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Procedia Computer Science, volume 136, pages 321-330

Anomalies Detection in Metocean Simulation Results Using Convolutional Neural Networks

Publication typeJournal Article
Publication date2018-09-26
Quartile SCImago
Quartile WOS
Impact factor
ISSN18770509
General Medicine
Abstract
Metocean modeling with short output fields timestep, for example, hourly average fields output, generates a large number of pictures and requires extended computational power. Often, during the simulation different types of artifacts can appear due to the inappropriate model tuning or errors in boundary and initial data and, therefore, expert’s supervision and validation are required. When the number of images is increasing it becomes difficult or even impossible to check all output images manually. Therefore, it is required to use machine learning algorithms to reduce a time for expert’s validation. Thereby, it would be useful to develop a system that allows detecting anomalies in generated data automatically during the experiment. In the paper, we provide a method of anomalies detection for the geospatial data. Data in climatographic archives is available in restricted amount and therefore, full Arctic images are divided into sub-zones, which allows one to increase training set. Moreover, this division can be used to account for the spatial dependency, which is required for ice images. An advantage of the approach is the ability to detect anomalies completely in automatic mode without involving a domain expert and manual labeling.

Citations by journals

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Applied Sciences (Switzerland)
Applied Sciences (Switzerland), 1, 100%
Applied Sciences (Switzerland)
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Multidisciplinary Digital Publishing Institute (MDPI)
Multidisciplinary Digital Publishing Institute (MDPI), 1, 100%
Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 100%
1
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Vychuzhanin P. et al. Anomalies Detection in Metocean Simulation Results Using Convolutional Neural Networks // Procedia Computer Science. 2018. Vol. 136. pp. 321-330.
GOST all authors (up to 50) Copy
Vychuzhanin P., Hvatov A., Kalyuzhnaya A. V., Kalyuzhnaya A. Anomalies Detection in Metocean Simulation Results Using Convolutional Neural Networks // Procedia Computer Science. 2018. Vol. 136. pp. 321-330.
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RIS Copy
TY - JOUR
DO - 10.1016/j.procs.2018.08.282
UR - https://doi.org/10.1016%2Fj.procs.2018.08.282
TI - Anomalies Detection in Metocean Simulation Results Using Convolutional Neural Networks
T2 - Procedia Computer Science
AU - Vychuzhanin, Pavel
AU - Hvatov, Alexander
AU - Kalyuzhnaya, Anna V
AU - Kalyuzhnaya, Anna
PY - 2018
DA - 2018/09/26 00:00:00
PB - Elsevier
SP - 321-330
VL - 136
SN - 1877-0509
ER -
BibTex
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BibTex Copy
@article{2018_Vychuzhanin
author = {Pavel Vychuzhanin and Alexander Hvatov and Anna V Kalyuzhnaya and Anna Kalyuzhnaya},
title = {Anomalies Detection in Metocean Simulation Results Using Convolutional Neural Networks},
journal = {Procedia Computer Science},
year = {2018},
volume = {136},
publisher = {Elsevier},
month = {sep},
url = {https://doi.org/10.1016%2Fj.procs.2018.08.282},
pages = {321--330},
doi = {10.1016/j.procs.2018.08.282}
}
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