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Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors

This paper presents a proposal for an Artificial Neural Network (ANN)-based architecture for completion and prediction of data retrieved by underwater sensors. Due to the specific conditions under which these sensors operate, it is not uncommon for them to fail, and maintenance operations are diffic...

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Detalles Bibliográficos
Autores principales: Baladrón, Carlos, Aguiar, Javier M., Calavia, Lorena, Carro, Belén, Sánchez-Esguevillas, Antonio, Hernández, Luis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3304122/
https://www.ncbi.nlm.nih.gov/pubmed/22438720
http://dx.doi.org/10.3390/s120201468
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author Baladrón, Carlos
Aguiar, Javier M.
Calavia, Lorena
Carro, Belén
Sánchez-Esguevillas, Antonio
Hernández, Luis
author_facet Baladrón, Carlos
Aguiar, Javier M.
Calavia, Lorena
Carro, Belén
Sánchez-Esguevillas, Antonio
Hernández, Luis
author_sort Baladrón, Carlos
collection PubMed
description This paper presents a proposal for an Artificial Neural Network (ANN)-based architecture for completion and prediction of data retrieved by underwater sensors. Due to the specific conditions under which these sensors operate, it is not uncommon for them to fail, and maintenance operations are difficult and costly. Therefore, completion and prediction of the missing data can greatly improve the quality of the underwater datasets. A performance study using real data is presented to validate the approach, concluding that the proposed architecture is able to provide very low errors. The numbers show as well that the solution is especially suitable for cases where large portions of data are missing, while in situations where the missing values are isolated the improvement over other simple interpolation methods is limited.
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spelling pubmed-33041222012-03-21 Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors Baladrón, Carlos Aguiar, Javier M. Calavia, Lorena Carro, Belén Sánchez-Esguevillas, Antonio Hernández, Luis Sensors (Basel) Article This paper presents a proposal for an Artificial Neural Network (ANN)-based architecture for completion and prediction of data retrieved by underwater sensors. Due to the specific conditions under which these sensors operate, it is not uncommon for them to fail, and maintenance operations are difficult and costly. Therefore, completion and prediction of the missing data can greatly improve the quality of the underwater datasets. A performance study using real data is presented to validate the approach, concluding that the proposed architecture is able to provide very low errors. The numbers show as well that the solution is especially suitable for cases where large portions of data are missing, while in situations where the missing values are isolated the improvement over other simple interpolation methods is limited. Molecular Diversity Preservation International (MDPI) 2012-02-02 /pmc/articles/PMC3304122/ /pubmed/22438720 http://dx.doi.org/10.3390/s120201468 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Baladrón, Carlos
Aguiar, Javier M.
Calavia, Lorena
Carro, Belén
Sánchez-Esguevillas, Antonio
Hernández, Luis
Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors
title Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors
title_full Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors
title_fullStr Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors
title_full_unstemmed Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors
title_short Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors
title_sort performance study of the application of artificial neural networks to the completion and prediction of data retrieved by underwater sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3304122/
https://www.ncbi.nlm.nih.gov/pubmed/22438720
http://dx.doi.org/10.3390/s120201468
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