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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/17:10236462

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LM2015070" target="_blank" >LM2015070: IT4Innovations národní superpočítačové centrum</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Informatica

ISSN

0868-4952

e-ISSN

—

Svazek periodika

28

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

LT - Litevská republika

Počet stran výsledku

16

Strana od-do

359-374

Kód UT WoS článku

000405641900007

EID výsledku v databázi Scopus

2-s2.0-85031682286