Robust Classification of Largely Corrupted Electronic Nose Data Using Deep Neural Networks
Youngjoon Yoo, Hyun-Il Kim, Sang‐Il Choi
Abstract
Data loss for electronic noses may occur because of the sensor’s installation environment or from electrical disturbances. As a result, electronic noses may experience difficulties when identifying gases. This paper proposes two deep neural network-based functions for identifying gases. First, a denoising auto-encoder based on the corruption reconstruction method is proposed for electronic nose data to solve this problem. Second, a convolutional neural network-based gas-classifying model is proposed. Although the electronic nose data are highly discriminative, they are sensitive to the corruption of information; hence, they require an efficient restoration method for practical use. From the experiments we demonstrate that the proposed denoising auto-encoder provides a strong restoration capability, and the convolutional neural network-based classifier successfully discriminates the gas data samples with a classification rate over 95% even when the data loss is 50%.