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Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic
Volume 28, Issue 2 (2017), pp. 359–374
Pub. online: 1 January 2017      Type: Research Article      Open accessOpen Access
Received
1 December 2016
Accepted
1 March 2017
Published
1 January 2017

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 Klaipėda 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.

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Biographies

Venskus Julius
julius.venskus@mii.stud.vu.lt

J. Venskus graduated from the Klaipeda University, Lithuania, in 2016 and received a master’s degree in informatics engineering. In 2016 he started doctoral (PhD) studies in informatics engineering at the Institute of Mathematics and Informatics, Vilnius University, Lithuania. He is a lead software developer at Flinke Folk AS and a lecturer of Informatics Engineering study programmes at Informatics and Statistics Department of Klaipeda University. His research interests include artificial intelligence, data mining, machine learning.

Treigys Povilas
povilas.treigys@mii.vu.lt

P. Treigys graduated from the Vilnius Gediminas Technical University, Lithuania, in 2005. In 2010 he received the doctoral degree in computer science (PhD) from Institute of Mathematics and Informatics jointly with Vilnius Gediminas Technical University. He is a member of the Lithuanian Society for biomedical engineering. His interests include: image analysis, detection and object’s feature extraction in image processing, automated image objects segmentation, optimization methods, artificial neural networks, and software engineering.

Bernatavičienė Jolita
jolita.bernataviciene@mii.vu.lt

J. Bernatavičienė graduated from the Vilnius Pedagogical University in 2004 and received a master’s degree in informatics. In 2008, she received the doctoral degree in computer science (PhD) from Institute of Mathematics and Informatics jointly with Vilnius Gediminas Technical University. She is a researcher at the System Analysis Department of Vilnius University, Institute of Mathematics. Her research interests include data bases, data mining, neural networks, image analysis, visualization, decision support systems and Internet technologies.

Medvedev Viktor
viktor.medvedev@mii.vu.lt

V. Medvedev received the doctoral degree in computer science (PhD) from Institute of Mathematics and Informatics jointly with Vilnius Gediminas Technical University in 2008. Currently, he is a researcher at the Institute of Mathematics and Informatics of Vilnius University. His research interests include artificial intelligence, visualization of multidimensional data, dimensionality reduction, neural networks, data mining and parallel computing.

Voznak Miroslav
miroslav.voznak@vsb.cz

M. Voznak obtained his PhD degree in telecommunications engineering in 2002 from the Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava and was appointed associate professor in 2009 based on his habilitation in the same faculty. Since 2013, he has led the Department of Telecommunications in the VSB-Technical University of Ostrava as department chair. He is an IEEE senior member and his interests are focused generally on information and communications technology, particularly on voice over IP, quality of experience, network security, wireless networks and also on Big Data analytics.

Kurmis Mindaugas
mindaugask01@gmail.com

M. Kurmis graduated from the Klaipeda University, Lithuania, in 2011 and received a master’s degree in informatics engineering. In 2016 received the doctoral degree (PhD) in informatics engineering at the Institute of Mathematics and Informatics, Vilnius University, Lithuania. He is a researcher at the Klaipeda University Open Access Center and head of Informatics Engineering study programmes at Informatics and Statistics department. His research interests include artificial intelligence, data mining, distributed systems.

Bulbenkienė Violeta
bulbenkiene@gmail.com

V. Bulbenkienė is a doctor of physical sciences, associate professor at Informatics and Statistics Department of Klaipeda University (Lithuania). She received her PhD degree in semiconductor physics in 1986 at Vilnius University. Her research interests include dynamic modelling of engineering systems, mobile technology, intelligent transportation/logistic systems, and network security.


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© 2017 Vilnius University
Open access article under the CC BY license.

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

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