Integration of a Self-Organizing Map and a Virtual Pheromone for Real Time Abnormal Movement Detection in Marine Traffic
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10236462" target="_blank" >RIV/61989100:27240/17:10236462 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/17:10236462
Result on the web
<a href="https://www.mii.lt/informatica/htm/INFO1145.htm" target="_blank" >https://www.mii.lt/informatica/htm/INFO1145.htm</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.15388/Informatica.2017.133" target="_blank" >10.15388/Informatica.2017.133</a>
Alternative languages
Result language
angličtina
Original language name
Integration of a Self-Organizing Map and a Virtual Pheromone for Real Time Abnormal Movement Detection in Marine Traffic
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LM2015070" target="_blank" >LM2015070: IT4Innovations National Supercomputing Center</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Informatica
ISSN
0868-4952
e-ISSN
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Volume of the periodical
28
Issue of the periodical within the volume
2
Country of publishing house
LT - LITHUANIA
Number of pages
16
Pages from-to
359-374
UT code for WoS article
000405641900007
EID of the result in the Scopus database
2-s2.0-85031682286