Algorithmic feature selection and dimensionality reduction in signal classification tasks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F24%3A00369794" target="_blank" >RIV/68407700:21340/24:00369794 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-52965-8_15" target="_blank" >https://doi.org/10.1007/978-3-031-52965-8_15</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-52965-8_15" target="_blank" >10.1007/978-3-031-52965-8_15</a>

Result language

angličtina

Original language name

Algorithmic feature selection and dimensionality reduction in signal classification tasks

Original language description

This paper presents a research endeavour addressing the recognition of acoustic emission signals, aiming to enhance their utilisation in non-destructive defectoscopy and machining process control. The classification task can be accomplished through two approaches: representing signals using a suitable attribute set, or directly passing the signals in their entirety to the classification algorithm. Our primary focus was on the meticulous selection of methods and tools for automating the extraction of a comprehensive set of features from the signals, followed by dimensionality reduction techniques. Subsequently, we conducted a comprehensive performance evaluation by comparing various classifiers applied to the low-dimensional projections. Lastly, we put the feature based classification approach to the test with direct signal classification employing convolutional neural networks.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10307 - Acoustics

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

2024

Confidentiality

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

Article name in the collection

Mathematical Modeling in Physical Sciences

ISBN

978-3-031-52965-8

ISSN

2194-1009

e-ISSN

—

Number of pages

7

Pages from-to

187-193

Publisher name

Springer Nature Switzerland AG

Place of publication

Cham

Event location

Belgrade

Event date

Aug 28, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001263810800015