1. Home
  2. Issues
  3. Volume 28, Issue 2 (2017)
  4. Integration of a Self-Organizing Map and ...

Informatica


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.

References

 
Alessandrini, A., Alvarez, M., Greidanus, H., Gammieri, V., Arguedas, V.F., Mazzarella, F., Santamaria, C., Stasolla, M., Tarchi, D., Vespe, M. (2016). Mining vessel tracking data for maritime domain applications. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, pp. 361–367.
 
Chen, C.-H., Khoo, L.P., Chong, Y.T., Yin, X.F. (2014). Knowledge discovery using genetic algorithm for maritime situational awareness. Expert Systems with Applications, 41(6), 2742–2753.
 
Dzemyda, G. (2001). Visualization of a set of parameters characterized by their correlation matrix. Computational Statistics & Data Analysis, 36(1), 15–30.
 
Dzemyda, G., Kurasova, O., Žilinskas, J. (2012). Multidimensional Data Visualization: Methods and Applications, Vol. 75. Springer Science & Business Media.
 
Handayani, D.O.D., Sediono, W., Shah, A. (2013). Anomaly detection in vessel tracking using Support Vector Machines (SVMs). In: 2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT). IEEE, pp. 213–217.
 
Jasinevicius, R., Petrauskas, V. (2008). Fuzzy expert maps for risk management systems. In: US/EU-Baltic International Symposium, 2008 IEEE/OES. IEEE, pp. 1–4.
 
Johansson, F., Falkman, G. (2007). Detection of vessel anomalies-a Bayesian network approach. ISSNIP 2007. In: 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. IEEE, pp. 395–400. 2007.
 
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.
 
Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J. (1996). Engineering applications of the self-organizing map. Proceedings of the IEEE, 84(10), 1358–1384.
 
Kowalska, K., Peel, L. (2012). Maritime anomaly detection using Gaussian Process active learning. In: 2012 15th International Conference on Information Fusion (FUSION). IEEE, pp. 1164–1171.
 
Kurasova, O., Molytė, A. (2011). Quality of quantization and visualization of vectors obtained by neural gas and self-organizing map. Informatica, 22(1), 115–134.
 
Lane, R.O., Nevell, D.A., Hayward, S.D., Beaney, T.W. (2010). Maritime anomaly detection and threat assessment. In: 2010 13th Conference on Information Fusion (FUSION). IEEE, pp. 1–8.
 
Liu, Y., Weisberg, R.H., Mooers, C.N. (2006). Performance evaluation of the self-organizing map for feature extraction. Journal of Geophysical Research: Oceans, 111(C5).
 
Liu, B., de Souza, E.N., Hilliard, C., Matwin, S. (2015). Ship movement anomaly detection using specialized distance measures. In: 2015 18th International Conference on Information Fusion (Fusion). IEEE, pp. 1113–1120.
 
Martineau, E., Roy, J. (2011). Maritime anomaly detection: domain introduction and review of selected literature. Technical Report, DTIC Document.
 
Mascaro, S., Nicholso, A.E., Korb, K.B. (2014). Anomaly detection in vessel tracks using Bayesian networks. International Journal of Approximate Reasoning, 55(1), 84–98.
 
Osekowska, E., Aselsson, S., Carlsson, B. (2013). Potential fields in maritime anomaly detection. In: Proceedings of the 3rd International Conference on Models and Technologies for Intelligent Transport Systems. TUD Press.
 
Perera, L.P., Oliveira, P., Soares, C.G. (2012). Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction. IEEE Transactions on Intelligent Transportation Systems, 13(3), 1188–1200.
 
Rhodes, B.J., Bomberger, N.A., Seibert, M., Waxman, A.M. (2005). Maritime situation monitoring and awareness using learning mechanisms. In: MILCOM 2005, Military Communications Conference. IEEE, pp. 646–652. 2005.
 
Riveiro, M.J. (2011). Visual analytics for maritime anomaly detection. PhD thesis, Örebro Universitet.
 
Riveiro, M., Falkman, G., Ziemke, T. (2008a). Improving maritime anomaly detection and situation awareness through interactive visualization. In: 2008 11th International Conference on Information Fusion. IEEE, pp. 1–8.
 
Riveiro, M., Johansson, F., Falkman, G., Ziemke, T. (2008b). Supporting maritime situation awareness using self organizing maps and gaussian mixture models. Frontiers in Artificial Intelligence and Applications, 173, 84.
 
Smith, M., Reece, S., Roberts, S., Psorakis, I., Rezek, I. (2014). Maritime abnormality detection using Gaussian processes. Knowledge and information systems, 38(3), 717–741.
 
Stefanovič, P., Kurasova, O. (2011). Influence of learning rates and neighboring functions on self-organizing maps. In: International Workshop on Self-Organizing Maps. Springer, pp. 141–150.
 
Stefanovic, P., Kurasova, O. (2014). Investigation on learning parameters of self-organizing maps. Baltic Journal of Modern Computing, 2(2), 45.
 
Venskus, J., Kurmis, M., Andziulis, A., Lukošius, Ž., Voznak, M., Bykovas, D. (2015). Self-learning adaptive algorithm for maritime traffic abnormal movement detection based on virtual pheromone method. In: 2015 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS). IEEE, pp. 1–6.
 
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J. (2000). SOM Toolbox for Matlab 5. Helsinki University of Technology, Finland.
 
Will, J., Peel, L., Claxton, C. (2011). Fast maritime anomaly detection using kd-tree gaussian processes. In: IMA Maths in Defence Conference.
 
Yingying, D., Yan, H., Jingping, J. (2003). Multi-robot cooperation method based on the ant algorithm. Proceedings of the 2003 IEEE. In: Swarm Intelligence Symposium, 2003, SIS’03. IEEE, pp. 14–18.
 
Zhen, R., Jin, Y., Hu, Q., Shao, Z., Nikitakos, N. (2017). Maritime anomaly detection within coastal waters based on vessel trajectory clustering and naïve bayes classifier. The Journal of Navigation, 1–23.
 
Zissis, D., Xidias, E.K., Lekkas, D. (2016). Real-time vessel behaviour prediction. Evolving Systems, 7(1), 29–40.

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.


Full article Related articles Cited by PDF XML
Full article Related articles Cited by PDF XML

Copyright
© 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

Metrics
since January 2020
1465

Article info
views

584

Full article
views

539

PDF
downloads

232

XML
downloads


Share


RSS