PID Sensor Reading Calibration for Vigi E-Nose System Using Deep Neural Network

Page view(s)
49
Checked on Jun 11, 2024
PID Sensor Reading Calibration for Vigi E-Nose System Using Deep Neural Network
Title:
PID Sensor Reading Calibration for Vigi E-Nose System Using Deep Neural Network
Journal Title:
The 17th IEEE Conference on Industrial Electronics and Applications (ICIEA 2022)
DOI:
Publication Date:
19 December 2022
Citation:
NA
Abstract:
Sensors are widely used to monitor the urban environment and human activities. Due to the nature of chemical sensors, they suffer seriously to the environment. Especially for PID sensors, its sensitivity may degrade over time which results in an unreliable reading. To solve this problem, sensor calibration is adopted. However, regular calibration can only capture the status at the time of calibration, while the sensitivity degradation is not captured in between the period of two calibrations. In this paper, the sensitivity degradation pattern of PID sensor is studied and modeled using deep neural network. The refined base sensitivity is used to re-calibrate the sensor reading to improve the reliability of the gas concentration measurement of the Vigi E-nose system.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Singapore Ministry of National Development / National Research Foundation, Prime Minister’s Office - Land and Liveability National Innovation Challenge (L2 NIC) Research Programme
Grant Reference no. : L2NICTDF1-2017-3
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
978-1-6654-0983-4
Files uploaded:

File Size Format Action
2022202004.pdf 787.40 KB PDF Request a copy