Classifying Gas Data Measured Under Multiple Conditions Using Deep Learning

Abstract

Gas classification is a machine learning problem that is important for various applications including monitoring systems, health care, public security, etc. Since measuring the characteristic of gas molecules is greatly affected by external factors such as wind speed and the internal setting of detecting sensors, classification should be done by taking into account the combination of these individual factors, which we call a condition in this paper. In particular, when classifying gas data measured under multiple conditions, the data from each condition need to be integrated, which we call multi-conditioned gas classification. While there have been some studies on gas classification for a single condition, no previous approach deals with the multi-conditioned gas classification problem to the best of our knowledge. In this paper, we propose a novel multi-conditioned gas classification method for the first time. We present a new deep learning network structure that can efficiently extract features from the data of multiple conditions and effectively integrate them, which is referred to as a multi-conditioned gas classification network (MCGCN). We also propose a new training loss function to guarantee good performance reliably for the varying number of given conditions. Experimental results demonstrate the superiority of the proposed method, which achieves accuracies of 99.15% ± 0.41 regardless of the number of conditions with 15 times fewer model parameters in comparison to the existing method.

Flicker Noise in Resistive Gas Sensors—Measurement Setups and Applications for Enhanced Gas Sensing

“…The estimated power spectrum can determine Lorentzian frequencies or local slopes [ 7 , 10 , 98 , 99 ]. Next, the detection algorithm can determine the ambient atmosphere’s composition similarly to recorded DC resistance changes, using various chemometric or machine learning methods [ 100 , 101 , 102 , 103 , 104 , 105 ]. Before application, the algorithms require data reduction, eventually through a principal component analysis (PCA) method [ 106 ].…”