NSSC OpenIR
Electromagnetic Ion Cyclotron Waves Pattern Recognition Based on a Deep Learning Technique: Bag-of-Features Algorithm Applied to Spectrograms
Alternative TitleWOS:000552691000001
Medeiros, Claudia; Souza, V. M.; Vieira, L. E. A.; Sibeck, D. G.; Remya, B.; Da Silva, L. A.; Alves, L. R.; Marchezi, J. P.; Jauer, P. R.; Rockenbach, M.; Dal Lago, A.; Kletzing, C. A.
Source PublicationASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
2020
Volume249Issue:1Pages:13
DOI10.3847/1538-4365/ab9697
ISSN0067-0049
Language英语
KeywordPlanetary magnetosphere Van Allen radiation belt Neural networks Space plasmas pp waves Support vector machine Classification EMIC WAVES ELECTRON-PRECIPITATION SCATTERING DEPENDENCE PRESSURE STORM
AbstractSeveral studies have shown the importance of electromagnetic ion cyclotron (EMIC) waves to the pitch angle scattering of energetic particles in the radiation belt, especially relativistic electrons, thus contributing to their net loss from the outer radiation belt to the upper atmosphere. The huge amount of data collected thus far provides us with the opportunity to use a deep learning technique referred to as the Bag-of-Features (BoF). When applied to images of magnetic field spectrograms in the frequency range of EMIC waves, the BoF allows us to distinguish, in a semi-automated way, several patterns in these spectrograms that can be relevant to describe physical aspects of EMIC waves. Each spectrogram image provided as an input to the BoF corresponds to the windowed Fourier transform of a similar to 40 minutes to 1 hour interval of Van Allen Probes' high time-resolution vector magnetic field observations. Our data set spans the 2012 September 8 to 2016 December 31 period and is at geocentric distances larger than 3 Earth radii. A total of 66,204 spectrogram images are acquired in this interval, and about 45% of them, i.e., 30,190 images, are visually inspected to validate the BoF technique. The BoF's performance in identifying spectrograms with likely EMIC wave signatures is comparable to the visual inspection method, with the enormous advantage that the BoF technique greatly expedites the analysis by accomplishing the task in just a few minutes.
Indexed BySCI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7537
Collection中国科学院国家空间科学中心
Affiliation1.[Medeiros, Claudia
2.Souza, V. M.
3.Vieira, L. E. A.
4.Da Silva, L. A.
5.Alves, L. R.
6.Marchezi, J. P.
7.Jauer, P. R.
8.Rockenbach, M.
9.Dal Lago, A.] Inst Nacl Pesquisas Espaciais, 1758 Astronautas Av, BR-12227010 Sao Jose Dos Campos, SP, Brazil
10.[Sibeck, D. G.] NASA, Goddard Space Flight Ctr, Greenbelt, MD USA
11.[Remya, B.] Indian Inst Geomagnetism, Navi Mumbai, Maharashtra, India
12.[Da Silva, L. A.
13.Jauer, P. R.] Chinese Acad Sci, Natl Space Sci Ctr, State Key Lab Space Weather, Beijing, Peoples R China
14.[Kletzing, C. A.] Univ Iowa, Dept Phys & Astron, Iowa City, IA 52242 USA
Recommended Citation
GB/T 7714
Medeiros, Claudia,Souza, V. M.,Vieira, L. E. A.,et al. Electromagnetic Ion Cyclotron Waves Pattern Recognition Based on a Deep Learning Technique: Bag-of-Features Algorithm Applied to Spectrograms[J]. ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,2020,249(1):13.
APA Medeiros, Claudia.,Souza, V. M..,Vieira, L. E. A..,Sibeck, D. G..,Remya, B..,...&Kletzing, C. A..(2020).Electromagnetic Ion Cyclotron Waves Pattern Recognition Based on a Deep Learning Technique: Bag-of-Features Algorithm Applied to Spectrograms.ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,249(1),13.
MLA Medeiros, Claudia,et al."Electromagnetic Ion Cyclotron Waves Pattern Recognition Based on a Deep Learning Technique: Bag-of-Features Algorithm Applied to Spectrograms".ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 249.1(2020):13.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Medeiros, Claudia]'s Articles
[Souza, V. M.]'s Articles
[Vieira, L. E. A.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Medeiros, Claudia]'s Articles
[Souza, V. M.]'s Articles
[Vieira, L. E. A.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Medeiros, Claudia]'s Articles
[Souza, V. M.]'s Articles
[Vieira, L. E. A.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.