Encoded Image-Based Time Series Classification for Improving Colorimetric Detection of Hydrogen Sulfide (H2S)

초록

In this study, a time series data analysis technique using a convolutional neural network (CNN), that performs multidimensional image encoding, is used to improve the accuracy of hydrogen sulfide gas detection. According to a recent study, the time -series data image-encoding technique is effective under specific conditions. The novelty of this study lies in the use of a time-series-based colorimetric analysis method developed using colorimetric fabric detection data from sensors to estimate hydrogen sulfide gas exposure levels. Time series data obtained through gas experiments are imageencoded to classify the color value change trend of a dyed fabric induced by its chemical reaction with hydrogen sulfide gas. The results show that learning using encoded image training data improves model performance in estimating gas exposure levels compared to the non-encoded image method.

키워드