• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1013-1022
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• 2022

Recently, with the worldwide development of the fourth industry, the development of technologies for smart and eco-friendly ships is accelerating. With the emergence of autonomous vessels with complete unmanned or minimum personnel on board and eco-friendly fuel (methane, ammonia, electricity, etc.), the role of existing seafarers on board is expected to change significantly. To improve the competitiveness of seafarers in the future, predicting future seafarer occupation groups, improving the educational curriculum, and creating an educational system are necessary. In this study, eight occupational groups that seafarers may have in the future were derived through a review of earlier studies and brainstorming of maritime university students, incumbent seafarers and expert groups. A survey was conducted on the eight occupational groups using the Likert scale, and based on the results, a leading occupational group related to future seafarer was derived. The most likely occupational groups with high scores were remote control centre operators and cargo remote manager. In addition, essential educational elements to be educated first for these occupational groups were derived and presented.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.2
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• pp.139-146
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• 2023

Maritime object detection systems, which detects small maritime obstacles such as fish farm buoys and visualizes distance and direction, is equipped with a 3-axis gimbal to compensate for errors caused by hull motion, but there is a limit to distance error corrections necessitated by the vertical movement of the camera and the maritime object due to wave motions. Therefore, in this study, the distance error of maritime object detection systems caused by the movement of the water surface according to the external environment is analyzed and corrected using average filter and moving average filter. Random numbers following a Gaussian standard normal distribution were added to or subtracted from the image coordinates to reproduce the rise or fall of the buoy under irregular waves. The distance calculated according to the change of image coordinates, the predicted distance through the average filter and the moving average filter, and the actual distance measured by laser distance meter were compared. In phases 1 and 2, the error rate increased to a maximum of 98.5% due to the changes of image coordinates due to irregular waves, but the error rate decreased to 16.3% with the moving average filter. This error correction capability was better than with the average filter, but there was a limit due to failure to respond to the distance change. Therefore, it is considered that use of the moving average filter to correct the distance error of the maritime object detection system will enhance responses to the real-time distance change and greatly improve the error rate.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.27-33
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• 2023

Recently, water shortages have occurred due to climate change, and the need for water management of agricultural water has increased due to the occurrence of algal blooms in reservoirs. Existing algae prevention is operated by putting many people on site and misses the optimal spraying time due to movement through boats. In order to solve this problem, it is necessary to block contamination in advance and move within time to uniformly spray complex microorganisms uniformly. Control drones are used for pesticide spraying and can be applied to algae prevention work by utilizing control drones. In this paper, basic research for the establishment of a marine control system was conducted for application to the reservoir environment, and as one of the results, the characteristics of a drone nozzle, a core technology that can be used for control drones, were calculated. In particular, it was found that the existing agricultural control drones had a disadvantage that the concentration was non-uniform within the suggested spraying interval, and to compensate for this, nozzle positioning and nozzle spraying uniformity were calculated. Based on the experimental results, we develop a core algorithm for establishing an algal bloom monitoring system in the reservoir environment and propose a precision control technology that can be used for marine control work in the future.