Multitask guided gas recognition algorithm on an intelligent MEMS sensor with thermal modulation

Owing to the complexity of analyzing gases with known and unknown chemical interferences, existing single metal–oxide–semiconductor (MOS) gas sensors fail to generate distinguishable signals for different gases. Besides, previous gas recognition algorithms have only extracted local or global features from sensor signals, with no interaction between local and global features. In this paper, an individual microelectromechanical systems (MEMS)-based MOS sensor is applied to generate multivariable signals under pulsed operating modes of an integrated microheater in the MEMS chip. This sensor is able to generate distinguishable gas signals. We also propose a multitask guided gas recognition algorithm that utilizes interacting local and global features of multivariable signals and is able to perform gas classification and quantitative concentration analysis simultaneously. Specifically, to capture the time series information from sensor response signals, a Gramian angular field module is applied to convert the signal into a two-dimensional image. Then, a multi-scale convolutional neural network and Transformers are combined to extract multiscale local and global features, which strengthens the discriminative ability for different gases with different concentrations. Finally, a multitask module is introduced to perform the classification and regression simultaneously. Extensive experiments demonstrate the effectiveness of the proposed algorithm, which achieves an average recognition accuracy of 96.6% for five types of gases: ammonia, ethanol, formaldehyde, water vapor, and methanol. Compared with many classical recognition algorithms, the proposed algorithm yields lower mean absolute error and root mean square error in concentration estimation, which demonstrates its superiority. The pulsed operating modes combined with the proposed algorithm enable individual MEMS sensor to achieve intelligent for gas recognition, which is crucial for applications to the Internet of Things and artificial intelligence.