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A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification Cover

A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification

Open Access
|Jun 2018

Abstract

Safety analysis of navigation over a given area may cover application of various risk measures for ship collisions. One of them is percentage of the so called near-miss situations (potential collision situations). In this article a method of automatic detection of such situations based on the data from Automatic Identification System (AIS), is proposed. The method utilizes input parameters such as: collision risk measure based on ship’s domain concept, relative speed between ships as well as their course difference. For classification of ships encounters, there is used a neuro-fuzzy network which estimates a degree of collision hazard on the basis of a set of rules. The worked out method makes it possibile to apply an arbitrary ship’s domain as well as to learn the classifier on the basis of opinions of experts interpreting the data from the AIS.

DOI: https://doi.org/10.2478/pomr-2018-0017 | Journal eISSN: 2083-7429 | Journal ISSN: 1233-2585
Language: English
Page range: 14 - 21
Published on: Jun 7, 2018
Published by: Gdansk University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2018 Rafał Szłapczyński, Tacjana Niksa-Rynkiewicz, published by Gdansk University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.