Real-time monitoring of ship's dynamic behavior characteristics by AIS

Lee, Dae-Jae

doi:10.3796/ksft.2008.44.3.218

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…Dynamic data of Class-A AIS are received at every period of 10 s for vessels sailing below 14 knots. When changing course during vessels sailing below 14 knots, AIS data are received at every period of 3.3 s [27]. If the distance from the GPS position to the bridge pier is measured for the collected AIS data, an error may occur.…”

Section: Automatic Identification System (Ais) Datamentioning

confidence: 99%

An Investigation of the Ship Safety Distance for Bridges across Waterways Based on Traffic Distribution

Son

Lee

et al. 2020

JMSE

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For ship passage safety, a bridge across a waterway is a risk for accidents. However, no standard for the safe distance between a bridge and a ship is available in Korea. The UK MCA considers the 90% confidence interval of traffic distribution as the acceptable passage range, using it for measuring the separation between offshore wind farms. In this study, an optimal ship safety distance is proposed by evaluating traffic distribution at the Incheon and Busan harbor bridges and analyzing the confidence intervals. The results, based on the Z-score, reveal that at the Incheon bridge, all but one ship for 2-way departure were in circulation within the 95% confidence interval range, whereas at the Busan harbor bridge, six ships for arrival and two ships for departure were outside the 95% range. Based on the results of this study, the design of bridges across waterways can incorporate traffic distribution corresponding to each port.

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Section: Automatic Identification System (Ais) Datamentioning

confidence: 99%

An Investigation of the Ship Safety Distance for Bridges across Waterways Based on Traffic Distribution

Son

Lee

et al. 2020

JMSE

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An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm

Lee

Yang

et al. 2021

Applied Sciences

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As the maritime industry enters the era of maritime autonomous surface ships, research into artificial intelligence based on maritime data is being actively conducted, and the advantages of profitability and the prevention of human error are being emphasized. However, although many studies have been conducted relating to oceanic operations by ships, few have addressed maneuvering in ports. Therefore, in an effort to resolve this issue, this study explores ship trajectories derived from automatic identification systems’ data collected from ships arriving in and departing from the Busan New Port in South Korea. The collected data were analyzed by dividing them into port arrival and departure categories. To analyze ship trajectory patterns, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a machine learning clustering method, was employed. As a result, in the case of arrival, seven clusters, including the leg and turning section, were derived, and departure was classified into six clusters. The clusters were then divided into four phases and a pattern analysis was conducted for speed over ground, course over ground, and ship position. The results of this study could be used to develop new port maneuvering guidelines for ships and represent a significant contribution to the maneuvering practices of autonomous ships in port.

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Stabilization of ship's heading in AIS of fishing vessel by a hybrid GPS/EM compass

Lee¹,

Lee³

2010

Journal of the Korean society of Fisheries Technology

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The ship s heading information of hybrid GPS/EM compass were applied to the AIS of fishing vessel and examined the possibility of accuracy improvement. It is observed 453 each in AIS receiver of land among 3,982 each in AIS Rx/Tx transponder of the test ship at sea, and transmission interval according to the speed of ship is determined the 11.4% good transmitting data of the all information. In results, maximum compass error for the ship s heading of an EM compass was 19.1 . The variance of ship s heading owing to the speed of ship is surveyed. The COG (Course Over Ground) was changed extremely in 180 W-179 E range under 4.9knots, and in 24 W-23 E range over 4.9knots. Finally, using the ship s heading of EM compass and the COG from GPS for the autopilot system of a small fishing boat and the ship s heading information of AIS results in danger on the own ship s navigation safety and leads to make confusion both the others and VTS (Vessel Traffic Service) center. Therefore, the hybrid GPS/EM compass is identified as the best system for a small fishing boat and is allowed to offer continuously a ship s heading information with high accuracy and stability.

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Real-time monitoring of ship's dynamic behavior characteristics by AIS

Cited by 5 publications

References 4 publications

An Investigation of the Ship Safety Distance for Bridges across Waterways Based on Traffic Distribution

An Investigation of the Ship Safety Distance for Bridges across Waterways Based on Traffic Distribution

An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm

Stabilization of ship's heading in AIS of fishing vessel by a hybrid GPS/EM compass

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