New Classification Attributes for Signal Separation in Acoustic Emission Processing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F10%3A00176063" target="_blank" >RIV/68407700:21340/10:00176063 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

New Classification Attributes for Signal Separation in Acoustic Emission Processing

Popis výsledku v původním jazyce

We deal with the classification of acoustic emission random signals by means of fuzzy clustering, model-based statistical clustering and support vector machines. The signals are compared by means of newly introduced attributes obtained directly from thesignals and from normed frequency spectra such as phi-divergence distance measure originated from information theory and statistics. We mention the well-known divergences (Kullback, Hellinger, Chi , Power) and we introduce several of its new modifications and extensions as generalized Hellinger divergences. These new families of divergences open new research possibilities in the area of statistical treatment of random signals. We realize experiments to test the proposed classification attributes and also consider industrial data from the real life.

Název v anglickém jazyce

New Classification Attributes for Signal Separation in Acoustic Emission Processing

Popis výsledku anglicky

We deal with the classification of acoustic emission random signals by means of fuzzy clustering, model-based statistical clustering and support vector machines. The signals are compared by means of newly introduced attributes obtained directly from thesignals and from normed frequency spectra such as phi-divergence distance measure originated from information theory and statistics. We mention the well-known divergences (Kullback, Hellinger, Chi , Power) and we introduce several of its new modifications and extensions as generalized Hellinger divergences. These new families of divergences open new research possibilities in the area of statistical treatment of random signals. We realize experiments to test the proposed classification attributes and also consider industrial data from the real life.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Forum Statisticum Slovacum

ISSN

1336-7420

e-ISSN

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Svazek periodika

7

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

7

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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