• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.163-171
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• 2024

Structural health monitoring for ships and offshore structures is important in various aspects. Ships and offshore structures are continuously exposed to various environmental conditions, such as waves, wind, and currents. In the event of an accident, immense economic losses, environmental pollution, and safety problems can occur, so it is necessary to detect structural damage or defects early. In this study, structural response data of multi-linked floating offshore structures under various wave load conditions was calculated by performing fluid-structure coupled analysis. Furthermore, the order reduction method with distortion base mode was applied to the structures for predicting the structural response by using the results of numerical analysis. The distortion base mode order reduction method can predict the structural response of a desired area with high accuracy, but prediction performance is affected by sensor arrangement. Optimization based on a genetic algorithm was performed to search for optimal sensor arrangement and improve the prediction performance of the distortion base mode-based reduced-order model. Consequently, a sensor arrangement that predicted the structural response with an error of about 84.0% less than the initial sensor arrangement was derived based on the root mean squared error, which is a prediction performance evaluation index. The computational cost was reduced by about 8 times compared to evaluating the prediction performance of reduced-order models for a total of 43,758 sensor arrangement combinations. and the expected performance was overturned to approximately 84.0% based on sensor placement, including the largest square root error.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.7
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• pp.851-857
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• 2019

This study is about ship horsepower prediction of machine learning method using the big data of ship. Currently, new ships use the ISO15016 method to predict external environmental resistance through mathematical equations but due to complicated equations and requires many input variables so it is less applicable to be used in ship. In this recent research, we propose a model capable of predicting ship performance with high performance using SVM (Support Vector Machine) algorithm which shows excellent performance in recent prediction and recognition. The proposed predictive model has the advantage of being able to predict better performance than ISO15016 only if secured big data is used. In this study, we compared the ISO15016 technique and the SVM algorithm-based horsepower analysis method using the 178K bulk carrier's voyage data to reduce ship model data preparation, which is a disadvantage of ISO15016, and improve inaccurate horsepower prediction performance.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.208-209
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• 2023

Intelligent maritime traffic information service for maritime traffic safety operates a service that provides safe and efficient optimal safety routes considering information such as water depth, maritime safety law, weather information, and fuel consumption. However, from a service user's point of view, they prefer a route that suits their personal navigation experience and style, such as unnecessary detours and conservative safety distances for maritime objects. In this study, the optimal safety route can be extracted based on the experience of service users without reflecting the separate maritime environment by adjusting the weight of the trunk line for the area where the ship frequently navigates with the ship's track data collected through LTE-M model was studied.

• Journal of Navigation and Port Research
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• v.44 no.6
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• pp.460-468
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• 2020

Research related to the technology developed for the Maritime Autonomous Surface Ship (MASS) is currently underway. Although one of those core technologies is collision-avoidance technology for ship operators at sea, no research has been done to objectively quantify its effectiveness. Therefore, this study was conducted to develop an evaluation model to examine the collision-avoidance ability of MASS. Ship-control experts performed a ship-handling simulation for each ship encounter type using the Full Mission Ship-handling Simulator (FMSS). We used the resulting data and technical statistics, to develop an evaluation model that utilized FMSS to quantify the operational capability of the collision-avoidance technology. This evaluation model also can be used at sea to assess deck officers' ability to use the technology and to improve and develop other MASS technologies.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.466-475
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• 2024

Matched-Field Processing (MFP) is a model-based approach that requires accurate knowledge of the ocean environment and array geometry (e.g., array tilt) to localize underwater acoustic sources. Consequently, it is inherently sensitive to model mismatches. In contrast, the waveguide invariant-based approach (also known as array invariant) offers a simple and robust means for source-range estimation in shallow waters. This approach solely exploits the beam angles and travel times of multiple arrivals separated in the beam-time domain, requiring no modeling of the acoustic fields, unlike MFP. This paper extends the waveguide invariant-based approach to shallow water environments featuring a shallow pit, where the waveguide invariant is not defined due to the complex bathymetry. An in-depth performance analysis is conducted using experimental data and numerical simulations.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.620-623
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• 2009

