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Statements

Subject Item
n2:RIV%2F68407700%3A21340%2F11%3A00187897%21RIV12-MSM-21340___
rdf:type
skos:Concept n15:Vysledek
dcterms:description
The distribution mixture (DM) model is usually applied whenever there is a strong evidence of multimodality in the data which is the case of Acoustic Emission (AE) applications. Our scope is the following: to employ the divergence based set of signal classification attributes and to explore the possible use of the distribution mixture as a model based method in statistical AE classification and cluster analysis. The clusters are considered to be generated from the DM components with normal densities. We deal with the problem of optimal number of components of the mixture. The contaminated model with simulated misleading cluster is considered and comparative sensitivity study is produced to discover the basic stability and robustness properties of DM approach. The distribution mixture (DM) model is usually applied whenever there is a strong evidence of multimodality in the data which is the case of Acoustic Emission (AE) applications. Our scope is the following: to employ the divergence based set of signal classification attributes and to explore the possible use of the distribution mixture as a model based method in statistical AE classification and cluster analysis. The clusters are considered to be generated from the DM components with normal densities. We deal with the problem of optimal number of components of the mixture. The contaminated model with simulated misleading cluster is considered and comparative sensitivity study is produced to discover the basic stability and robustness properties of DM approach.
dcterms:title
Sensitivity of Acoustic Emission Classification under Distribution Mixtures Sensitivity of Acoustic Emission Classification under Distribution Mixtures
skos:prefLabel
Sensitivity of Acoustic Emission Classification under Distribution Mixtures Sensitivity of Acoustic Emission Classification under Distribution Mixtures
skos:notation
RIV/68407700:21340/11:00187897!RIV12-MSM-21340___
n15:predkladatel
n16:orjk%3A21340
n3:aktivita
n7:S n7:Z
n3:aktivity
S, Z(MSM6840770039)
n3:cisloPeriodika
7
n3:dodaniDat
n14:2012
n3:domaciTvurceVysledku
n8:1204963 n8:1794973
n3:druhVysledku
n10:J
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
228838
n3:idVysledku
RIV/68407700:21340/11:00187897
n3:jazykVysledku
n18:eng
n3:klicovaSlova
Classification attributes; phi-divergences; Signal separation; Robustness properties
n3:klicoveSlovo
n4:phi-divergences n4:Signal%20separation n4:Classification%20attributes n4:Robustness%20properties
n3:kodStatuVydavatele
SK - Slovenská republika
n3:kontrolniKodProRIV
[101E0E0530CF]
n3:nazevZdroje
Forum Statisticum Slovacum
n3:obor
n9:BA
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n14:2011
n3:svazekPeriodika
8
n3:tvurceVysledku
Tláskal, Jan Kůs, Václav
n3:zamer
n5:MSM6840770039
s:issn
1336-7420
s:numberOfPages
7
n19:organizacniJednotka
21340