIE, Vol. 39, No. 5, pp. 39-46. 
  2. Hyun, Y. G., J. H. Han, U.R. Chae, G. H. Lee and J. Y. Lee(2020), A Study On Technical Trend Analysis Related to Semantic Analysis of NLP Through Domestic/Foreign Patent Data, Journal of Digital Convergence, Vol. 18, No. 1, pp. 137-146.  https://doi.org/10.14400/JDC.2020.18.1.137
  3. Hwang, T. W., T. M. Hwang, and I. H. Yoon(2022), A Study on Improvement of Maritime Traffic Analysis Using Shape Format Data for Maritime Autonomous Surface Ships, Journal of the Korean Society of Marine Environment & Safety, Vol. 28, No. 6, pp. 992-1001.  https://doi.org/10.7837/kosomes.2022.28.6.992
  4. Jeong, H. M. and G. S. Kim(2016), Artificial intelligence technology trends using language processing, Weekly Technology Trends, Vol. 1741, pp. 12-24. 
  5. Jeong, S. H., J. H. Shim, and K. S. Choi(2018), The Common Platform Technology of Smart Maritime Autonomous Surface Ships, Proceedings of KIIT Conference. 
  6. Kim, J. and H. S. Jang(2019), Technology trends and preparations for autonomous ships, Journal of the Society of Naval Architects of Korea, Vol. 56, No. 4, pp. 4-7. 
  7. Kim, T. G. and S. H. Hong(2012), A Study on the System for Calculating the Proportion of Causes of Ship Collisions, Journal of Korean Navigation and Port Reserch, pp. 180-182. 
  8. Kim, Y. G. and D. H. Lee(2023), Method for evaluating interoperability of weapon systems applying natural language processing techniques, Journal of the Korean Society of Defense Technology, Vol. 5, No. 3, pp. 8-17.  https://doi.org/10.52682/jkidt.2023.5.3.8
  9. Kwon, S. J., Y. H. Kang, Y. H. Lee, M. H. Lee, S. H. Park and M. J. Kang(2020), Analysis of disaster and safety situation classification algorithm based on natural language processing using 119 report data, KIPS Transactions on Software and Data Engineering, Vol. 9, No. 10, pp. 317-322.  https://doi.org/10.3745/KTSDE.2020.9.10.317
  10. Lee, D. Y.(2018), Natural Language Processing Research, Korea Information Science Society, pp. 771-1773 
  11. Lee, J. W., S. Y. Kim, Y. G. Park, and C. J. Park(2023), An Approach to Defense Information Analysis Utilizing Natural Language Processing Technology, Defense and Technology, Vol. 536, pp. 132-137. 
  12. Lee, S. H., S. G. Baek, and J. S. Park(2015), A Study on Natural Language Process Methods for Unmanned Air Vehicle Control, The Korean Society for Aeronautical and Space Sciences conference abstracts, pp. 2086-2089. 
  13. Lee, T. H.(2020), Analysis of overseas cases of smart ports and policy implications: Focusing on Europe and Singapore, Journal of Korean Port Economics, Vol. 36, No. 1, pp. 77-89.  https://doi.org/10.38121/kpea.2020.03.36.1.77
  14. Noh, B. S. and S. Y. Kang(2021), A Statistical Analysis of the Causes of Marine Incidents occurring during Berthing, Journal of Korean Navigation and Port Reserch, Vol. 45, No. 3, pp. 95-101. 
  15. Park, Y. S.(2015), A Comparative Study on the Contributory Ratios of Fault in Civil Courts and the Causation Ratios in Maritime Safety Tribunal in Ship Collision Accidents, Korean Journal of Maritime Law, Vol. 37, No. 2, pp. 207-246 
  16. Park, Y. S.(2016), A Study on Improvement Measures for Contributory Ratios in Collision Accidents between Navigating and Moored Vessels, Maritime Law Research, Vol. 28, No. 2, pp. 173-204. 
  17. Weihong, Y. U., F. U. Piaoyun, R. E. N. Yue, and W. A. N. G. Qingwu(2021), Text Mining for Causes of Ship Accidents Based on PMI and BTM., Journal of Transport Information and Safety, Vol. 39, No. 1, pp. 35-44.