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Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores
Noises such as thermal noise, background noise or burst noise can reduce the reliability and confidence of measurement devices. In this work, a recursive and adaptive Kalman filter is proposed to detect and process burst noise or outliers and thermal noise, which are popular in electrical and electr...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891277/ https://www.ncbi.nlm.nih.gov/pubmed/31717590 http://dx.doi.org/10.3390/s19224900 |
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author | Pham, Son Dinh, Anh |
author_facet | Pham, Son Dinh, Anh |
author_sort | Pham, Son |
collection | PubMed |
description | Noises such as thermal noise, background noise or burst noise can reduce the reliability and confidence of measurement devices. In this work, a recursive and adaptive Kalman filter is proposed to detect and process burst noise or outliers and thermal noise, which are popular in electrical and electronic devices. The Kalman filter and neural network are used to preprocess data of three detectors of a nondispersive thermopile device, which is used to detect and quantify Fusarium spores. The detectors are broadband (1 µm to 20 µm), [Formula: see text] (6.09 ± 0.06 µm) and [Formula: see text] (9.49 ± 0.44 µm) thermopiles. Additionally, an artificial neural network (NN) is applied to process background noise effects. The adaptive and cognitive Kalman Filter helps to improve the training time of the neural network and the absolute error of the thermopile data. Without applying the Kalman filter for [Formula: see text] thermopile, it took 12 min 09 s to train the NN and reach the absolute error of 2.7453 × 10(4) (n. u.). With the Kalman filter, it took 46 s to train the NN to reach the absolute error of 1.4374 × 10(4) (n. u.) for [Formula: see text] thermopile. Similarly, to the [Formula: see text] (9.49 ± 0.44 µm) thermopile, the training improved from 9 min 13 s to 1 min and the absolute error of 2.3999 × 10(5) (n. u.) to the absolute error of 1.76485 × 10(5) (n. u.) respectively. The three-thermopile system has proven that it can improve the reliability in detection of Fusarium spores by adding the broadband thermopile. The method developed in this work can be employed for devices that encounter similar noise problems. |
format | Online Article Text |
id | pubmed-6891277 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68912772019-12-12 Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores Pham, Son Dinh, Anh Sensors (Basel) Article Noises such as thermal noise, background noise or burst noise can reduce the reliability and confidence of measurement devices. In this work, a recursive and adaptive Kalman filter is proposed to detect and process burst noise or outliers and thermal noise, which are popular in electrical and electronic devices. The Kalman filter and neural network are used to preprocess data of three detectors of a nondispersive thermopile device, which is used to detect and quantify Fusarium spores. The detectors are broadband (1 µm to 20 µm), [Formula: see text] (6.09 ± 0.06 µm) and [Formula: see text] (9.49 ± 0.44 µm) thermopiles. Additionally, an artificial neural network (NN) is applied to process background noise effects. The adaptive and cognitive Kalman Filter helps to improve the training time of the neural network and the absolute error of the thermopile data. Without applying the Kalman filter for [Formula: see text] thermopile, it took 12 min 09 s to train the NN and reach the absolute error of 2.7453 × 10(4) (n. u.). With the Kalman filter, it took 46 s to train the NN to reach the absolute error of 1.4374 × 10(4) (n. u.) for [Formula: see text] thermopile. Similarly, to the [Formula: see text] (9.49 ± 0.44 µm) thermopile, the training improved from 9 min 13 s to 1 min and the absolute error of 2.3999 × 10(5) (n. u.) to the absolute error of 1.76485 × 10(5) (n. u.) respectively. The three-thermopile system has proven that it can improve the reliability in detection of Fusarium spores by adding the broadband thermopile. The method developed in this work can be employed for devices that encounter similar noise problems. MDPI 2019-11-09 /pmc/articles/PMC6891277/ /pubmed/31717590 http://dx.doi.org/10.3390/s19224900 Text en © 2019 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pham, Son Dinh, Anh Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores |
title | Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores |
title_full | Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores |
title_fullStr | Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores |
title_full_unstemmed | Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores |
title_short | Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores |
title_sort | adaptive-cognitive kalman filter and neural network for an upgraded nondispersive thermopile device to detect and analyze fusarium spores |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891277/ https://www.ncbi.nlm.nih.gov/pubmed/31717590 http://dx.doi.org/10.3390/s19224900 |
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