Machine learning and Monte Carlo based data analysis methods in cosmic dust research

dc.contributor.advisor	Srama, Ralf (Priv.-Doz. Dr.-Ing.)
dc.contributor.author	Albin, Thomas
dc.date.accessioned	2019-07-19T08:40:56Z
dc.date.available	2019-07-19T08:40:56Z
dc.date.issued	2019	de
dc.description.abstract	This work applies miscellaneous algorithms from the fields Machine Learning and Computational Numerics on the research field Cosmic Dust. The task is to determine the scientific and technical potential of using different methods. Here, the methods are applied on two different projects: the meteor camera system Canary Island Long-Baseline Observatory (CILBO) and the Cassini in-situ dust telescope Cosmic-Dust-Analyzer (CDA).	en
dc.identifier.other	1669486192
dc.identifier.uri	http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-104716	de
dc.identifier.uri	http://elib.uni-stuttgart.de/handle/11682/10471
dc.identifier.uri	http://dx.doi.org/10.18419/opus-10454
dc.language.iso	en	de
dc.rights	info:eu-repo/semantics/openAccess	de
dc.subject.ddc	520	de
dc.title	Machine learning and Monte Carlo based data analysis methods in cosmic dust research	en
dc.type	doctoralThesis	de
ubs.dateAccepted	2019-04-01
ubs.fakultaet	Luft- und Raumfahrttechnik und Geodäsie	de
ubs.institut	Institut für Raumfahrtsysteme	de
ubs.publikation.seiten	xx, 246	de
ubs.publikation.typ	Dissertation	de
ubs.thesis.grantor	Luft- und Raumfahrttechnik und Geodäsie	de

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