• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.236-237
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• 2023

선박무선통신장비는 선박이 항해하는데 필요한 안전정보, 선박교통 모니터링 및 관제, 입·출항 정보를 교환하기 위한 필수 장비이므로 선박항해사는 무선통신 내용을 항상 주의 깊게 청취해야 함. 본 연구에서는 선박의 실제 음성 교신데이터 500시간 데이터를 수집 및 학습하고, Wav2Vec 및 Whisper 모델을 활용하여 한글 및 영어(해사영어) 음성인식 모델을 개발하고 실용화를 수행하였다. 음성인식 모델의 성능은 CER(Character Error Rate) 기준 94.5%로 향후 선박 운항 관련 댜양한 분야에 적용이 가능할 것으로 사료된다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.60-66
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• 2023

Currently, the maritime industry is focusing on developing technologies that promote autonomy and intelligence, such as smart ships, autonomous ships, and eco-friendly technologies, to enhance ship operational efficiency. Many countries are conducting research on different methods to ensure ship safety while increasing operational efficiency. This study aims to develop a real-time ship operational efficiency analysis model using data analysis methods to address the current limitations of the present technologies in the real-time evaluation of operational efficiency. The model selected ship operational efficiency factors and ship operational condition factors to compare the operational efficiency of the ship with present and classified factors to determine whether the present ship operational efficiency is appropriate. The study involved selecting a target ship, collecting data, preprocessing data, and developing classification models. The results of the research were obtained by determining the improved ship operational efficiency based on the ship operational condition factors to support ship operators.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.828-835
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• 2023

Vessel traffic service operators (VTSOs) need to quickly and accurately analyze the maritime traffic situation in the vessel traffic service (VTS) area and provide information to the vessels. However, if traf ic increases rapidly, the workload of VTSOs increases, and they may not be able to provide adequate information. Therefore, it is essential to develop VTSO support technologies that can reduce their workload and provide consistent information. In this paper, we propose a model for automatically detecting abnormal vessels in the VTS area. The proposed model consists of a positional model and a contextual model and is specifically optimized for the traffic characteristics of the target area. The implemented model was tested by using real-world data collected at a test center (Daesan Port VTS). Our experiments confirmed that the model could automatically detect various abnormal situations, and the results were validated through expert evaluation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.178-179
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• 2011

해양 분야에서는 최근 e-navigation 실현을 위한 차세대 표준에 관한 연구가 진행 중이다. IHO(국제수로기구)를 중심으로 전자해도뿐만 아니라 해양의 지리 정보 및 관련 데이터의 교환, 공유, 활용을 위해 S-100기반 다양한 표준이 가능하다. IHO S-100 표준은 ISO 19100 시리즈 표준의 프로파일로, 응용 분야와 서비스에 따른 S-10x 표준의 기반이 된다. 프로파일링을 통해 S-101, S-102, S-10x 등 다양한 표준 프로파일이 만들어지면 각 표준에서 정의하는 데이터 모델의 요소를 일관성 있고 명확하게 해석하는 것이 필요하다. 본 논문에서는 S-10x 표준의 기반이 되는 S-100 표준을 일관된 의미 해석과 처리를 할 수 있도록 S-100 표준의 온톨로지로의 변환 방안을 제시한다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.82-88
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• 2024

As the modern maritime industry rapidly progresses through technological advancements, data processing technology is emphasized as a key driver of this development. Natural language processing is a technology that enables machines to understand and process human language. Through this methodology, we aim to develop a model that predicts the proportions of outcomes when entering new written judgments by analyzing the rulings of the Marine Safety Tribunal and learning the cause-providing ratios of previously adjudicated ship collisions. The model calculated the cause-providing ratios of the accident using the navigation applied at the time of the accident and the weight of key keywords that affect the cause-providing ratios. Through this, the accuracy of the developed model could be analyzed, the practical applicability of the model could be reviewed, and it could be used to prevent the recurrence of collisions and resolve disputes between parties involved in marine accidents.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.183-186
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• 2001

