AIS Big Data Framework for Maritime Safety Supervision

Bao, Kexin; Shang, Dongfang; Wang, Ruixi; Ma, Ruixin

doi:10.1109/icris52159.2020.00045

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…In particular, the vessel kinematic information, including latitude (lat), longitude (lon), speed over ground (SOG), and course over ground (COG), plays a critical role in evaluating optimal navigation routes, and predicting the future path of a vessel over specifi c time-series data based on relevant historical data requires analyzing an array of AIS data [13]. It is denoted by equations ( 1), ( 2), (3) [14][15] (1)…”

Section: Specifi C Time-series Data Based Ais / Specifi čNi Ais Temel...mentioning

confidence: 99%

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Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

Pham,

Dang,

Koboević

et al. 2024

Naše more

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High-speed passenger vessels, integrated river and sea vessels, container vessels, oil tankers, and other underwater vehicles operating in maritime traffic are among the types of vessels that must be equipped with AIS and VHF. The safety of navigation is one of the major problems in the maritime sector, particularly in Vietnam. Furthermore, marine traffic in the seaport zone is a common and difficult issue to manage in areas with a high volume of vessel traffic, mostly in places where the infrastructure supporting navigation is inadequately developed to meet the rapidly growing demands of the contemporary world. Therefore, it is necessary to create an integrated maritime management system to improve the efficiency of data exploitation and support maritime safety. To address this challenge, this study suggests a Maritime Traffic State Prediction (MTSP) model to predict traffic conditions in the channels where real-time data collection is insufficient in some specific locations. We recommend a deep learning method using Long Short-Term Memory (LSTM) networks to predict the safe path of the vessel in case of missing data segments. The findings have shown that the proposed approach encourages the mining of historical vessel data for maritime traffic, is ready to be applied, and can easily be implemented in a computer program or a web-based app.

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Section: Specifi C Time-series Data Based Ais / Specifi čNi Ais Temel...mentioning

confidence: 99%

“…The goal of the classifi cation challenge is to identify a variety model that allows the determination of the class to which the latest information belongs. In this section, we examine some modern analysis and application methods for processing maritime big data [6][7][8][9][10][11][12][13][14][15][16][17][18][19] as follows:…”

Section: Introduction / Uvodmentioning

confidence: 99%

Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

Pham,

Dang,

Koboević

et al. 2024

Naše more

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“…Sea exploration [1], marine safety [2], monitoring underwater [3], [4], [5] an internet of underwater thing [6] technologies have recently advanced at a fast rate. The used of radio frequency (RF) wave in underwater applications, allows flexibility in the deployment of communications [7].…”

Section: Introductionmentioning

confidence: 99%

Design of Normal Mode Helical Antenna for Seawater Application

Mohd Razali,

Ngah,

Kamardin

et al. 2023

IJIE

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To overcome the substantial propagation loss in the ocean, a low frequency band should be chosen when contemplating radio wave communication.Then, the antenna used at the portable radio equipment should be very small compare to the wavelength. For the miniaturized antenna, the normal mode helical antenna(NMHA) is suitable because of achieving high efficiency. In this paper, design method of NMHA in the seawater condition (r=81, =4) is explained. The frequency bands of 100MHz and 15MHz are selected. The self-resonant structures are determined based on the theoretical equation. Electrical characteristics such as resonant frequency, input impedance, VWSR and antenna efficiency are obtained through simulation by FEKO simulator. Electromagnetic simulation model is formed by taking into account the practical experimental condition. At 100MHz and 15MHz, resonances are ensured for antenna diameter lengths of 6.83 cm and 12.2 cm respectively. Input resistance of 27.15and 94.14, antenna efficiencies of -14.88dB and -26.27dB are achieved at 100MHz and 15MHz respectively.

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“…The machine learning deals also with a problem of prediction of quantitative and qualitative values. It is one of the typical task of machine learning tools (that also can be observed by view of data mining [2]). The problem of missing AIS data can be related to the problem of prediction of the incomplete data [17], lost due to packet collision.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for AIS Data Anomaly Detection Using Wavelet Transform

Szarmach

Czarnowski

2022

IEEE Access

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Thanks to the Automatic Identification System (AIS), ships and other maritime equipment are able to communicate with each other, for example, by sending information about their position. This solution allows for early collision detection when two or more ships are on a collision course. In the newer version of AIS, a satellite infrastructure is used to extend the communication range. Unfortunately, satellite AIS deals with so-called packet collision effect: since there is a problem with synchronizing AIS data coming from multiple terrestrial areas, a single satellite may receive several AIS messages at the same time and be unable to correctly process them, causing the data to get lost or garbled. In this article, a machine learning based framework for detecting the incorrect AIS data is presented. In this approach, after the first stage (clustering), a dedicated anomaly detection algorithm searches for damaged AIS messages and conducts multi-label classification (with Random Forest and wavelet transform) to decide which fields of such message requires further correction. The results of measuring the effectiveness of the proposed approach using real AIS data are presented.

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AIS Big Data Framework for Maritime Safety Supervision

Cited by 4 publications

References 10 publications

Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

Maritime Data Mining for Marine Safety Based on Deep Learning: Southern Vietnam Case Study

Design of Normal Mode Helical Antenna for Seawater Application

Multi-Label Classification for AIS Data Anomaly Detection Using Wavelet Transform

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