• 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.

• 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.

• Journal of the Korean Institute of Navigation
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• v.20 no.4
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• pp.1-6
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• 1996

A vessel in distress might be well identified when both the variables affecting the drifting of the vessel and the extent to which the variables affect the drifted vessel are known. And also the disembarking place inside the ship might be easily located if the drifting poised is forecasted. The forecasting method of the drifting poise is resolved by combining the vectors of the current and the wind. It is, however, very hard to forecast the effect of the wind, which should be mainly determined by field survey. This study aims at identifying the drifting characteristics of medium/large ships, considering only the effect of the wind. The experiment revealed the following results. $\circled1$ The drifting poise is determined by the aspect ratio of the ship and the shape of the superstructure of the ship. $\circled2$ Drifting direction is quite stable when wind speed goes over a certain level. $\circled3$ Drifting speed is 3-7% of the wind speed in case of T/S Hannara.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1451-1466
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• 2023

In the context of maritime emergencies, the utilization of drones has rapidly increased, with a particular focus on their application in search and rescue operations. Deep learning models utilizing drone images for the rapid detection of distressed vessels and other maritime drift objects are gaining attention. However, effective training of such models necessitates a substantial amount of diverse training data that considers various weather conditions and vessel states. The lack of such data can lead to a degradation in the performance of trained models. This study aims to enhance the performance of deep learning models for distress ship detection by developing a maritime environment simulator to augment the dataset. The simulator allows for the configuration of various weather conditions, vessel states such as sinking or capsizing, and specifications and characteristics of drones and sensors. Training the deep learning model with the dataset generated through simulation resulted in improved detection performance, including accuracy and recall, when compared to models trained solely on actual drone image datasets. In particular, the accuracy of distress ship detection in adverse weather conditions, such as rain or fog, increased by approximately 2-5%, with a significant reduction in the rate of undetected instances. These results demonstrate the practical and effective contribution of the developed simulator in simulating diverse scenarios for model training. Furthermore, the distress ship detection deep learning model based on this approach is expected to be efficiently applied in maritime search and rescue operations.