Matches in UGent Biblio for { <https://biblio.ugent.be/publication/1149315#aggregation> ?p ?o. }
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- aggregation classification "C3".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2003".
- aggregation hasFormat 1149315.bibtex.
- aggregation hasFormat 1149315.csv.
- aggregation hasFormat 1149315.dc.
- aggregation hasFormat 1149315.didl.
- aggregation hasFormat 1149315.doc.
- aggregation hasFormat 1149315.json.
- aggregation hasFormat 1149315.mets.
- aggregation hasFormat 1149315.mods.
- aggregation hasFormat 1149315.rdf.
- aggregation hasFormat 1149315.ris.
- aggregation hasFormat 1149315.txt.
- aggregation hasFormat 1149315.xls.
- aggregation hasFormat 1149315.yaml.
- aggregation language "eng".
- aggregation subject "Physics and Astronomy".
- aggregation title "Reconstruction of edge Zeff profiles from bremsstrahlung data via (semi-)blind source separation methods on TEXTOR".
- aggregation abstract "The understanding of the behaviour of impurities is a critical issue in tokamak physics. At the tokamak TEXTOR we run a diagnostic for determining the eective ion charge Ze from bremsstrahlung measurements in the visible. However, a major problem in determining a Ze prole is posed by the incertitude at the plasma edge on the dierent contributions to the measured continuum signal, as well as by the routine use of estimated edge values for Te and ne. As a result, the interpretation of a prole is rendered problematic outside the central plasma. In fact, so far none of the available methods for the determination of Ze has been able to extend the Ze prole up to the edge, which is at present a real challenge. Now, a measured line-integrated emissivity signal can be considered as a linear or nonlinear function of a number of signals from which Ze can be derived directly. Several methods for (semi-)Blind Signal Separation (BSS) allow the separation of signal mixtures into their components, (mainly) based on the statistical properties of each contribution. We have conducted a preliminary experiment with multi-channel linear Independent Component Analysis, which indeed already suggests that certain features in the data can be attributed".
- aggregation authorList BK164120.
- aggregation isDescribedBy 1149315.
- aggregation similarTo LU-1149315.