본 연구는 충돌 사고 중에서 정박지에서 대기하고 있는 선박과 이 정박지를 통항하는 선박 간 충돌사고가 자주 발생함에 따라, 정박선 사이를 통항하는 선박의 충돌위험을 예측할 수 있는 모델을 개발하기 위한 기초 연구로 통항 선박의 안전 영역을 도출하는 것이 목적이다. 이를 위해 우리나라 최대 항만인 부산항 남외항 정박지를 대상 해역으로 선정하고 정박선이 가장 많이 대기한 기간 VTS(Vessel Traffic Service) 항적 자료를 추출하여 분석하였다. 정박선 사이를 통항하는 선박의 길이(L)를 기준으로 정박선과 어느 정도의 안전한 거리 (D)를 두고 통과하는지를 알기 위하여 통항 선박의 방위별 D/L 비를 구하였다. D/L 비 분포의 평균 domain을 기준으로 기존 선박 domain 범위 안으로 정박선이 존재할 비율을 분석하여 VTS 관제사의 위험 정도를 반영한 domain을 도출하였다. 추후 연구로는 정박선 사이의 최소 안전거리인 Domain-watch와 정박지 통항 선박의 안전 domain을 활용한 정박지 통항 선박의 충돌위험도 평가 및 분석을 하고, 이를 통해 VTS가 정박지를 좀 더 효율적이고 안전하게 관리하기 위한 모델을 개발하고자 한다.

Even if only two ships are encountered, a collision may occur due to the mistaken judgment of the positional relationship. In other words, if an officer does not know a target ship’s intention, there is always a risk of collision. In this paper, the experiments are conducted to investigate how the intention affects the action of collision avoidance in cooperative and non-cooperative situations. In non-cooperative situation, each ship chooses a course that minimizes costs based on the current situation. That is, it always performs a selfish selection. In a cooperative situation, the information is exchanged with a target ship and a course is selected based on this information. Each ship uses the Distributed Stochastic Search Algorithm so that a next-intended course can be selected by a certain probability and determines the course. In the experimental method, four virtual ships are set up to analyze the action of collision avoidance. Then, using the actual AIS data of eight ships in the strait of Dover, I compared and analyzed the action of collision avoidance in cooperative and non-cooperative situations. As a result of the experiment, the ships showed smooth trajectories in the cooperative situation, but the ship in the non-cooperative situation made frequent big changes to avoid a collision. In the case of the experiment using four ships, there was no collision in the cooperative situation regardless of the size of the safety domain, but a collision occurred between the ships when the size of the safety domain increased in cases of non-cooperation. In the case of experiments using eight ships, it was found that there are optimal parameters for collision avoidance. Also, it was possible to grasp the variation of the sailing distance and the costs according to the combination of the parameters, and it was confirmed that the setting of the parameters can have a great influence on collision avoidance among ships.

Marine traffic engineering has been pushed to the limits due to a rising demand in the shipping business. Merchant ships are growing dramatically, both in numbers and in size. To keep pace with current developments, automation seems to be one viable option when it comes to keeping ships running with fewer seafarers available. The aim of this paper is to monitor a modern day mariners’ performance while working in a tense situation. The objective is to define the size of the safety domain whilst overtaking a vessel. The approach was to assess the ship’s domain area within a 3 nm wide traffic separation scheme by using a ship handling simulator. From the simulation results, an overtaking domain was determined as 1.36nm long and 0.4nm wide. Safety domains in real-life situations were experienced on a much smaller scale compared to the previous findings. The working load for this particular operation is expected to be stressful and highly skilled orientated.

In this study, a structural health monitoring methodology using acceleration responses is proposed for damage detection of a three-story prototype building structure during shaking table testing. A damage index is developed using the acceleration data and applied to outlier analysis, one of unsupervised learning based pattern recognition methods. A threshold value for the outlier analysis is determined based on confidence level of the probabilistic distribution of the acceleration data. The probabilistic distribution is selected according to the feature of the collected data.