Testing of 2-D signal separation statistical techniques for real and generated physical data sets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F12%3A00199567" target="_blank" >RIV/68407700:21340/12:00199567 - 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

Testing of 2-D signal separation statistical techniques for real and generated physical data sets

Popis výsledku v původním jazyce

We deal with various classification methods in data sets originated from different physical experiments. It can be the case of accelerated particles data in proton-antiproton collisions (Fermilab) or applications of acoustic emission detection in nondestructive testing. The acoustic emission emerges due to the cracks, fatigues or possibly other nonlinear material effects. Signals of the acoustic emission may differ by types of materials and through these differences the signals can be assigned to material which they originate from. The classification of signals of acoustic emission can be done by means of different classification methods, in our case by means of Model-Based Clustering method (MBC). In this work we also test the suitability of chosen parameters which were used for identification of acoustic emission sources [1, 2]. The method is also successfully applied to real experimental data.

Název v anglickém jazyce

Testing of 2-D signal separation statistical techniques for real and generated physical data sets

Popis výsledku anglicky

We deal with various classification methods in data sets originated from different physical experiments. It can be the case of accelerated particles data in proton-antiproton collisions (Fermilab) or applications of acoustic emission detection in nondestructive testing. The acoustic emission emerges due to the cracks, fatigues or possibly other nonlinear material effects. Signals of the acoustic emission may differ by types of materials and through these differences the signals can be assigned to material which they originate from. The classification of signals of acoustic emission can be done by means of different classification methods, in our case by means of Model-Based Clustering method (MBC). In this work we also test the suitability of chosen parameters which were used for identification of acoustic emission sources [1, 2]. The method is also successfully applied to real experimental data.

Druh

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

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/LG12020" target="_blank" >LG12020: Využití pokročilé statistické analýzy a nestatistických separačních metod pro detekování fyzikálních procesů v datech snímaných urychlovači elementárních částic.</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

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

8

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

6

Strana od-do

67-72

Kód UT WoS článku

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EID výsledku v databázi Scopus

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