Matches in SemOpenAlex for { <https://semopenalex.org/work/W2895435802> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W2895435802 endingPage "817" @default.
- W2895435802 startingPage "808" @default.
- W2895435802 abstract "Muon imaging is a new imaging technique which can be used to image large bulky objects, especially objects with heavy shielding where other techniques like X-ray CT scanning will often fail. This is due to the fact that high energy cosmic rays have a very high penetrative power and can easily penetrate hundreds of meters of rock. Muon imaging is essentially an inverse problem. There are two popular forms of muon imaging techniques - absorption muon imaging based on the attenuation of muons in matter and multiple scattering muon imaging based on the multiple scattering effect of muons. Muon imaging can be used in many areas, ranging from volcanology and searching for secret cavities in pyramids over border monitoring for special nuclear materials to nuclear safeguards applications for monitoring the spent fuel casks. Due to the lack of man-made muon sources, both of the muon imaging techniques rely on cosmic ray muons. One important shortcoming of comic ray muons are the very low intensities. In order to get high image resolutions, very long exposure times are needed. In this paper, we will study how machine learning techniques can be used to improve the muon imaging techniques." @default.
- W2895435802 created "2018-10-12" @default.
- W2895435802 creator A5028166786 @default.
- W2895435802 creator A5035322891 @default.
- W2895435802 creator A5062995749 @default.
- W2895435802 creator A5072747920 @default.
- W2895435802 date "2018-01-01" @default.
- W2895435802 modified "2023-09-26" @default.
- W2895435802 title "Machine Learning for Muon Imaging" @default.
- W2895435802 cites W1616799229 @default.
- W2895435802 cites W1923698600 @default.
- W2895435802 cites W1966248051 @default.
- W2895435802 cites W1976258997 @default.
- W2895435802 cites W1994739585 @default.
- W2895435802 cites W1998092115 @default.
- W2895435802 cites W2006380397 @default.
- W2895435802 cites W2018150558 @default.
- W2895435802 cites W2025025456 @default.
- W2895435802 cites W2044029912 @default.
- W2895435802 cites W2048137459 @default.
- W2895435802 cites W2094121632 @default.
- W2895435802 cites W2110464397 @default.
- W2895435802 cites W2128158076 @default.
- W2895435802 cites W2128548195 @default.
- W2895435802 cites W2136855473 @default.
- W2895435802 cites W2165739256 @default.
- W2895435802 cites W2168008172 @default.
- W2895435802 cites W2168414160 @default.
- W2895435802 cites W2169185809 @default.
- W2895435802 cites W2542660088 @default.
- W2895435802 cites W2605700053 @default.
- W2895435802 cites W2767121551 @default.
- W2895435802 cites W2789171981 @default.
- W2895435802 cites W3100103432 @default.
- W2895435802 cites W4246623150 @default.
- W2895435802 doi "https://doi.org/10.1007/978-3-030-00563-4_79" @default.
- W2895435802 hasPublicationYear "2018" @default.
- W2895435802 type Work @default.
- W2895435802 sameAs 2895435802 @default.
- W2895435802 citedByCount "2" @default.
- W2895435802 countsByYear W28954358022021 @default.
- W2895435802 countsByYear W28954358022023 @default.
- W2895435802 crossrefType "book-chapter" @default.
- W2895435802 hasAuthorship W2895435802A5028166786 @default.
- W2895435802 hasAuthorship W2895435802A5035322891 @default.
- W2895435802 hasAuthorship W2895435802A5062995749 @default.
- W2895435802 hasAuthorship W2895435802A5072747920 @default.
- W2895435802 hasConcept C109214941 @default.
- W2895435802 hasConcept C121332964 @default.
- W2895435802 hasConcept C121684516 @default.
- W2895435802 hasConcept C154945302 @default.
- W2895435802 hasConcept C205334942 @default.
- W2895435802 hasConcept C31972630 @default.
- W2895435802 hasConcept C41008148 @default.
- W2895435802 hasConceptScore W2895435802C109214941 @default.
- W2895435802 hasConceptScore W2895435802C121332964 @default.
- W2895435802 hasConceptScore W2895435802C121684516 @default.
- W2895435802 hasConceptScore W2895435802C154945302 @default.
- W2895435802 hasConceptScore W2895435802C205334942 @default.
- W2895435802 hasConceptScore W2895435802C31972630 @default.
- W2895435802 hasConceptScore W2895435802C41008148 @default.
- W2895435802 hasLocation W28954358021 @default.
- W2895435802 hasOpenAccess W2895435802 @default.
- W2895435802 hasPrimaryLocation W28954358021 @default.
- W2895435802 hasRelatedWork W1891287906 @default.
- W2895435802 hasRelatedWork W1969923398 @default.
- W2895435802 hasRelatedWork W2036807459 @default.
- W2895435802 hasRelatedWork W2058170566 @default.
- W2895435802 hasRelatedWork W2166024367 @default.
- W2895435802 hasRelatedWork W2166044122 @default.
- W2895435802 hasRelatedWork W2229312674 @default.
- W2895435802 hasRelatedWork W2755342338 @default.
- W2895435802 hasRelatedWork W2772917594 @default.
- W2895435802 hasRelatedWork W3116076068 @default.
- W2895435802 isParatext "false" @default.
- W2895435802 isRetracted "false" @default.
- W2895435802 magId "2895435802" @default.
- W2895435802 workType "book-chapter" @default.