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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F13%3APU108491%21RIV14-MSM-26230___
rdf:type
skos:Concept n4:Vysledek
rdfs:seeAlso
http://link.springer.com/chapter/10.1007%2F978-3-319-00542-3_46
dcterms:description
The goal of our work is to create a feature extraction method for classification of Be stars. Be stars are characterized by prominent emission lines in their spectrum. We focus on the automated classification of Be stars based on typical shapes of their emission lines. We aim to design a reduced, specific set of features characterizing and discriminating the shapes of Be lines. In this paper, we present a feature extraction method based on the wavelet transform and its power spectrum. Both the discrete and continuous wavelet transform are used. Different feature vectors are created and compared on clustering of Be stars spectra from the archive of the Astronomical Institute of the Academy of Sciences of the Czech Republic. The clustering is performed using the k- means algorithm. The results of our method are promising and encouraging to more detailed analysis. The goal of our work is to create a feature extraction method for classification of Be stars. Be stars are characterized by prominent emission lines in their spectrum. We focus on the automated classification of Be stars based on typical shapes of their emission lines. We aim to design a reduced, specific set of features characterizing and discriminating the shapes of Be lines. In this paper, we present a feature extraction method based on the wavelet transform and its power spectrum. Both the discrete and continuous wavelet transform are used. Different feature vectors are created and compared on clustering of Be stars spectra from the archive of the Astronomical Institute of the Academy of Sciences of the Czech Republic. The clustering is performed using the k- means algorithm. The results of our method are promising and encouraging to more detailed analysis.
dcterms:title
Wavelet Based Feature Extraction for Clustering of Be Stars Wavelet Based Feature Extraction for Clustering of Be Stars
skos:prefLabel
Wavelet Based Feature Extraction for Clustering of Be Stars Wavelet Based Feature Extraction for Clustering of Be Stars
skos:notation
RIV/00216305:26230/13:PU108491!RIV14-MSM-26230___
n4:predkladatel
n5:orjk%3A26230
n3:aktivita
n6:I n6:S
n3:aktivity
I, S
n3:dodaniDat
n18:2014
n3:domaciTvurceVysledku
n8:3725340 n8:1077759
n3:druhVysledku
n21:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
117139
n3:idVysledku
RIV/00216305:26230/13:PU108491
n3:jazykVysledku
n22:eng
n3:klicovaSlova
Be star, feature extraction, wavelet transform, wavelet power spectrum
n3:klicoveSlovo
n14:feature%20extraction n14:wavelet%20power%20spectrum n14:wavelet%20transform n14:Be%20star
n3:kontrolniKodProRIV
[25F0E4801BF7]
n3:mistoKonaniAkce
VSB Technical University of Ostrava
n3:mistoVydani
New York
n3:nazevZdroje
Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems
n3:obor
n23:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n18:2013
n3:tvurceVysledku
Zendulka, Jaroslav Škoda, Petr Bromová, Pavla
n3:typAkce
n11:EUR
n3:zahajeniAkce
2013-06-03+02:00
s:numberOfPages
9
n20:doi
10.1007/978-3-319-00542-3_46
n9:hasPublisher
Springer US
n13:isbn
978-3-319-00541-6
n19:organizacniJednotka
26230