TY - GEN
T1 - ML-based humidity and temperature calibration system for heterogeneous mox sensor array in ppm-level BTEX monitoring
AU - Kim, Sujin
AU - Sung, Hoyong
AU - Kim, Sohyeon
AU - Je, Minkyu
AU - Kim, Ji Hoon
N1 - Funding Information:
This work was supported in part by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT & Future Planning as Global Frontier Project (CISS-2-2018-0648-001-3), and in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-0-01847) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation)
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Recently, indoor air quality is an important issue for human health and high concentrations of toxic Volatile Organic Compounds (VOCs) gases such as BTEX (Benzene, Toluene, Ethylbenzene, and Xylene) are very harmful to our respiratory system and metabolism. To detect BTEX gases at indoors, Metal Oxide (MOx) sensors are widely used because of their low-cost and high sensitivity. MOx sensors are easily affected by temperature and humidity, hence it is difficult to detect BTEX gases accurately without additional calibration process. In this paper, we present the calibration system for heterogeneous MOx sensor array where machine learning (ML)-based techniques, Linear Regression (LR), Non-Linear Curve Fitting (NLCF), and Artificial Neural Network (ANN), are exploited to reduce the impact of temperature and humidity. For the performance evaluation, we have setup the gas concentration measurement system and recorded the sensor outputs from Temperature-Cycled Operation (TCO) responses of five heterogenous MOx sensors. The proposed calibration system with ANN-based calibration system shows the reduction of gas sensors variation due to temperature and humidity 73% on average, and presents maximum 92% reduction for benzene, 75% for toluene, 83% for ethylbenzene, and 91% for xylene gases, respectively.
AB - Recently, indoor air quality is an important issue for human health and high concentrations of toxic Volatile Organic Compounds (VOCs) gases such as BTEX (Benzene, Toluene, Ethylbenzene, and Xylene) are very harmful to our respiratory system and metabolism. To detect BTEX gases at indoors, Metal Oxide (MOx) sensors are widely used because of their low-cost and high sensitivity. MOx sensors are easily affected by temperature and humidity, hence it is difficult to detect BTEX gases accurately without additional calibration process. In this paper, we present the calibration system for heterogeneous MOx sensor array where machine learning (ML)-based techniques, Linear Regression (LR), Non-Linear Curve Fitting (NLCF), and Artificial Neural Network (ANN), are exploited to reduce the impact of temperature and humidity. For the performance evaluation, we have setup the gas concentration measurement system and recorded the sensor outputs from Temperature-Cycled Operation (TCO) responses of five heterogenous MOx sensors. The proposed calibration system with ANN-based calibration system shows the reduction of gas sensors variation due to temperature and humidity 73% on average, and presents maximum 92% reduction for benzene, 75% for toluene, 83% for ethylbenzene, and 91% for xylene gases, respectively.
KW - BTEX
KW - Calibration
KW - Gas sensor
KW - Heterogeneous sensor array
KW - Machine learning
KW - MOx sensor
KW - Smart sensory systems
UR - http://www.scopus.com/inward/record.url?scp=85109011111&partnerID=8YFLogxK
U2 - 10.1109/ISCAS51556.2021.9401413
DO - 10.1109/ISCAS51556.2021.9401413
M3 - Conference contribution
AN - SCOPUS:85109011111
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 May 2021 through 28 May 2021
ER -
