• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.258-260
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• 2023

목포해양대학교와 삼성중공업은 2021년 공동 연구를 통하여 '실습선 기반 원격/자율 운항 선박 시스템 개발 및 검증'을 수행하였고 이를 통하여 목포해양대학교 실습선인 세계로호를 활용하여 삼성중공업의 자율운항 선박 시스템(Autonomous Navigation System)인 SAS의 Test Bed 시연 실험 시스템을 구축하여 2022년 자율운항 선박(MASS) 기술 요소에 대한 시뮬레이션 기반 SAS(Samsung Autonomous Ship) System 검증 및 '운항 노선에 대한 설정을 통한 선박 자율운항 검증'을 위한 실선·실해역 실험을 통해 이를 시연 및 검증을 시행하였다. 특히 의미 있는 것은 자율운항선박 실선 테스트 과정 중 발생할 수 있는 모든 위험요소 분석을 위해 한국선급과 HAZID Workship을 실시하였고, 이를 통해 국내최초 자율운항실험용 기국 승인 및 선급임시검사 완료 후 실험이 진행되었다는 것이다. 이 연구에서는 실습선 기반 자율운항 선박 실해역 실험 연구를 소개하고 그 결과를 운항자 입장에서 분석하여 향후 보완하여야 할 사항을 제시하였다.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.507-513
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• 2018

The maritime autonomous surface ship is automatically collects and manages various information necessary for the operation to minimize human intervention and safely perform the mission assigned to the ship. And the ship may autonomously operate the partial or entire route to the destination determined by the ship himself. This ship navigation technology allows partially remote control the ship to be operated if necessary. The maritime autonomous surface ship (MASS) should collect and manage signals of various navigation communication equipments and engines mounted on the ship for safe operation. This requires a common platform technology. In this paper, we propose a common platform that is the core of smart ship implementation. Territorial authorities and ships are connected by satellite or terrestrial communication. In such a communication environment, information is exchanged smoothly in real time. This allows the onshore authorities to monitor ships and provide remote control to enable safe vessel navigation at sea.

• Journal of Navigation and Port Research
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• v.46 no.5
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• pp.435-440
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• 2022

The development and introduction of a Maritime Autonomous Surface Ship (MASS) are some of the most important changes leading to the fourth industrial era in the maritime area. The term 'MASS' refers to a ship operating independently, without human intervention, to reduce maritime accidents caused by human errors. Recent UK findings MASS also noted that particularly the dynamic positioning system will be considered to apply as newly function to a MASS. The DP system, a ship system developed decades ago and used for specific purposes like offshore operations, provides various functions to facilitate the accurate movements of the vessel, and operators can make decisions within the DP system, in addition to the ordinary ship system. In this paper, it would like to present the connection and application method with the main technical elements of the DP system in connection with the main technology of the DP system to achieve the safe operation of a MASS. In particular, among various position reference systems, the capability plot function of DP system, and the "follow target" mode in the operation mode are attractive functions that can contribute to the safe operation of autonomous ships.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.674-682
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• 2003

Ship autonomous navigation is designated as what computerizes mental faculties possessed of navigation experts, which are building navigation plans, grasping the situation, forecasting the fluctuation, and coping with the situation. An autonomous navigation system, which consists of several subsystems such as navigation system, a collision avoidance system, several data fusion systems, and a motion control system, is based on an intelligent control architecture for the sake of integrating the systems. The motion control system, which is one of the most essential system in autonomous navigation system, controls its propulsion and steering gears to move the ship satisfying its hydrodynamic characteristics. This paper is the study on the ship movement control system and its implementation which are totally developed and run on virtual-world system. Receiving the high-level control values such as a waypoint presented from the collision avoidance system, the motion control system generates them to low-level control values for propulsion and steering devices. In the paper, we develop a ship motion controller using Oldenburger's theory based on mathematical fundamentals, and simulate it with various scenarios in order to verify its performance.

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.173-173
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• 2018

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.79-79
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• 2018

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.78-78
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• 2018

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.149-149
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• 2017

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.158-159
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• 2018

It analyzed the domestic and overseas trends related to smart ship development in the world and identifies key functions required for smart vessels, especially autonomous vessels, and introduces key technologies that can be utilized in the development of INS(Integrated Navigation System) for autonomous vessels.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.221-227
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• 2024

In autonomous navigation systems, the need for fast and accurate image processing using deep learning and advanced sensor technologies is paramount. These systems rely heavily on the ability to process and interpret visual data swiftly and precisely to ensure safe and efficient navigation. Despite the critical importance of such capabilities, there has been a noticeable lack of research specifically focused on ship image classification for maritime applications. This gap highlights the necessity for more in-depth studies in this domain. In this paper, we aim to address this gap by presenting a comprehensive comparative study of ship image classification using two distinct neural network models: the Feedforward Neural Network (FNN) and the Convolutional Neural Network (CNN). Our study involves the application of both models to the task of classifying ship images, utilizing a dataset specifically prepared for this purpose. Through our analysis, we found that the Convolutional Neural Network demonstrates significantly more effective performance in accurately classifying ship images compared to the Feedforward Neural Network. The findings from this research are significant as they can contribute to the advancement of core source technologies for maritime autonomous navigation systems. By leveraging the superior image classification capabilities of convolutional neural networks, we can enhance the accuracy and reliability of these systems. This improvement is crucial for the development of more efficient and safer autonomous maritime operations, ultimately contributing to the broader field of autonomous transportation technology.