A Collaborative Strategy for Multiple Intelligent Sensory Technologies Based on a Discriminant Extreme Learning Machine for Detecting Gases

Factory emissions, vehicle emissions, and fossil fuel combustion, along with other activities, release a significant amount of harmful gases that pose a serious threat to human health and the environment. However, when detection is performed via a single sensing mode alone, the accuracy of identification often fails to reach ideal results. In this paper, we propose a guided fusion model based on a discriminant extreme learning machine framework (GF-DELM) to fuse gas sensor data and thermal image data for industrial gas detection; this model achieves effective fusion by purposefully constraining the fusion space of extreme learning machines. Specifically, to enhance the separability of the fused data between different categories, we consider controlling the within-class and between-class dispersion during the fusion process, aiming to minimize within-class while maximizing between-class distances. Additionally, this method further strengthens the relationship between the fused features and labels via the Hilbert–Schmidt independence criterion (HSIC). The experimental results demonstrate that the proposed fusion model outperforms the other fusion methods. Our approach can be further extended to various other gas detection applications by incorporating a range of sensors and additional modal data, thus displaying significant potential for practical implementation.