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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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 |
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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 |
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