000			03661nam a22004575i 4500
988			_aSpringer_Computer_2022
999			_c382963 _d382963 _x1
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003			ES-MaUEC
005			20230102122030.0
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008			221008s2022 si | s |||| 0|eng d
020			_a9789811927461
024	7		_a10.1007/978-981-19-2746-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQB501 _b2022 EB
100	1		_aXu, Long _eauthor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684964
245	1	0	_aDeep Learning in Solar Astronomy _cby Long Xu, Yihua Yan, Xin Huang
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XIV, 92 páginas) _b1 ilustraciones
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_aChapter 1: Introduction -- Chapter 2: Classical deep learning models -- Chapter 3: Deep learning in solar image classification tasks -- Chapter 4: Deep learning in solar object detection tasks -- Chapter 5: Deep learning in solar image generation tasks -- Chapter 6: Deep learning in solar forecasting tasks.
520			_aThe volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.
650		7	_2embne _9405013 _aAstronomía
650		7	_2embne _9166090 _aAprendizaje automático
651		7	_2embne _9560264 _aSistema solar
700	1		_aYan, Yihua _eauthor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684965
700	1		_aHuang, Xin _eauthor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684966
776	0	8	_iPrinted edition: _z9789811927454
776	0	8	_iPrinted edition: _z9789811927478
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-2746-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2022 _dz _eIG _zSI