한국항해항만학회지 (Journal of Navigation and Port Research)

  • 제43권3호
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  • Pages.160-165
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  • 2019
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  • 1598-5725(pISSN)
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  • 2093-8470(eISSN)
한국항해항만학회 (Korean Institute of Navigation and Port Research)
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은닉 마르코프 모델을 이용한 해양사고에 개입된 선원의 행동경로 추정

Estimating the Behavior Path of Seafarer Involved in Marine Accidents by Hidden Markov Model

  • 임정빈 (한국해양대학교 항해학부)
  • Yim, Jeong-Bin (Division of Navigation Science, Korea Maritime & Ocean University)
  • 투고 : 2019.03.15
  • 심사 : 2019.04.02
  • 발행 : 2019.06.30
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초록

선원의 행동은 해양사고에 있어서 주요한 원인이다. 본 연구에서는 은닉 마르코프 모델(Hidden Markov Model)에 기반하여 선원의 행동을 모델링하였다. 그런 후, 모델에서 추정한 행동의 경로분석을 통하여 어떠한 상황과 절차 그리고 오류에 의해서 해양사고가 발생되는지를 해석하였다. 모델 구현을 위하여, 선원의 행동을 해양안전심판원에서 간행된 재결 요약서에서 관측하였고, 관측한 결과는 SRKBB(Skill-, Rule-, and Knowledge-Based Behavior)를 기반으로 한 행동분류 프레임워크를 이용하여 HMM 학습에 적합한 행동 데이터로 변환하였다. 선박유형별 선원의 행동을 모델링한 결과, 선박 유형별로 차별성이 있음을 확인하였고, 선원이 우선적으로 행한 행동경로의 식별이 가능하였다. 연구 결과, 본 연구에서 제안한 모델링 기법은 선원의 행동경로 예측에 적용 가능할 뿐만 아니라 해양사고 예방에 필요한 선원 행동 보정을 위한 우선순위 결정에 기여할 수 있을 것으로 기대된다.

The conduct of seafarer is major cause of marine accidents. This study models the behavior of the seafarer based on the Hidden Markov Model (HMM). Additionally, through the path analysis of the behavior estimated by the model, the kind of situations, procedures and errors that may have caused the marine accidents were interpreted. To successfully implement the model, the seafarer behaviors were observed by means of the summarized verdict reports issued by the Korean Maritime Safety Tribunal, and the observed results converted into behavior data suitable for HMM learning through the behavior classification framework based on the SRKBB (Skill-, Rule-, and Knowledge-Based Behavior). As a result of modeling the seafarer behaviors by the type of vessels, it was established that there was a difference between the models, and the possibility of identifying the preferred path of the seafarer behaviors. Through these results, it is expected that the model implementation technique proposed in this study can be applied to the prediction of the behavior of the seafarer as well as contribute to the prioritization of the behavior correction among seafarers, which is necessary for the prevention of marine accidents.

키워드

GHMHD9_2019_v43n3_160_f0001.png 이미지

Fig. 1 Graphical explanation of Hidden Markov Model with 3 states

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Fig. 2 A framework for behavior classification

Table 1 Experimental data

GHMHD9_2019_v43n3_160_t0001.png 이미지

Table 2 Training and testing data for Supervised learning in HMM

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Table 3 Comparison results between five behaviour models

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Table 4 Part of estimation results of optimal states paths

GHMHD9_2019_v43n3_160_t0004.png 이미지

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