Feature selection and classification of acoustic emission signals

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F23%3A00369790" target="_blank" >RIV/68407700:21340/23:00369790 - 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
Feature selection and classification of acoustic emission signals
Original language description
This contribution deals with the recognition of acoustic emission signals for use in non-destructive defectoscopy or in machining process control. Classification can either be performed by representing signals by a convenient, lower-dimensional set of attributes or more directly, by passing them in their entirety to the classification algorithm. We focused on selecting methods and tools for the automated extraction of a large number of features from signals and then performing dimensionality reduction on them. Finally, we compared the performances of various classifiers on these low-dimensional projections with the direct classification of signals using convolutional neural networks.
Czech name
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Czech description
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Project
<a href="/en/project/LM2023061" target="_blank" >LM2023061: Research Infrastructure for Fermilab Experiments</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
SPMS 2022/23 Stochastic and Physical Monitoring Systems, Proceedings of the international conferences
ISBN
978-80-01-07250-9
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
65-70
Publisher name
České vysoké učení technické v Praze
Place of publication
Praha
Event location
Sloup v Čechách
Event date
Jun 26, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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