Information-Divergence Based Methods for Acoustic Micro-Defect Identification in Materials

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F11%3A00188005" target="_blank" >RIV/68407700:21340/11:00188005 - 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

Information-Divergence Based Methods for Acoustic Micro-Defect Identification in Materials

Popis výsledku v původním jazyce

We deal with the diversity of acoustic emission sources in materials through signal processing methodology for the data sets detected by the measurement device Dakel Xedo 5. Applying the following methods of Fuzzy Clustering (FC), Model-Based Clustering(MBC) and Support Vector Machines (SVM) in combination with empirical nonstandard signal and spectrum attributes, we arrive to the efficient source separation technique. These methods belong to fundamentally different groups. The FC is based on the optimization of an objective function. The MBC consists of two parts - the Agglomerative Clustering and the iterative EM algorithm minimizing likelihood function of the statistical model under consideration. Finally, the SVM searches for optimal separating hyperplanes between clusters. The signals are compared by means of suitable parameters obtained directly from detected signals or their normalized spectral density estimates. We also use distinctive phi-divergence distance measures between

Název v anglickém jazyce

Information-Divergence Based Methods for Acoustic Micro-Defect Identification in Materials

Popis výsledku anglicky

We deal with the diversity of acoustic emission sources in materials through signal processing methodology for the data sets detected by the measurement device Dakel Xedo 5. Applying the following methods of Fuzzy Clustering (FC), Model-Based Clustering(MBC) and Support Vector Machines (SVM) in combination with empirical nonstandard signal and spectrum attributes, we arrive to the efficient source separation technique. These methods belong to fundamentally different groups. The FC is based on the optimization of an objective function. The MBC consists of two parts - the Agglomerative Clustering and the iterative EM algorithm minimizing likelihood function of the statistical model under consideration. Finally, the SVM searches for optimal separating hyperplanes between clusters. The signals are compared by means of suitable parameters obtained directly from detected signals or their normalized spectral density estimates. We also use distinctive phi-divergence distance measures between

Druh

O - Ostatní výsledky

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

2011

Kód důvěrnosti údajů

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