Algorithmic Feature Selection and Dimensionality Reduction in Signal Classification Tasks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388998%3A_____%2F24%3A00603949" target="_blank" >RIV/61388998:_____/24:00603949 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-52965-8_15" target="_blank" >https://link.springer.com/chapter/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>

Jazyk výsledku

angličtina

Název v původním jazyce

Algorithmic Feature Selection and Dimensionality Reduction in Signal Classification Tasks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Algorithmic Feature Selection and Dimensionality Reduction in Signal Classification Tasks

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Springer Proceedings in Mathematics and Statistics

ISBN

978-3-031-52964-1

ISSN

2194-1009

e-ISSN

—

Počet stran výsledku

6

Strana od-do

187-193

Název nakladatele

Springer

Místo vydání

CHAM

Místo konání akce

Bělehrad

Datum konání akce

23. 8. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001263810800015