• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2022년도 춘계학술대회
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• pp.277-279
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• 2022

Rapid Development and adoption of AIS as a survailance tool has resulted in widespread application of data analysis technology, in addition to AIS ship trajectory clustering. AIS data-based clustering has become an increasingly popular method for marine traffic pattern recognition, ship route prediction and anomaly detection in recent year. In this paper we propose a route waypoint extraction by clustering ships CoG variance trajectory using Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm in both port approach channel and coastal waters. The algorithm discovers route waypoint effectively. The result of the study could be used in traffic route extraction, and more-so develop a maritime anomaly detection tool.

• 해양환경안전학회지
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• 제30권5호
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• pp.415-425
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• 2024

세계 해양산업은 자율운항선박 기술의 등장으로 급속도로 발전하고 있으며, 해양 데이터에서 파생된 인공지능 활용에 관한 관심이 높아지고 있다. 다양한 기술 발전 중에서 선박 항로 군집화는 자율운항선박 상용화를 위한 중요한 기술로 부각되고 있다. 항로 군집화를 통해 해상에서 선박 항로 패턴을 추출하여 가장 빠르고 안전한 항로를 최적화하고 충돌 방지 시스템의 개발에 기반이 된다. 항로 군집화 알고리즘의 정확성과 효율성을 보장하기 위해 고품질의 잘 처리된 데이터가 필수적이다. 본 연구에서는 다양한 항로 군집화 방법중 항로의 실제 형태와 특성을 정확히 반영할 수 있는 선박 항로 유사도 기반 군집화 방식에 주목하였다. 이러한 방식의 효율을 극대화하기 위해 최적의 데이터 전처리 기술 조합을 구성하고자 한다. 구체적으로, 4가지의 선박 항로 간 유사도 측정법과 3가지의 차원 축소 방법을 조합하여 연구를 진행하였다. 각 조합에 대해 k-means 군집 분석을 수행하고, 그 결과를 Silhouette Index를 통해 정량적으로 평가하여 최고 성능을 보이는 전처리 기법 조합을 도출하였다. 본 연구는 단순히 최적의 전처리 기법을 찾는 것에 그치지 않고, 광범위한 해양 데이터에서 의미 있는 정보를 추출하는 과정의 중요성을 강조한다. 이는 4차 산업혁명 시대의 해양 및 해운 산업이 직면한 디지털 전환에 효과적으로 대응하기 위한 기초 연구로서 의의를 갖는다.

• 산업경영시스템학회지
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• 제35권1호
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• pp.101-106
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• 2012

Ship handling simulator is a virtual ship navigating system with three dimensional screen system and simulation programs. FTS simulation can produce theoretically infinite experiment tests without time constraint, but which results in collecting determinstic observations. RTS simulation can collect statistical observations but has disadvantage of spending at least 30 minutes for a single experiment. The previous studies suggested that the number of experiment conditions to be tested could be reduced to obtain random data with RTS simulation by focusing on highly difficult experiment condition for ship handling. It has the limitation of not estimating the distribution of ship handling difficulty for the route. In this paper, similarity and clustering analysis are suggested for reduction methodology of experiment conditions. Similarity of experiment conditions are measured as follows: euclidean distance of ship handling difficulty index and correlation matrix of distance differences from the designed route. Clustering analysis and multi-dimensional scaling are applied to classify experiment conditions with measured similarity into reducing the number of RTS simulation conditions. An empirical result on Dangin harbor is shown and discussed.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.