uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Classification of JET Neutron and Gamma Emissivity Profiles
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.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Show others and affiliations
Number of Authors: 11092016 (English)In: Journal of Instrumentation, ISSN 1748-0221, E-ISSN 1748-0221, Vol. 11, article id C05021Article in journal (Refereed) Published
Abstract [en]

In thermonuclear plasmas, emission tomography uses integrated measurements along lines of sight (LOS) to determine the two-dimensional (2-D) spatial distribution of the volume emission intensity. Due to the availability of only a limited number views and to the coarse sampling of the LOS, the tomographic inversion is a limited data set problem. Several techniques have been developed for tomographic reconstruction of the 2-D gamma and neutron emissivity on JET. In specific experimental conditions the availability of LOSs is restricted to a single view. In this case an explicit reconstruction of the emissivity profile is no longer possible. However, machine learning classification methods can be used in order to derive the type of the distribution. In the present approach the classification is developed using the theory of belief functions which provide the support to fuse the results of independent clustering and supervised classification. The method allows to represent the uncertainty of the results provided by different independent techniques, to combine them and to manage possible conflicts.

Place, publisher, year, edition, pages
IOP PUBLISHING LTD , 2016. Vol. 11, article id C05021
Keywords [en]
Data processing methods, Nuclear instruments and methods for hot plasma diagnostics, Pattern recognition, cluster finding, calibration and fitting methods
National Category
Accelerator Physics and Instrumentation
Identifiers
URN: urn:nbn:se:uu:diva-400798DOI: 10.1088/1748-0221/11/05/C05021ISI: 000377851700021OAI: oai:DiVA.org:uu-400798DiVA, id: diva2:1382600
Funder
EU, Horizon 2020, 633053
Note

For complete list of authors see http://dx.doi.org/10.1088/1748-0221/11/05/C05021

Available from: 2020-01-03 Created: 2020-01-03 Last updated: 2020-01-03Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Andersson Sundén, ErikBinda, FedericoCecconello, MarcoConroy, SeanDzysiuk, NataliiaEricsson, GöranEriksson, JacobHellesen, CarlHjalmarsson, AndersPossnert, GöranSjöstrand, HenrikSkiba, MateuszWeiszflog, Matthias

Search in DiVA

By author/editor
Andersson Sundén, ErikBinda, FedericoCecconello, MarcoConroy, SeanDzysiuk, NataliiaEricsson, GöranEriksson, JacobHellesen, CarlHjalmarsson, AndersPossnert, GöranSjöstrand, HenrikSkiba, MateuszWeiszflog, Matthias
By organisation
Applied Nuclear Physics
In the same journal
Journal of Instrumentation
Accelerator Physics and Instrumentation

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 1 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf