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Extrapolation of Calibration Curve of Hot-wire Spirometer Using a Novel Neural Network Based Approach
Hot-wire spirometer is a kind of constant temperature anemometer (CTA). The working principle of CTA, used for the measurement of fluid velocity and flow turbulence, is based on convective heat transfer from a hot-wire sensor to a fluid being measured. The calibration curve of a CTA is nonlinear and...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3662101/ https://www.ncbi.nlm.nih.gov/pubmed/23724368 |
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author | Ardekani, Mohammad Ali Nafisi, Vahid Reza Farhani, Foad |
author_facet | Ardekani, Mohammad Ali Nafisi, Vahid Reza Farhani, Foad |
author_sort | Ardekani, Mohammad Ali |
collection | PubMed |
description | Hot-wire spirometer is a kind of constant temperature anemometer (CTA). The working principle of CTA, used for the measurement of fluid velocity and flow turbulence, is based on convective heat transfer from a hot-wire sensor to a fluid being measured. The calibration curve of a CTA is nonlinear and cannot be easily extrapolated beyond its calibration range. Therefore, a method for extrapolation of CTA calibration curve will be of great practical application. In this paper, a novel approach based on the conventional neural network and self-organizing map (SOM) method has been proposed to extrapolate CTA calibration curve for measurement of velocity in the range 0.7-30 m/seconds. Results show that, using this approach for the extrapolation of the CTA calibration curve beyond its upper limit, the standard deviation is about –0.5%, which is acceptable in most cases. Moreover, this approach for the extrapolation of the CTA calibration curve below its lower limit produces standard deviation of about 4.5%, which is acceptable in spirometry applications. Finally, the standard deviation on the whole measurement range (0.7-30 m/s) is about 1.5%. |
format | Online Article Text |
id | pubmed-3662101 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-36621012013-05-30 Extrapolation of Calibration Curve of Hot-wire Spirometer Using a Novel Neural Network Based Approach Ardekani, Mohammad Ali Nafisi, Vahid Reza Farhani, Foad J Med Signals Sens Original Article Hot-wire spirometer is a kind of constant temperature anemometer (CTA). The working principle of CTA, used for the measurement of fluid velocity and flow turbulence, is based on convective heat transfer from a hot-wire sensor to a fluid being measured. The calibration curve of a CTA is nonlinear and cannot be easily extrapolated beyond its calibration range. Therefore, a method for extrapolation of CTA calibration curve will be of great practical application. In this paper, a novel approach based on the conventional neural network and self-organizing map (SOM) method has been proposed to extrapolate CTA calibration curve for measurement of velocity in the range 0.7-30 m/seconds. Results show that, using this approach for the extrapolation of the CTA calibration curve beyond its upper limit, the standard deviation is about –0.5%, which is acceptable in most cases. Moreover, this approach for the extrapolation of the CTA calibration curve below its lower limit produces standard deviation of about 4.5%, which is acceptable in spirometry applications. Finally, the standard deviation on the whole measurement range (0.7-30 m/s) is about 1.5%. Medknow Publications & Media Pvt Ltd 2012 /pmc/articles/PMC3662101/ /pubmed/23724368 Text en Copyright: © Journal of Medical Signals and Sensors http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Ardekani, Mohammad Ali Nafisi, Vahid Reza Farhani, Foad Extrapolation of Calibration Curve of Hot-wire Spirometer Using a Novel Neural Network Based Approach |
title | Extrapolation of Calibration Curve of Hot-wire Spirometer Using a Novel Neural Network Based Approach |
title_full | Extrapolation of Calibration Curve of Hot-wire Spirometer Using a Novel Neural Network Based Approach |
title_fullStr | Extrapolation of Calibration Curve of Hot-wire Spirometer Using a Novel Neural Network Based Approach |
title_full_unstemmed | Extrapolation of Calibration Curve of Hot-wire Spirometer Using a Novel Neural Network Based Approach |
title_short | Extrapolation of Calibration Curve of Hot-wire Spirometer Using a Novel Neural Network Based Approach |
title_sort | extrapolation of calibration curve of hot-wire spirometer using a novel neural network based approach |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3662101/ https://www.ncbi.nlm.nih.gov/pubmed/23724368 |
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