Prototype-Optimized unsupervised domain adaptation via dynamic Transformer encoder for sensor drift compensation in electronic nose systems

Abstract

In the field of electronic nose systems, sensor drift poses a significant challenge, affecting the reliability and accuracy of gas detection. Current solutions often require labeled data and fail to generalize well across different domains. This paper presents a novel, unsupervised domain adaptation framework for sensor drift compensation, leveraging a dynamic Transformer-based encoder and prototype learning. Our approach extracts semantic representations from source domain data and aligns instances to prototypes for knowledge transfer across domains. A dynamic prototype-guided classification model is deployed for drift compensation. The key contributions of this work include the introduction of prototype-optimized unsupervised learning and the development of an end-to-end drift compensation model. Experimental results on two gas sensor datasets demonstrate superior performance over existing unsupervised and semi-supervised methods, validating the effectiveness of our approach.