PUBLICATION DATE

2017-05-04

AUTHORS

M. A. Krinitskiy

TITLE

Application of machine learning methods to the solar disk state detection by all-sky images over the ocean

ISSUE

2

VOLUME

57

ISSN (print)

0001-4370

ISSN (electronic)

1531-8508

ABSTRACT

A new approach to automatic solar disk state detection by all-sky images using machine learning methods is developed and implemented. The efficiency of the most widely used machine learning algorithms is analyzed. The effect of reducing the dimensionality of the feature space on the classification accuracy is estimated. The multilayer artificial neural network model has shown the best accuracy in terms of the true score. The operation result demonstrates the effectiveness of machine learning methods applied to solar disk state detection by all-sky images.

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33 TRIPLES      30 PREDICATES      33 URIs      21 LITERALS

Subject Predicate Object
1 articles:21908e9acf75c686c56eafc393449367 sg:abstract Abstract A new approach to automatic solar disk state detection by all-sky images using machine learning methods is developed and implemented. The efficiency of the most widely used machine learning algorithms is analyzed. The effect of reducing the dimensionality of the feature space on the classification accuracy is estimated. The multilayer artificial neural network model has shown the best accuracy in terms of the true score. The operation result demonstrates the effectiveness of machine learning methods applied to solar disk state detection by all-sky images.
2 sg:articleType OriginalPaper
3 sg:coverYear 2017
4 sg:coverYearMonth 2017-03
5 sg:ddsId S0001437017020126
6 sg:ddsIdJournalBrand 11491
7 sg:doi 10.1134/S0001437017020126
8 sg:doiLink http://dx.doi.org/10.1134/S0001437017020126
9 sg:hasContributingOrganization grid-institutes:grid.4886.2
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15 sg:hasJournalBrand journal-brands:84dbd3167a57e3310acc94a35c2a4954
16 sg:indexingDatabase Scopus
17 Web of Science
18 sg:issnElectronic 1531-8508
19 sg:issnPrint 0001-4370
20 sg:issue 2
21 sg:language English
22 sg:license http://scigraph.springernature.com/explorer/license/
23 sg:pageEnd 269
24 sg:pageStart 265
25 sg:publicationDate 2017-05-04
26 sg:publicationYear 2017
27 sg:publicationYearMonth 2017-05
28 sg:scigraphId 21908e9acf75c686c56eafc393449367
29 sg:title Application of machine learning methods to the solar disk state detection by all-sky images over the ocean
30 sg:volume 57
31 sg:webpage https://link.springer.com/10.1134/S0001437017020126
32 rdf:type sg:Article
33 rdfs:label Article: Application of machine learning methods to the solar disk state detection by all-sky images over the ocean
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