• Journal of Navigation and Port Research
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• v.48 no.4
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• pp.335-341
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• 2024

Recently, research, development, and commercialization of maritime autonomous surface ships (MASS) and remote control are in progress. Remote control is intended to secure autonomous navigation environments for existing ships or early-stage MASS using a remote control system (RCS). The main function of an RCS is to control MASS using data transmission between the MASS and the remote control centre. Remote control by a remote control officer also has an important function. The purpose of this study was to develop RCS and a performance evaluation technique for operation data provided by the RCS. The experiment was conducted during the navigation period of a training ship 'Hannara' after building experimental equipment at both an onshore remote control center and a training ship. As a result of evaluating data transmitted and received using the developed RCS, it was confirmed that data transmission was possible within an error range of 0.1%p. Fourteen types of ship information reflecting the navigation environment of the training ship were confirmed to be transmitted and received. The RCS developed in this work complies with the three principles of remote control: safety, reliability, and availability. This study provides a core technology for the development of RCSs for MASS and the evaluation of data transmission performance.

• 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.