Open-set adversarial domain match for electronic nose drift compensation and unknown gas recognition

Electronic nose (EN) is widely used for gas classification in practical applications. In the long-term open environments work, there often exists the unknown gases that the electronic nose cannot predict in advance. ENs need to resist the interference of these unknown gases in addition to overcoming long-term sensor drift problem. However, the present research cannot solve both the sensor drift and unknown gas intrusion problem simultaneously well. In this work, we unify above problems in to the open-set risk boundary. We propose an open-set adversarial domain match (OSADM) model and introduce the considers of open-set domain adaptation (OSDA). OSDA trains a target classifier through matching the domain distribution to recognize the known and unknown gases. First, a binary adversarial loss divides the class boundary. Secondly, adversarial domain adaptation unifies the distribution of different domains. Compared with the metric methods, it avoids complex distribution computation and parameter adjustment to reduce negative transfer. Extensive experimental results on two benchmark datasets, Gas Sensor Array Drift and Twin gas sensor arrays Dataset show that OSADM outperformance of the existing open-set models.