• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.06a
• /
• pp.277-279
• /
• 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.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.30 no.5
• /
• pp.415-425
• /
• 2024

The global maritime industry is developing rapidly owing to the emergence of autonomous ship technology, and interest in utilizing artificial intelligence derived from marine data is increasing. Among the diverse technological developments, ship-route clustering is emerging as an important technology for the commercialization of autonomous ships. Through route clustering, ship-route patterns are extracted from the sea to obtain the fastest and safest route and serve as a basis for the development of a collision-prevention system. High-quality, well-processed data are essential in ensuring the accuracy and efficiency of route-clustering algorithms. In this study, among the various route-clustering methods, we focus on the ship-route-similarity-based clustering method, which can accurately reflect the actual shape and characteristics of a route. To maximize the efficiency of this method, we attempt to formulate an optimal combination of data-preprocessing technologies. Specifically, we combine four methods of measuring similarity between ship routes and three dimensionality-reducing methods. We perform k-means cluster analysis for each combination and then quantitatively evaluate the results using the silhouette index to obtain the best-performing preprocessing combination. This study extends beyond merely identifying the optimal preprocessing technique and emphasizes the importance of extracting meaningful information from a wide range of ocean data. Additionally, this study can be used as a reference for effectively responding to the digital transformation of the maritime and shipping industry in the Fourth Industrial Revolution era.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.35 no.1
• /
• pp.101-106
• /
• 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
• /
• v.13 no.1
• /
• pp.641-649
• /
• 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.