001447186 000__ 03620cam\a2200517Ii\4500
001447186 001__ 1447186
001447186 003__ OCoLC
001447186 005__ 20230310004106.0
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001447186 008__ 220603s2022\\\\si\a\\\\ob\\\\000\0\eng\d
001447186 020__ $$a9789811927461$$q(electronic bk.)
001447186 020__ $$a9811927464$$q(electronic bk.)
001447186 020__ $$z9789811927454$$q(print)
001447186 0247_ $$a10.1007/978-981-19-2746-1$$2doi
001447186 035__ $$aSP(OCoLC)1322837872
001447186 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dN$T$$dOCLCF$$dUKAHL$$dOCLCQ
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001447186 049__ $$aISEA
001447186 050_4 $$aQB501
001447186 08204 $$a523.70285631$$223/eng/20220603
001447186 1001_ $$aXu, Long,$$eauthor.
001447186 24510 $$aDeep learning in solar astronomy /$$cLong Xu, Yihua Yan, Xin Huang.
001447186 264_1 $$aSingapore :$$bSpringer,$$c2022.
001447186 300__ $$a1 online resource (xiv, 92 pages) :$$billustrations.
001447186 336__ $$atext$$btxt$$2rdacontent
001447186 337__ $$acomputer$$bc$$2rdamedia
001447186 338__ $$aonline resource$$bcr$$2rdacarrier
001447186 4901_ $$aSpringerBriefs in computer science,$$x2191-5776
001447186 504__ $$aIncludes bibliographical references.
001447186 5050_ $$aChapter 1: Introduction -- Chapter 2: Classical deep learning models -- Chapter 3: Deep learning in solar image classification tasks -- Chapter 4: Deep learning in solar object detection tasks -- Chapter 5: Deep learning in solar image generation tasks -- Chapter 6: Deep learning in solar forecasting tasks.
001447186 506__ $$aAccess limited to authorized users.
001447186 520__ $$aThe volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.
001447186 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 3, 2022).
001447186 650_0 $$aDeep learning (Machine learning)
001447186 651_0 $$aSun$$xObservations$$xData processing.
001447186 655_0 $$aElectronic books.
001447186 7001_ $$aYan, Yihua,$$eauthor.
001447186 7001_ $$aHuang, Xin,$$eauthor.
001447186 830_0 $$aSpringerBriefs in computer science,$$x2191-5776
001447186 852__ $$bebk
001447186 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-2746-1$$zOnline Access$$91397441.1
001447186 909CO $$ooai:library.usi.edu:1447186$$pGLOBAL_SET
001447186 980__ $$aBIB
001447186 980__ $$aEBOOK
001447186 982__ $$aEbook
001447186 983__ $$aOnline
001447186 994__ $$a92$$bISE