• Title/Summary/Keyword: 항해 함수

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An Estimation of the Average Waiting Cost of Vessels Calling Container Terminals in Northern Vietnam (북베트남 컨테이너 터미널에 기항하는 선박의 평균대기비용 추정)

  • Nguyen, Minh-Duc;Kim, Sung-june
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.25 no.1
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    • pp.27-33
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    • 2019
  • Several studies have been completed on the topic of container terminals in Northern Vietnam. Few of them, however, deal with competition in terms of costs related to vessel waiting time or cargo handling. This paper estimates the average waiting cost per TEU for all the container terminals in Northern Vietnam. After average waiting time was first estimated by applying queuing theory, uncertainty theory was applied to estimated vessel daily cost. A simulation was performed to create a series of data representing waiting cost per TEU in relation to the rate of volume handled/capacity of each terminal. Non-linear regression based on this series was used to present a function for the relationship between the average waiting cost of each terminal and the rate of volume handled /capacity.

A Study on the Traffic Patterns of Dangerous Goods Carriers in Busan North and Gamcheon Port (부산 북항·감천항의 위험화물운반선 통항패턴에 관한 연구)

  • Kim, Jong-Kwan;Kim, Se-Won;Lee, Yun-Sok
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.23 no.1
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    • pp.9-16
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    • 2017
  • As a preliminary study of enter or leaving traffic patterns of the Korea main port, port Management Information System (Port-MIS) data was used to check the volume of vessels entering and leaving the port of Busan, and three consecutive days from each seasons were selected for study. Selected 12-day General Information Center on Maritime Safety & Security (GICOMS) data was also used to analyze the traffic pattern in the main traffic lane of Busan port for dangerous goods carrier. Also, the distance between dangerous goods carriers and Oryukdo breakwater of east breakwater in the main traffic lane was analyzed. Collision probability was estimated using the cumulative probability distribution function of the normal distribution for the maritime traffic safety audit scheme based on the assumption that a ship's trajectory has a normal distribution for a section of the route. However, in case of entry or leaving thorough the Oryukdo breakwater and entry thorough the east breakwater, ship's sailing trajectories were revealed not to follow a normal distribution via regularity testing using a KS-test and SW-test. Especially in the north port, the tendency of the right side of the ship to pass was remarkable. It is desirable to develop a traffic model suitable for the characteristics of the port rather than to apply general traffic theories, and to apply this model to a maritime traffic safety diagnosis, so further research is needed.

Estimation of Significant Wave Heights from X-Band Radar Based on ANN Using CNN Rainfall Classifier (CNN 강우여부 분류기를 적용한 ANN 기반 X-Band 레이다 유의파고 보정)

  • Kim, Heeyeon;Ahn, Kyungmo;Oh, Chanyeong
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.33 no.3
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    • pp.101-109
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    • 2021
  • Wave observations using a marine X-band radar are conducted by analyzing the backscattered radar signal from sea surfaces. Wave parameters are extracted using Modulation Transfer Function obtained from 3D wave number and frequency spectra which are calculated by 3D FFT of time series of sea surface images (42 images per minute). The accuracy of estimation of the significant wave height is, therefore, critically dependent on the quality of radar images. Wave observations during Typhoon Maysak and Haishen in the summer of 2020 show large errors in the estimation of the significant wave heights. It is because of the deteriorated radar images due to raindrops falling on the sea surface. This paper presents the algorithm developed to increase the accuracy of wave heights estimation from radar images by adopting convolution neural network(CNN) which automatically classify radar images into rain and non-rain cases. Then, an algorithm for deriving the Hs is proposed by creating different ANN models and selectively applying them according to the rain or non-rain cases. The developed algorithm applied to heavy rain cases during typhoons and showed critically improved results.