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Statements

Subject Item
n2:RIV%2F68407700%3A21340%2F10%3A00176076%21RIV11-MSM-21340___
rdf:type
n11:Vysledek skos:Concept
dcterms:description
We deal with the classification of acoustic emission signals by means of Fuzzy Clustering (FC), Model-Based Clustering (MBC) and Support Vector Machines (SVM). These methods belong to a different group of classification techniques, e.g. the SVM is searching for optimal separating hyperplanes between clusters. The signals are compared by means of suitable parameters obtained directly from the signals and from normed frequency spectra such as phi-divergence distance measure as the additional attribute. We are concerned with resulting cluster comparisons and the selection of efficient classification parameters. We realize three experiments in the area of acoustic emission to test the proposed classification methods by means of laboratory data and also considering industrial data from the real life. We deal with the classification of acoustic emission signals by means of Fuzzy Clustering (FC), Model-Based Clustering (MBC) and Support Vector Machines (SVM). These methods belong to a different group of classification techniques, e.g. the SVM is searching for optimal separating hyperplanes between clusters. The signals are compared by means of suitable parameters obtained directly from the signals and from normed frequency spectra such as phi-divergence distance measure as the additional attribute. We are concerned with resulting cluster comparisons and the selection of efficient classification parameters. We realize three experiments in the area of acoustic emission to test the proposed classification methods by means of laboratory data and also considering industrial data from the real life.
dcterms:title
STATISTICAL METHODS IN SIGNAL PROCESSING AND DISCRIMINATION STATISTICAL METHODS IN SIGNAL PROCESSING AND DISCRIMINATION
skos:prefLabel
STATISTICAL METHODS IN SIGNAL PROCESSING AND DISCRIMINATION STATISTICAL METHODS IN SIGNAL PROCESSING AND DISCRIMINATION
skos:notation
RIV/68407700:21340/10:00176076!RIV11-MSM-21340___
n4:aktivita
n12:S n12:Z
n4:aktivity
S, Z(MSM6840770039)
n4:dodaniDat
n8:2011
n4:domaciTvurceVysledku
n6:1204963 n6:2335522
n4:druhVysledku
n16:D
n4:duvernostUdaju
n5:S
n4:entitaPredkladatele
n14:predkladatel
n4:idSjednocenehoVysledku
289943
n4:idVysledku
RIV/68407700:21340/10:00176076
n4:jazykVysledku
n18:eng
n4:klicovaSlova
Signal classification; phi-divergences; Fuzzy method; Model-Based method; SVM method; Real data processing
n4:klicoveSlovo
n15:SVM%20method n15:Signal%20classification n15:Real%20data%20processing n15:Fuzzy%20method n15:phi-divergences n15:Model-Based%20method
n4:kontrolniKodProRIV
[D02EEC34FC56]
n4:mistoKonaniAkce
Plzeň
n4:mistoVydani
Brno
n4:nazevZdroje
DEFEKTOSKOPIE 2010 NDE for Safety PROCEEDINGS 40th International Conference
n4:obor
n19:BA
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:rokUplatneniVysledku
n8:2010
n4:tvurceVysledku
Kůs, Václav Farová, Zuzana
n4:typAkce
n17:EUR
n4:zahajeniAkce
2010-11-10+01:00
n4:zamer
n21:MSM6840770039
s:numberOfPages
9
n9:hasPublisher
Vysoké učení technické v Brně
n13:isbn
978-80-214-4182-8
n10:organizacniJednotka
21340