여러 해양에너지 중 유체의 빠른 흐름을 이용하는 조류발전은 서해안과 남해안에 적용하기에 적합하며 해양환경의 영향을 최소화 하면서 많은 에너지를 연속적으로 생산할 수 있는 장점이 있다. 조류발전에서 1차적으로 에너지를 변환시키는 로터는 조류발전시스템의 주요한 장치중의 하나로 여러 변수에 의해 그 성능이 결정된다. 블래이드 수, 형상, 단면적, 허브, 직경 등 여러 요소를 고려하여 로터를 설계하며, 설계정보와 실험데이터를 바탕으로 수치모델을 구현하여 실험에서 직접 계측할 수 없는 로터 주변의 유체현상 및 간섭영향 등을 예측할 수 있다. 본 논문에서는 변화하는 유속에 따른 HAT 로터의 시동속도, 회전수를 측정하여 로터 형상과 허브-직경비가 다른 로터의 성능을 고찰하고, 이를 수치모델로 구현하여 로터주변 유동변화를 연구하였다.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.265-282
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• 2022

Sea surface temperature (SST) is a factor that greatly influences ocean circulation and ecosystems in the Earth system. As global warming causes changes in the SST near the Korean Peninsula, abnormal water temperature phenomena (high water temperature, low water temperature) occurs, causing continuous damage to the marine ecosystem and the fishery industry. Therefore, this study proposes a methodology to predict the SST near the Korean Peninsula and prevent damage by predicting abnormal water temperature phenomena. The study area was set near the Korean Peninsula, and ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF) was used to utilize SST data at the same time period. As a research method, Long Short-Term Memory (LSTM) algorithm specialized for time series data prediction among deep learning models was used in consideration of the time series characteristics of SST data. The prediction model predicts the SST near the Korean Peninsula after 1- to 7-days and predicts the high water temperature or low water temperature phenomenon. To evaluate the accuracy of SST prediction, Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) indicators were used. The summer (JAS) 1-day prediction result of the prediction model, R²=0.996, RMSE=0.119℃, MAPE=0.352% and the winter (JFM) 1-day prediction result is R²=0.999, RMSE=0.063℃, MAPE=0.646%. Using the predicted SST, the accuracy of abnormal sea surface temperature prediction was evaluated with an F1 Score (F1 Score=0.98 for high water temperature prediction in summer (2021/08/05), F1 Score=1.0 for low water temperature prediction in winter (2021/02/19)). As the prediction period increased, the prediction model showed a tendency to underestimate the SST, which also reduced the accuracy of the abnormal water temperature prediction. Therefore, it is judged that it is necessary to analyze the cause of underestimation of the predictive model in the future and study to improve the prediction accuracy.

• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.435-444
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• 2009

In the case of oil spill accident at sea, information concerning the movement of spilled oil is important in making response strategies. Aircrafts and the satellites have been utilized for monitoring of spilled oil. In these days, numerical models are using to predict the movement of the spilled oil. In the future a coupling method of modeling and remote sensing data should be needed to predict more correctly the spilled oil. The purpose of this paper is to present an application of satellite image data to an oil spill prediction model as an initial condition. Environmental Fluid Dynamics Computer Code (EFDC) was used to predict the movement of the oil spilled from Hebei Spirit incident occurred in Taean coastal area on December 7,2007. In order to make the model initial condition and to compare the model results, two satellite images, KOMPSAT-2 MSC and ENVISAT ASAR obtained on December 8 and 11, were used during the period of the oil spill incident. The model results showed an improvement for the prediction of the spilled oil by using the initial condition deduced from satellite image data than the initial condition specified at the oil spill incident site in the respects of the distributed spilled area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.299-308
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• 2016

Various space geodetic techniques have been developed for highly precise and cost-efficient positioning solutions. By correcting the physical phenomena near the earth’s surface, the positioning accuracy can be further improved. In this study, the vertical crustal deformation induced by the ocean tide loading was accurately estimated through GNSS absolute and relative positioning, respectively, and the tidal constituents of the results were then analyzed. In order to validate the processing accuracy, we calculated the amplitude of eight major tidal constituents from the results and compared them to the global ocean tide loading model FES2004. The experimental results showed that absolute positioning and positioning done every hour during the observation time of 2 hours, which yielded an outcome similar to the reference ocean tide loading model, were better approaches for extracting tide constituents than relative positioning. As a future study, a long-term GNSS data processing will be required in order to conduct more comprehensive analysis including an extended tidal component analysis.