Hidden Markov Model(HMM)-Based Fishing Activity Prediction Using V-Pass Data

Park, Ju-Han; Jeon, Ho-Kun; Yang, Chan-Su

doi:10.20481/kscdp.2021.8.4.221

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…For instance, a method for detecting the two most dangerous abnormal behaviors based on AIS was conducted in [14], a study establishing a major ship navigation route between ports based on the extracted ship orbiter cluster was conducted in [15], and a study suggesting a data preparation process for managing actual kinematic data and detecting fishing boats was conducted in [16]. Most studies on V-pass terminals used in most fishing boats focus only on V-pass data utilization; thus, few studies on the location accuracy-a crucial variableexist [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of GNSS Position Stored in Fishing Boat Location Transmitters in Comparison with That of DGPS Position

Jung

Park

Choi

2023

JMSE

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Fishing boat laws in Korea require fishing boats to be equipped with a location transmitter. Approximately 91% of registered fishing boats have V-pass terminals, whereas those with a gross tonnage of 10 t or more have either V-pass or automatic identification system (AIS) terminals (or use both). Most navigators, including the fishing boat location transmitter, rely on the satellite signals provided by the global navigation satellite system (GNSS) without considering the accuracy of the navigation instruments that indicate ship location. Many scholars are conducting research to analyze the accuracy of GNSS locations through continuous experiments; however, few of them focus on fishing boat location transmitters. Particularly, the location accuracy of V-pass equipment is unknown. Notably, the V-pass and AIS terminals used in fishing boats are mainly designed to locate fishing boats in need of assistance following a marine accident rather than to provide information on approaching ships and preventing collisions. Therefore, this study aimed to compare GNSS location storage data extracted from the location transmitter (V-pass and AIS terminal) of the accident fishing boat with the DGPS location information from the Electronic Chart Display and Information System to check the position error and use it for accident analysis and investigation.

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Section: Introductionmentioning

confidence: 99%

Accuracy of GNSS Position Stored in Fishing Boat Location Transmitters in Comparison with That of DGPS Position

Jung

Park

Choi

2023

JMSE

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“…Notably, no prior research has been conducted to validate the location accuracy of V-pass terminals, and the current research on the tracking data and system is limited, except for certain studies reported in the literature [4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Reliability of the Location Transmitted by V-Pass Terminals: Prompt Rescue of Fishing Vessels

Park

Jung

Choi

2023

JMSE

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Fishing boats are equipped to quickly rescue ships and save lives in the event of an incident at sea; therefore, determination of the incident location is imperative for a swift response. According to the 2021 marine accidents by ship use statistics, fishing and non-fishing boats accounted for 64.6 and 35.4%, respectively, of the total 3053 ships involved in an accident. In addition to V-pass and the Automatic Identification System (AIS), several other types of terminals exist; however, approximately 91% of all registered fishing boats use V-pass terminals. Therefore, it is essential to know the exact location of fishing boats. However, little research has been conducted on V-pass equipment. Therefore, in this study, marine experiments were conducted using V-pass and AIS terminals that are mainly used in fishing boats. To determine the exact location of an incident and rapidly respond and rescue, this study compared the data saved in the terminals with the data received by the Vessel Traffic Service center. In the event of a maritime incident, the radio shadow areas and causes of the error in the location transmitter must be investigated to quickly rescue the fishing boat and to determine the root cause of the incident, respectively.

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Classification of Ship Type from Combination of HMM–DNN–CNN Models Based on Ship Trajectory Features

Shin,

Yang

2024

Remote Sensing

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This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system.

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Hidden Markov Model(HMM)-Based Fishing Activity Prediction Using V-Pass Data

Cited by 3 publications

References 14 publications

Accuracy of GNSS Position Stored in Fishing Boat Location Transmitters in Comparison with That of DGPS Position

Accuracy of GNSS Position Stored in Fishing Boat Location Transmitters in Comparison with That of DGPS Position

Investigating the Reliability of the Location Transmitted by V-Pass Terminals: Prompt Rescue of Fishing Vessels

Classification of Ship Type from Combination of HMM–DNN–CNN Models Based on Ship Trajectory Features

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