Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Sensors and Materials, Volume 23, Number 1 (2011)
Copyright(C) MYU K.K.
pp. 71-85
S&M827 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2011.710
Published: January 28, 2011

Humidity Compensation by Neural Network for Bad-Smell Sensing System Using Gas Detector Tube and Built-in Camera [PDF]

Takamichi Nakamoto, Tomohiro Ikeda, Hiroyuki Hirano and Takemi Arimoto

(Received May 31, 2010; Accepted August 30, 2010)

Keywords: gas detector tube, humidity, neural network, bad smell, FPGA

A cheap and rapid sensing system is required for detecting bad smells and volatile organic compounds (VOCs). Although detection using a gas detector tube is a simple gas detection method, its measurement process has not been automated. We studied an automated measurement system for gas detector tubes using a built-in camera. Although the measurement was automated using our system, another problem was revealed. Because digital cameras are sensitive to color changes, a slight change due to humidity, which is not a problem in manual inspections, cannot be ignored in our system. Thus, a humidity sensor was added to the system. However, simple humidity compensation methods such as linear regression did not work because the humidity affected the data in a complicated manner. Therefore, a neural network was used for humidity compensation. Both the discoloration area and humidity data were input to the neural network. As a result, accurate concentration estimation was successfully performed.

Corresponding author: Takamichi Nakamoto


Cite this article
Takamichi Nakamoto, Tomohiro Ikeda, Hiroyuki Hirano and Takemi Arimoto, Humidity Compensation by Neural Network for Bad-Smell Sensing System Using Gas Detector Tube and Built-in Camera, Sens. Mater., Vol. 23, No. 1, 2011, p. 71-85.



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