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

Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Information-Divergence Based Methods for Acoustic Micro-Defect Identification in Materials
Original language description
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
Czech name
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Czech description
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Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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