For a long time, the ocean was regarded as unknown area ruled by god, consequently, not so much things were developed in scientific methods and activities. Despite a rapid development of geographic information systems(GIS) marine and ocean fields still remained in the scope of traditional tools and intuitive experiences by the late of 1990. However, land based concepts and technology models require additional customization to apply GIS effectively in marine domains, which are resulted from her dynamic, complex and seamless massive nature. This paper gives a brief review of marine spatial data characteristics and also presents strategic approaches to meet the unique marine GIS requirements.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.858-859
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• 2012

해상에서의 데이터는 디지털 수로데이터 교환 표준인 국제수로기구(IHO, International Hydrographic Organization)의 S-57을 기반으로 제작된다. 그러나 S-57은 지리정보 외의 정보를 표현하는데 한계를 나타내었다. S-57의 한계를 극복하기 위하여 다양한 해양데이터를 활용할 수 있는 시스템 체계로 수로 데이터를 위한 데이터 모델과 규격을 정의하는 S-100을 제정하였다. S-100을 기반으로 하여 수로데이터를 위한 표준으로 S-101을 제정하고 있으며, 데이터 검색 및 활용을 위한 메타데이터도 포함하여 구성하였다. S-101의 개발에 따라 S-57전자해도를 S-101전자해도로 변환이 요구되고 있으며, 이와 함께 메타데이터에 대한 변환도 필요하다. 본 논문에서는 S-57의 메타데이터 요소와 S-101의 메타데이터 요소를 비교 분석하여 메타데이터 변환모델을 제시하였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.50-64
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• 2019

According to the Maritime Safety Act, there are all 34 routes including 5 traffic safety zones, 3 traffic separation schemes, 26 routes designated by regional maritime affairs departments in the Republic of Korea. In the SOLAS convention, the route information should be is effectively used for the safe navigation. However, the route information is complicatedly composed of the location of the route, the navigation rule by each route, the restriction of the navigation, and the anchorages. Moreover, the present method of providing information using the navigational chart and other publications is not effective for users to grasp the navigational information. Therefore, it was conducted to study the design of the S-100 based routing information data model developed by the International Hydrographic Organization to find ways to more effectively provide route information. To do this, the analysis of route requirement, selection of items, encoding test and users' review were carried out. Through expert user review, it was evaluated that the study on the design of the route information data model can be utilized as a good basic data for the route information integration service. Future research on the development of route information data models is expected to provide integrated route information services.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.108-110
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• 2020

본 연구의 목적은 딥러닝 기법의 하나인 인공신경망 모델을 활용하여 선박의 가치평가 모델을 개발하는 것이다. 선박의 가치는 해운시장 변화와 밀접한 관계가 있으며, 경기 변동성이 크고 시장 민감성이 높은 해운시장의 특성상 가치의 불확실성 역시 높게 나타나고 있다. 이러한 선박가치의 중요성에도 불구하고 국내외적으로 선박가치평가의 체계 개선 및 평가모델의 객관성과 신뢰성을 제고시키기 위한 연구는 부족한 실정이다. 따라서 본 연구에서는 딥러닝 방법을 통해 선박의 가치를 산출하는 새로운 평가모델을 제시하고자 한다. 가치평가의 대상은 중고 VLCC선이며, 선행연구를 통해 선박의 가치 변화를 유발하는 주요 요인들을 선별하여 변수를 설정하고 2010년 1월부터 현재까지의 해당 데이터를 확보하였다. 교차검증을 통해 파라미터들을 추정하여 인공신경망의 최적 구조를 식별하고 이에 대한 객관성과 신뢰성을 검증한 결과 인공신경망 모델의 가치평가 정확성이 우수함을 확인하였다. 본 연구는 선박가치평가의 전통적 방법론에서 탈피하여 기계학습 기반의 딥러닝 모델을 활용한 측면에서 독창적인 의미가 있다.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.199-209
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• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.