Pingling Deng

TL;DR: In this paper, the authors proposed a cross-domain discriminative subspace learning (CDSL) method for multiple electronic noses (E-noses), machine olfaction odor perception systems.

TL;DR: Experiments on several datasets by an E-nose system fabricated in the laboratory prove that the proposed SEM and SE methods are significantly effective for real-time abnormal odor detection.

TL;DR: Experiments demonstrate that the proposed CdELM method significantly outperforms other compared methods on electronic nose olfaction datasets.

TL;DR: In this paper, an electronic nose gas identification method based on source domain migration extreme learning to realize drift compensation is proposed, which is used from the perspective of machine learning and used for solving the problem of sensor drift instead of direct correction for single sensor response; a source data set and a target domain data set are built according to labeled gas sensor array sense data matrixes collected by an electronic noses before drift and after drift respectively and are taken as inputs of an extreme learning machine for training an identification classifier of the electronic nose, so that the tolerability performance of the identification class