TY - JOUR
T1 - A Study of Drift Effect in a Popular Metal Oxide Sensor and Gas Recognition Using Public Gas Datasets
AU - Chang, Il Sik
AU - Byun, Sung Woo
AU - Lim, Tae Beom
AU - Park, Goo Man
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2023
Y1 - 2023
N2 - Metal oxide sensor is widely used in many research fields, including E-nose for gas detection due to their tunable sensitivity, space efficiency and low cost. One of the most popular open data sets in electronic nose research contains data on various gases sampled using a MOx sensor in a wind tunnel over 16 months. A recent study published in 2022 by Nik Dennler has reported the discovery of the drift effect of a public dataset due to incorrect experimental design. they reported that the order of gas collection was not randomized and further discovered that a select set of gases were collected over a particular period. This paper expands the previous paper, by analyzing the drift effect with low signal, zero-offset subtracted signal's mean, and standard deviation value by location and time, and examining it with TSNE, a dimensional reduction method. In addition, the accuracy by time and location was analyzed by applying it to various Deep Learning methods. According to the results, we confirmed that gas information is already classified before the gas leaks in terms of temporal and spatial domain. Therefore, the classification accuracy overestimates the actual accuracy that can be obtained due to the drift effect. Based on the results of this study, it is necessary to thoroughly verify the temporal and spatial validity of the gas dataset when using the publicly available gas dataset to develop gas detection algorithms.
AB - Metal oxide sensor is widely used in many research fields, including E-nose for gas detection due to their tunable sensitivity, space efficiency and low cost. One of the most popular open data sets in electronic nose research contains data on various gases sampled using a MOx sensor in a wind tunnel over 16 months. A recent study published in 2022 by Nik Dennler has reported the discovery of the drift effect of a public dataset due to incorrect experimental design. they reported that the order of gas collection was not randomized and further discovered that a select set of gases were collected over a particular period. This paper expands the previous paper, by analyzing the drift effect with low signal, zero-offset subtracted signal's mean, and standard deviation value by location and time, and examining it with TSNE, a dimensional reduction method. In addition, the accuracy by time and location was analyzed by applying it to various Deep Learning methods. According to the results, we confirmed that gas information is already classified before the gas leaks in terms of temporal and spatial domain. Therefore, the classification accuracy overestimates the actual accuracy that can be obtained due to the drift effect. Based on the results of this study, it is necessary to thoroughly verify the temporal and spatial validity of the gas dataset when using the publicly available gas dataset to develop gas detection algorithms.
KW - artificial olfactory
KW - drift effect
KW - electronic nose system
KW - gas recognition
KW - Metal oxide sensor
UR - http://www.scopus.com/inward/record.url?scp=85151342016&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3257414
DO - 10.1109/ACCESS.2023.3257414
M3 - Article
AN - SCOPUS:85151342016
SN - 2169-3536
VL - 11
SP - 26383
EP - 26392
JO - IEEE Access
JF - IEEE Access
ER -