Recent global warming has been accompanied by high water temperatures (HWTs) in coastal areas of Korea, resulting in huge economic losses in the marine fishery industry due to disease outbreaks in aquaculture. To mitigate these losses, it is necessary to predict such outbreaks to prevent or respond to them as early as possible. In the present study, we propose an HWT prediction method that applies sea surface temperatures (SSTs) and deep-learning technology in a long short-term memory (LSTM) model based on a recurrent neural network (RNN). The LSTM model is used to predict time series data for the target areas, including the coastal area from Goheung to Yeosu, Jeollanam-do, Korea, which has experienced frequent HWT occurrences in recent years. To evaluate the performance of the SST prediction model, we compared and analyzed the results of an existing SST prediction model for the SST data, and additional external meteorological data. The proposed model outperformed the existing model in predicting SSTs and HWTs. Although the performance of the proposed model decreased as the prediction interval increased, it consistently showed better performance than the European Center for Medium-Range Weather Forecast (ECMWF) prediction model. Therefore, the method proposed in this study may be applied to prevent future damage to the aquaculture industry.

In cold-region environments characterized by high wind speeds, low external air temperatures, and supercooled water droplets, wind turbines frequently experience ice formation along the leading edge of the blade tip, which has a higher angular velocity. This ice formation directly and negatively affects the wind turbine's power performance. As such, a clear understanding of the relationship between ice formation and wind turbine power performance is required. In this study, we used computational fluid dynamics (CFD) simulation methods based on the dispersed multiphase (DMP) droplet model to predict ice formation along a blade tip airfoil's leading edge. To validate the ice formation model, the CFD simulation results were compared with the previously published wind tunnel testing results. Following validation, the model was used to predict the ice formation along the leading edge of the National Advisory Committee for Aeronautics (NACA) 64-618 airfoil and estimate its aerodynamic performance under variable external environmental conditions. Steady power calculations for the National Renewable Energy Laboratory's (NREL) 5-MW wind turbine was performed using the BLADED software. The aerodynamic performance of the NACA 64-618 airfoil following ice formation and the power performance of the NREL 5-MW wind turbine were subsequently correlated. The results indicated that ice formation on the blade tip airfoil's leading edge caused wind turbine power performance to decrease by approximately 8–29%.

In this study, basic research was conducted regarding the era of autonomous vessels and artificial intelligence (deep learning, big data, etc.). When a vessel is navigating autonomously, it must determine the optimal route by itself and accurately follow the designated route using route-following control technology. First, the optimal route should be generated in a manner that ensures safety and reduces fuel consumption by the vessel. To satisfy safety requirements, sea depth, under-keel clearance, and navigation charts are used; algorithms capable of determining and shortening the distance of travel and removing unnecessary waypoints are used to satisfy the requirements for reducing fuel consumption. In this study, a reinforcement-learning algorithm-based machine learning technique was used to generate an optimal route while satisfying these two sets of requirements. Second, when an optimal route is generated, the vessel must have a route-following controller that can accurately follow the set route without deviation. To accurately follow the route, a velocity-type fuzzy proportional–integral–derivative (PID) controller was established. This controller can prevent deviation from the route because overshoot rarely occurs, compared with a proportional derivative (PD) controller. Additionally, because the change in rudder angle is smooth, energy loss by the vessel can be reduced. Here, a method for determining the presence of environmental disturbance using the characteristics of the Kalman filter innovation process and estimating environmental disturbance with a fuzzy disturbance estimator is presented, which allows the route to be accurately maintained even under conditions involving environmental disturbance. The proposed approach can automatically set the vessel’s optimal route and accurately follow the route without human intervention, which is useful and can contribute to maritime safety and efficiency improvement.

This paper deals with the attitude control of a towfish (underwater towed vehicle) with two elevators and a single rudder to improve the image quality of an attached sound navigation ranging (sonar) system. Image distortion can occur if the towfish shakes excessively. Since a towfish is connected to the mother ship through a towing cable and the motion of the towfish is affected not only by the motion of the cable, but also by the position of the center of gravity, towing point, and towing speed, it is necessary to analyze how these factors affect the towfish to appropriately control its attitude. In this study, a method for obtaining a feasible region of the towing point in accordance with the variations in the center of gravity and towing speed is proposed, and the feasible region obtained can ensure that pitch control can be achieved using the installed elevators. In addition, the allowable range of disturbances for yaw control was also investigated. Simulations were conducted using the dynamic models of the towfish and cable to check the obtained feasible region/range, and it was confirmed that there is a region/range where the attitude control can be carried out with relative ease.

The goal of underwater docking is to safely insert an autonomous underwater vehicle (AUV) into the docking sleeve of a docking station (DS). However, AUVs frequently experience disturbances in their operating environment under motional constraints owing to their shapes, which can significantly impede successful docking missions. Therefore, it is essential to develop an assessment method and corresponding index representing feasibility. In this study, we suggest a new assessment method and a probability-based assessment index to assess the underwater docking process, considering aforementioned motional constraints. The assessment is made for both the position and heading angle of the AUV, with the results presented in probabilistic figures. These figures are used to estimate the assessment index, which represents the probability of successful docking. The final decision on whether to dock or not can be made based on this index. When the index exceeds a predefined threshold, it indicates that the current docking process is reliable, and the docking will be successful. The suggested assessment method and the index were validated through tests conducted in various underwater environments. The results show that the probability-based index estimated through the proposed method can be grounds for successful docking.