Cargando…

Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring

A novelty signal processing method is proposed for a technical vision system (TVS). During data acquisition of an optoelectrical signal, part of this is random electrical fluctuation of voltages. Information theory (IT) is a well-known field that deals with random processes. A method based on using...

Descripción completa

Detalles Bibliográficos
Autores principales: Garcia-González, Wendy, Flores-Fuentes, Wendy, Sergiyenko, Oleg, Rodríguez-Quiñonez, Julio C., Miranda-Vega, Jesús E., Hernández-Balbuena, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453124/
https://www.ncbi.nlm.nih.gov/pubmed/37628237
http://dx.doi.org/10.3390/e25081207
_version_ 1785095845597675520
author Garcia-González, Wendy
Flores-Fuentes, Wendy
Sergiyenko, Oleg
Rodríguez-Quiñonez, Julio C.
Miranda-Vega, Jesús E.
Hernández-Balbuena, Daniel
author_facet Garcia-González, Wendy
Flores-Fuentes, Wendy
Sergiyenko, Oleg
Rodríguez-Quiñonez, Julio C.
Miranda-Vega, Jesús E.
Hernández-Balbuena, Daniel
author_sort Garcia-González, Wendy
collection PubMed
description A novelty signal processing method is proposed for a technical vision system (TVS). During data acquisition of an optoelectrical signal, part of this is random electrical fluctuation of voltages. Information theory (IT) is a well-known field that deals with random processes. A method based on using of the Shannon Entropy for feature extractions of optical patterns is presented. IT is implemented in structural health monitoring (SHM) to augment the accuracy of optoelectronic signal classifiers for a metrology subsystem of the TVS. To enhance the TVS spatial coordinate measurement performance at real operation conditions with electrical and optical noisy environments to estimate structural displacement better and evaluate its health for a better estimation of structural displacement and the evaluation of its health. Five different machine learning (ML) techniques are used in this work to classify optical patterns captured with the TVS. Linear predictive coding (LPC) and Autocorrelation function (ACC) are for extraction of optical patterns. The Shannon entropy segmentation (SH) method extracts relevant information from optical patterns, and the model’s performance can be improved. The results reveal that segmentation with Shannon’s entropy can achieve over 95.33%. Without Shannon’s entropy, the worst accuracy was 33.33%.
format Online
Article
Text
id pubmed-10453124
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-104531242023-08-26 Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring Garcia-González, Wendy Flores-Fuentes, Wendy Sergiyenko, Oleg Rodríguez-Quiñonez, Julio C. Miranda-Vega, Jesús E. Hernández-Balbuena, Daniel Entropy (Basel) Article A novelty signal processing method is proposed for a technical vision system (TVS). During data acquisition of an optoelectrical signal, part of this is random electrical fluctuation of voltages. Information theory (IT) is a well-known field that deals with random processes. A method based on using of the Shannon Entropy for feature extractions of optical patterns is presented. IT is implemented in structural health monitoring (SHM) to augment the accuracy of optoelectronic signal classifiers for a metrology subsystem of the TVS. To enhance the TVS spatial coordinate measurement performance at real operation conditions with electrical and optical noisy environments to estimate structural displacement better and evaluate its health for a better estimation of structural displacement and the evaluation of its health. Five different machine learning (ML) techniques are used in this work to classify optical patterns captured with the TVS. Linear predictive coding (LPC) and Autocorrelation function (ACC) are for extraction of optical patterns. The Shannon entropy segmentation (SH) method extracts relevant information from optical patterns, and the model’s performance can be improved. The results reveal that segmentation with Shannon’s entropy can achieve over 95.33%. Without Shannon’s entropy, the worst accuracy was 33.33%. MDPI 2023-08-14 /pmc/articles/PMC10453124/ /pubmed/37628237 http://dx.doi.org/10.3390/e25081207 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Garcia-González, Wendy
Flores-Fuentes, Wendy
Sergiyenko, Oleg
Rodríguez-Quiñonez, Julio C.
Miranda-Vega, Jesús E.
Hernández-Balbuena, Daniel
Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring
title Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring
title_full Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring
title_fullStr Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring
title_full_unstemmed Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring
title_short Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring
title_sort shannon entropy used for feature extractions of optical patterns in the context of structural health monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453124/
https://www.ncbi.nlm.nih.gov/pubmed/37628237
http://dx.doi.org/10.3390/e25081207
work_keys_str_mv AT garciagonzalezwendy shannonentropyusedforfeatureextractionsofopticalpatternsinthecontextofstructuralhealthmonitoring
AT floresfuenteswendy shannonentropyusedforfeatureextractionsofopticalpatternsinthecontextofstructuralhealthmonitoring
AT sergiyenkooleg shannonentropyusedforfeatureextractionsofopticalpatternsinthecontextofstructuralhealthmonitoring
AT rodriguezquinonezjulioc shannonentropyusedforfeatureextractionsofopticalpatternsinthecontextofstructuralhealthmonitoring
AT mirandavegajesuse shannonentropyusedforfeatureextractionsofopticalpatternsinthecontextofstructuralhealthmonitoring
AT hernandezbalbuenadaniel shannonentropyusedforfeatureextractionsofopticalpatternsinthecontextofstructuralhealthmonitoring