Speaker
Jan Zavadil
(Department of Mathematics, FNSPE, Czech Technical University in Prague)
Description
Reliable classification of acoustic emission signals is crucial for practical use of this nondestructive testing technique. During classification, signals are represented by a convenient, low-dimensional set of attributes. This presentation adresses the problem of selecting appropriate atributes and consequently describes and compares several classification methods, specifically Division methods, Model Based method, KDE method and classification using Supervised Divergence Decision Tree. The paper proposes new attribute and classification method. The methods were tested and compared on a set of laboratory measured data. The most reliable method seems to be the supervised KDE classification method.
Primary author
Jan Zavadil
(Department of Mathematics, FNSPE, Czech Technical University in Prague)
