Application of deep learning methods to predict ionosphere parameters in real time
In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2020-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/56/e3sconf_strpep2020_02007.pdf |
| _version_ | 1819291334520864768 |
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| author | Mochalov Vladimir Mochalova Anastasia |
| author_facet | Mochalov Vladimir Mochalova Anastasia |
| author_sort | Mochalov Vladimir |
| collection | DOAJ |
| description | In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown. |
| first_indexed | 2024-12-24T03:36:59Z |
| format | Article |
| id | doaj.art-e5c6b089d0cd4b7e9b88557a3052bef6 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-24T03:36:59Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-e5c6b089d0cd4b7e9b88557a3052bef62022-12-21T17:17:02ZengEDP SciencesE3S Web of Conferences2267-12422020-01-011960200710.1051/e3sconf/202019602007e3sconf_strpep2020_02007Application of deep learning methods to predict ionosphere parameters in real timeMochalov VladimirMochalova AnastasiaIn this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/56/e3sconf_strpep2020_02007.pdf |
| spellingShingle | Mochalov Vladimir Mochalova Anastasia Application of deep learning methods to predict ionosphere parameters in real time E3S Web of Conferences |
| title | Application of deep learning methods to predict ionosphere parameters in real time |
| title_full | Application of deep learning methods to predict ionosphere parameters in real time |
| title_fullStr | Application of deep learning methods to predict ionosphere parameters in real time |
| title_full_unstemmed | Application of deep learning methods to predict ionosphere parameters in real time |
| title_short | Application of deep learning methods to predict ionosphere parameters in real time |
| title_sort | application of deep learning methods to predict ionosphere parameters in real time |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/56/e3sconf_strpep2020_02007.pdf |
| work_keys_str_mv | AT mochalovvladimir applicationofdeeplearningmethodstopredictionosphereparametersinrealtime AT mochalovaanastasia applicationofdeeplearningmethodstopredictionosphereparametersinrealtime |