선박 접안속도의 K-평균 군집분석을 활용한 도선사 조선 유형에 관한 연구

• 영문명: A Study on the Pattern of Pilot’s Maneuvering using K-means Clustering of Ship’s Berthing Velocity
• 발행기관: 한국연안방재학회
• 저자명: 이형탁(Hyeong-Tak Lee) 이정석(Jeong-Seok Lee) 조장원(Jang-Won Cho) 양현(Hyun Yang) 조익순(Ik-Soon Cho)
• 간행물 정보: 『한국연안방재학회지』제7권 제4호, 221~232쪽, 전체 12쪽
• 주제분류: 사회과학 > 사회과학일반
• 파일형태: PDF
• 발행일자: 2020.10.30

영문 초록

The most important factor when a ship is berthing is the berthing velocity. Excessive berthing velocity may cause accidents in ports and ships, so it is necessary to be cautious. Since the ship’s berthing is completed by the pilot on board, the propensity of the pilots is closely related to the decision of the berthing velocity. Therefore, this study the pattern of pilot s maneuvering to berth the ship to prevent berthing accidents was analyzed by clustering. K-means algorithm was used as a clustering analysis method as one of the unsupervised learning of machine learning. The berthing velocity data were collected for the pilot cluster analysis, the data were collected for 39 months at a domestic tanker jetty. During the collection period, 47 pilots were surveyed who boarded the ship that berthed at the jetty. The 47 pilots were grouped by individual and analyzed basic data including the average and standard deviation of the berthing velocity. Based on the analyzed mean and standard deviation, the k-means algorithm was applied for the clustering of pilots. As a result, the pilots were clustered into four groups. Groups with low mean and standard deviation, medium mean but low standard deviation, groups with medium mean and standard deviation, and finally, group with relatively high mean and standard deviation. Based on the four groups, the pilots were divided into three patterns of maneuvering: Low, Moderate, and High Risk.

목차

1. 서 론

2. 연구방법

3. 기초 데이터 분석

4. K-평균 군집분석

5. 결론

References

키워드

K-means Algorithm Clustering Analysis Machine Learning Ship’s Berthing Velocity Pilots