• Title/Summary/Keyword: 표류지점 예측모델

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Analysis of Drift Prediction Formula Used in the Search and Rescue Mission (수색구조 작업에 사용되는 표류지점 추정 공식 분석)

  • 강신영
    • Journal of Korean Port Research
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    • v.12 no.2
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    • pp.373-384
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    • 1998
  • In search and rescue mission the leeway formula based on the field experiments are utilized for the estimation of wind effect on distressed targets. This paper summarized the leeway formula from the available references. In the summary the environmental data collection method and experimental conditions are described along with the formula. Also the formula currently used in CASP of the U.S. Coast Gurard and CANSARP of the Canadian Coast Guard are discussed.

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Tracking Model of Drifted Ships for Search and Rescue (해상 수색구조를 위한 표류지점 신속추정모델 연구)

  • Lee Moonjin;Gong In-Young;Kang Chang-Gu
    • Journal of the Korean Society for Marine Environment & Energy
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    • v.2 no.2
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    • pp.78-85
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    • 1999
  • Tracking model of a drifted ship lot the search and rescue mission in southern sea of Korea is studied. In this model, search area is determined by considering standard deviation of position around reference point. The reference point is estimated for a given type of ship when marine environmental conditions such as wind and current are given. A database for environmental data, which is necessary for the real-lime tracking of drilled ship, is gel)elated on southern sea and western sea of Korea. Using this database, the real-time prediction of wind and current is possible. The simulated trajectories and search area of our model ate validated by comparing with reported real data.

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Development of a GUI Program for the Position Prediction of Distressed Vessel (조난 선박의 위치추정을 위한 GUI 프로그램 개발)

  • 강신영
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2002.11a
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    • pp.1-6
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    • 2002
  • To provide an easy operation of drift prediction model in SAR(search and rescue) mission a GUI program running on Window environment has developed. Users can make choice of input data on the screen by just clicking the mouse and the prediction results of datum points and trajectories of vessels are drawn on the map. The program contains both Leeway Equation model and mathematical model. The FORTRAN language was used in programming and Lehay Winteracter 4.0 software was utilized for graphic presentation. The result of May, 2001 Busan field experiment was plotted with that of model prediction for demonstration purpose.

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Development of a GUI Program for the Position Prediction of Distressed Vessel (조난 선박의 위치추정을 위한 GUI 프로그램 개발)

  • Kang, Sin-Young
    • Journal of Navigation and Port Research
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    • v.26 no.5
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    • pp.491-495
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    • 2002
  • To provide an easy operation of drift prediction model in SAR(search and rescue) mission a GUI program running on Windows environment has developed. Users can make choice of input data on the screen by just clicking the mouse and the prediction results of datum points and trajectories of vessels are drawn on the electric chart. The program contains both Leeway Equation model and Mathematical model. The FORTRAN language was used in programming and Lehay Winteraction 4.0 software was utilized for graphic presentation. The result of May, 2001 Busan field experiment was plotted with that of model prediction for demonstration purpose.

AI-Based Particle Position Prediction Near Southwestern Area of Jeju Island (AI 기법을 활용한 제주도 남서부 해역의 입자추적 예측 연구)

  • Ha, Seung Yun;Kim, Hee Jun;Kwak, Gyeong Il;Kim, Young-Taeg;Yoon, Han-Sam
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.34 no.3
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    • pp.72-81
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    • 2022
  • Positions of five drifting buoys deployed on August 2020 near southwestern area of Jeju Island and numerically predicted velocities were used to develop five Artificial Intelligence-based models (AI models) for the prediction of particle tracks. Five AI models consisted of three machine learning models (Extra Trees, LightGBM, and Support Vector Machine) and two deep learning models (DNN and RBFN). To evaluate the prediction accuracy for six models, the predicted positions from five AI models and one numerical model were compared with the observed positions from five drifting buoys. Three skills (MAE, RMSE, and NCLS) for the five buoys and their averaged values were calculated. DNN model showed the best prediction accuracy in MAE, RMSE, and NCLS.