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Feature Extraction Methods for Neural Networks in the Classification of Structural Health Anomalies

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Bio-inspired models and solutions; Complex Artificial Neural Network Based Systems and Dynamics; Computational Neuroscience

Authors: Natasha Hamilton 1 ; Jim Harkin 1 ; Liam McDaid 1 ; Junxiu Liu 1 and Eoghan Furey 2

Affiliations: 1 School of Computing, Engineering and Intelligent Systems, Ulster University, Northland Road, Derry, U.K. ; 2 Department of Computing, Atlantic Technological University, Port Road, Donegal, Ireland

Keyword(s): Structural Health Monitoring, Feature Extraction, Spiking Neural Networks, Classification.

Abstract: Failure of large complex structures such as buildings and bridges can have monumental repercussions such as human mortality, environmental destruction and economic consequences. It is therefore paramount that detection of structural damage or anomalies are identified and managed early. This highlights the need to develop automated Structural Health Monitoring (SHM) systems that can continuously allow the safety status of structures to be determined, even in the worst and most isolated conditions, to ultimately help prevent destruction and save lives. Signal processing is a crucial step to detecting structural anomalies and recent work demonstrates the opportunities for neural networks, however the encoding of data for SHM requires the extraction of features due to often, noisy data. This paper focuses on feature extraction methods for artificial neural networks (ANNs) and spiking neural networks (SNNs) and aims to identify bespoke features which enable SNNs to encode data and perform the classification of anomalies. Results show that extraction of particular features in large real-world applications improve the classification accuracy of SNNs. (More)

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Paper citation in several formats:
Hamilton, N.; Harkin, J.; McDaid, L.; Liu, J. and Furey, E. (2023). Feature Extraction Methods for Neural Networks in the Classification of Structural Health Anomalies. In Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA; ISBN 978-989-758-674-3; ISSN 2184-3236, SciTePress, pages 514-523. DOI: 10.5220/0012184800003595

@conference{ncta23,
author={Natasha Hamilton. and Jim Harkin. and Liam McDaid. and Junxiu Liu. and Eoghan Furey.},
title={Feature Extraction Methods for Neural Networks in the Classification of Structural Health Anomalies},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA},
year={2023},
pages={514-523},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012184800003595},
isbn={978-989-758-674-3},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA
TI - Feature Extraction Methods for Neural Networks in the Classification of Structural Health Anomalies
SN - 978-989-758-674-3
IS - 2184-3236
AU - Hamilton, N.
AU - Harkin, J.
AU - McDaid, L.
AU - Liu, J.
AU - Furey, E.
PY - 2023
SP - 514
EP - 523
DO - 10.5220/0012184800003595
PB - SciTePress