Speaker
Jan Zavadil
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.
Primary authors
Jan Zavadil
Václav Kůs
(KM FJFI CVUT)
