• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.95-109
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• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.261-262
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• 2023

자율운항선박의 도래와 예상 할 수 없는 선박의 구조 변화 및 적화 상태 변화로 인한 전복사고로부터 선박을 보호하기 위해서는 선박의 복원성 상태를 실시간으로 모니터링 것이 매우 중요합니다. 자율운항선박의 복원성 상태를 실시간으로 정밀 모니터링 시스템 개발은 운항자에게 사전에 위험을 경고하고 적화상태 보완 또는 평형수 상태 변형 등을 통하여 추가적은 복원성 확보를 위해서 필수적입니다. 본 연구는 실습선의 전자 경사계로 실시간 횡요 주기로 추정한 선박의 메타센터 높이(GM)의 정확도를 실험적으로 검증 하였습니다. 본 연구는 선박의 전자 경사계를 사용하여 선박의 복원성 안정성 상태를 실시간으로 평가하여 추정한 선박 GM의 정확도와 향후 연구에서 시스템 개선을 위한 요구 사항을 제시하였습니다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.369-370
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• 2022

최근 자율 운항 선박에 대한 관심이 높아지고 있다. 특히, MUNIN (Maritime Unmanned Navigation through Intelligence in Networks) 프로젝트를 계기로 자율 운항 선박에 대한 개발과 연구가 활발히 진행되고 있다. 또한 국제해사기구 IMO는 자율 운항 선박 시대에 대응하기 위해 자율 선박을 MASS (Maritime Autonomous Surface Ship)라 정의하고 선박 자율화 정도에 따라 4단계 등급을 제시하고 있다. 완전한 자율 운항 선박에 대한 요구조건을 만족하기 위해서는 항로 결정과 제어기술이 필수적이다. 본 연구에서는 여러 가지 기술 중 선박의 최적경로를 생성하는 기법을 다룬다. 기존에 최적항로를 생성하기 위한 방법으로는 A^*, Dijkstra와 같은 알고리즘들이 주로 사용되었다. 그러나 이와 같은 알고리즘은 섬이나 육지에 대한 충돌 회피는 고려하고 있지만 수심 및 연안 선박에 대한 규정들은 고려하지 않고 있어 실제로 적용하기에는 한계점이 있다. 따라서 본 연구에서는 안전을 위해 선박의 선저 여유 수심과, 해도에 규정되어 있는 선박 운항에 대한 여러 규정들을 반영하여 최적 항로를 생성하고자 한다. 최적 항로를 생성하기 위한 알고리즘으로는 강화학습 기반의 Q-learning 알고리즘을 적용하였다.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.5
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• pp.295-301
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• 2017

Recently, autonomous navigation technology, obstacle recognition, and obstacle collision avoidance technology are actively being developed for an unmanned surface vehicle (USV). The path to move from the current location to the destination should be planned, in order for an USV to autonomously operate safely to its destination. The dynamic window approach (DWA) is a well-known navigation scheme as a local path planning. The DWA algorithm derives the linear velocity and angular velocity by evaluating the destination direction, velocity, and distance from the obstacle. However, because DWA algorithm does not consider tracking the path, when using only the DWA algorithm, the ship may navigate away from the path line after avoiding obstacles. In this paper, we propose an improved DWA algorithm that can follow path line. And we implemented the simulation and compared the existing DWA algorithm with the improved DWA algorithm proposed in this paper. As a result, it is confirmed that the proposed DWA algorithm follows the path line better.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.117-126
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• 2020

A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1331-1340
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• 2020

Object detection in maritime means that the captain detects floating objects that has a risk of colliding with the ship using the computer automatically and as accurately as human eyes. In conventional ships, the presence and distance of objects are determined through radar waves. However, it cannot identify the shape and type. In contrast, with the development of AI, cameras help accurately identify obstacles on the sea route with excellent performance in detecting or recognizing objects. The computer must calculate high-volume pixels to analyze digital images. However, the CPU is specialized for sequential processing; the processing speed is very slow, and smooth service support or security is not guaranteed. Accordingly, this study developed maritime object detection software and implemented it with FPGA to accelerate the processing of large-scale computations. Additionally, the system implementation was improved through embedded boards and FPGA interface, achieving 30 times faster performance than the existing algorithm and a three-times faster entire system.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.726-734
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• 2019

The current maritime industry is expected to have a significant impact on the role of maritime-related technologies and systems, especially seafarers, in the rapidly changing Fourth Industrial Revolution. The Maritime Autonomous Surface Ship (MASS) aims to reduce the number of safety accidents and improve seafarers' working environment. With regard to MASS, the International Maritime Organization has been trying to minimize unexpected impact in the maritime education and training sector by establishing international conventions such as the Standards of Training, Certification and Watchkeeping for Seafarers. However, domestic designated educational institutions have not yet established an education and training scheme to develop seafarers who will be on board for MASS. Therefore, this paper reviews the technology of MASS, analyzes the changes in education and training in order to upgrade the qualifications, and suggests the competencies of smart seafarers equipped with the integrated management ability required for Artificial Intelligence, Big Data, Cybersecurity, and the Digital System Revolution through education and training. In addition, this study provides basic information for the education and training of seafarers who are optimized for the rapidly changing technological environment.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.6
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• pp.366-374
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• 2021

This paper presents the analysis of the speed-power performance in the real sea using a large container vessel data provided as a test bed from a shipping company. To perform a speed trial of the vessel during a commercial voyage, the on-board measuring device and various operation data acquisition systems were mounted on the vessel for long-term performance monitoring and the voyage operated under the container loading condition close to the design draft was adopted. The content of this paper consists of Part I and Part II. Part I, such as this paper, contains the speed trial method and analysis results of the operating vessel. Part II contains the analysis of the speed-power characteristics change over time and before and after hull cleaning using operation data measured from the voyage operated under a condition similar to the speed trial.

• 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.