• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.554-561
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• 2022

Technology development related to maritime autonomous surface ships (MASS) is actively progressing around the world. However, since there are still many technically unresolved problems such as communication, cybersecurity, and emergency response capabilities, it is expected that it will take a lot of time for MASS to be commercialized. In this study, we proposed a ship group navigation system in which one leader ship and several follower ship are grouped into one group. In this system, when the leader ship begins to navigate, the follower ship autonomously follows the path of the leader ship. For path following, PD (proportional-derivative) control is applied. In addition, each ship navigates in a straight line shape while maintaining a safe distance to prevent collisions. Speed control was implemented to maintain a safe distance between ships. Simulations were performed to verify the ship group navigation system. The ship used in the simulation is the L-7 model of KVLCC2, which has related data disclosed. And the MMG (Maneuvering Modeling Group) standard method proposed by the Japan Society of Naval Architects and Ocean Engineering (JASNAOE) was used as a model of ship maneuvering motion. As a result of the simulation, the leader ship navigated along a predetermined route, and the follower ship navigated along the leader ship's path. During the simulation, it was found that the three ships maintained a straight line shape and a safe distance between them. The ship group navigation system is expected to be used as a navigation system to solve the problems of MASS.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.515-524
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• 2022

To effectively collect, manage, and share the maritime traffic information, it is necessary to identify the technology trends concerning this particular information and analyze its current status and problems. Therefore, this study observes the domestic and foreign technology trends involving maritime traffic information while analyzing and summarizing the current status and problems in collecting, managing, and sharing it. According to the data analysis, the problems in the collecting stage are difficulties in collecting visual information from long-distance radars, CCTVs, and cameras in areas outside the LTE network coverage. Notably, this explains the challenges in detecting smuggling ships entering the territorial waters through the exclusive economic zone (EEZ) in the early stage. The problems in the management stage include difficult reductions and expansions of maritime traffic information caused by the lack of flexibility in storage spaces mostly constructed by the maritime transportation system. Additionally, it is challenging to deal with system failure with system redundancy and backup as a countermeasure. Furthermore, the problems in the sharing stage show that it is difficult to share information with external operating organizations since the internal network is mainly used to share maritime transportation information. If at all through the government cloud via platforms such as LRIT and SASS, it often fails to effectively provide various S/W applications that help use maritime big data. Therefore, it is suggested that collecting equipment such as unmanned aerial vehicles and satellites should be constructed to expand collecting areas in the collecting stage. In the management and sharing stages, the introduction and construction of private clouds are suggested, considering the operational administration and information disclosure of each maritime transportation system. Through these efforts, an enhancement of the expertise and security of clouds is expected.

• Journal of Navigation and Port Research
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• v.47 no.1
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• pp.10-17
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• 2023

A series of studies on the development of autonomous surface ships have been promoted in domestic and foreign countries. One of the main technologies for the development of autonomous ships is path-following control, which is closely related to securing the safety of ships at sea. In this regard, the path-following performance of an autonomous ship should be first evaluated at the design stage. The main aim of this study was to develop a visual and quantitative evaluation method for the path-following control performance of an autonomous ship at the design stage. This evaluation technique was developed using a computational fluid dynamics (CFD)-based path-following control model together with a line-of-sight (LOS) guidance algorithm. CFD software was utilized to visualize waves around the ship, performing path-following control for visual evaluation. In addition, a quantitative evaluation was carried out using the difference between the desired and estimated yaw angles, as well as the distance difference between the planned and estimated trajectories. The results demonstrated that the ship experienced large deviations from the planned path near the waypoints while changing its course. It was also found that the fluid phenomena around the ship could be easily identified by visualizing the flow generated by the ship. It is expected that the evaluation method proposed in this study will contribute to the visual and quantitative evaluation of the path-following performance of autonomous ships at the design stage.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.343-350
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• 2024

Since the release of Meta's Segment Anything Model (SAM), a large-scale vision transformer generation model with rapid image segmentation capabilities, several studies have been conducted to apply this technology in various fields. In this study, we aimed to investigate the applicability of SAM for water bodies detection and extraction using the QGIS Geo-SAM plugin, which enables the use of SAM with satellite imagery. The experimental data consisted of Compact Advanced Satellite 500 (CAS500)-1 images. The results obtained by applying SAM to these data were compared with manually digitized water objects, Open Street Map (OSM), and water body data from the National Geographic Information Institute (NGII)-based hydrological digital map. The mean Intersection over Union (mIoU) calculated for all features extracted using SAM and these three-comparison data were 0.7490, 0.5905, and 0.4921, respectively. For features commonly appeared or extracted in all datasets, the results were 0.9189, 0.8779, and 0.7715, respectively. Based on analysis of the spatial consistency between SAM results and other comparison data, SAM showed limitations in detecting small-scale or poorly defined streams but provided meaningful segmentation results for water body classification.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.24-35
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• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.