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INFORMATICA, 2017, Vol. 28, No. 2, 359-374
© Institute of Mathematics and Informatics,
DOI: http://dx.doi.org/10.15388/Informatica.2017.133

ISSN 0868-4952

Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic

Julius VENSKUS, Povilas TREIGYS1, Jolita BERNATAVICIENE, Viktor MEDVEDEV, Miroslav VOZNAK, Mindaugas KURMIS, Violeta BULBENKIENE

Institute of Mathematics and Informatics, Vilnius University, Akademijos 4, Vilnius, Lithuania, VSB-Technical University of Ostrava, 17. Listopadu 15/2172, Ostrava, Czech Republic, Klaipeda University, H. Manto str. 84, Klaipeda, Lithuania ^4Vilnius Gediminas Technical University, Sauletekio al. 11, Vilnius, Lithuania E-mail: julius.venskus@mii.stud.vu.lt, povilas.treigys@mii.vu.lt, jolita.bernataviciene@mii.vu.lt, viktor.medvedev@mii.vu.lt, miroslav.voznak@vsb.cz, mindaugask01@gmail.com, bulbenkiene@gmail.com

Abstract

In recent years, the growth of marine traffic in ports and their surroundings raise the traffic and security control problems and increase the workload for traffic control operators. The automated identification system of vessel movement generates huge amounts of data that need to be analysed to make the proper decision. Thus, rapid self-learning algorithms for the decision support system have to be developed to detect the abnormal vessel movement in intense marine traffic areas. The paper presents a new self-learning adaptive classification algorithm based on the combination of a self-organizing map (SOM) and a virtual pheromone for abnormal vessel movement detection in maritime traffic. To improve the quality of classification results, Mexican hat neighbourhood function has been used as a SOM neighbourhood function. To estimate the classification results of the proposed algorithm, an experimental investigation has been performed using the real data set, provided by the Klaipeda seaport and that obtained from the automated identification system. The results of the research show that the proposed algorithm provides rapid self-learning characteristics and classification.

Keywords:

marine traffic, abnormal vessel traffic detection, virtual pheromone, self-organizing map, neural network


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