Sensitivity of Acoustic Emission Classification under Distribution Mixtures
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F11%3A00187897" target="_blank" >RIV/68407700:21340/11:00187897 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Sensitivity of Acoustic Emission Classification under Distribution Mixtures
Original language 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. Wedeal 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.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BA - General mathematics
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Forum Statisticum Slovacum
ISSN
1336-7420
e-ISSN
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Volume of the periodical
8
Issue of the periodical within the volume
7
Country of publishing house
SK - SLOVAKIA
Number of pages
7
Pages from-to
103-109
UT code for WoS article
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EID of the result in the Scopus database
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