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Machine learning in nuclear safeguards
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.ORCID iD: 0000-0002-5133-6829
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.ORCID iD: 0000-0001-8207-3462
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.ORCID iD: 0000-0002-3136-5665
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2019 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

•Before placing spent nuclear fuel in in a geological repository, they will be characterized and their declared properties will be verified.

•We have created large library of modelled spent nuclear fuel (SNF) assemblies and estimated their activity of gamma-ray emitting fission products, the early die-away time τ and the Cherenkov light intensity.

•We have used Random Forest regression to evaluate the capability to determine the fuel parameters initial enrichment (IE), burnup (BU) and cooling time (CT) using data from non-destructive assay (NDA) techniques

Place, publisher, year, edition, pages
2019.
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Other Physics Topics
Identifiers
URN: urn:nbn:se:uu:diva-394881OAI: oai:DiVA.org:uu-394881DiVA, id: diva2:1359667
Conference
Swedish Workshop on Data Science
Funder
Swedish Radiation Safety AuthorityAvailable from: 2019-10-10 Created: 2019-10-10 Last updated: 2019-10-10

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Grape, SophieBranger, ErikJansson, Peter

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Grape, SophieBranger, ErikElter, ZsoltJansson, PeterMishra, Vaibhav
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