Gas Source Declaration With Tetrahedral Sensing Geometries and Median Value Filtering Extreme Learning Machine

• Main Authors: Hui-Rang Hou, Achim J. Lilienthal, Qing-Hao Meng
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Gas source declaration; tetrahedron; gas concentration measurement; wind information; extreme learning machine; median value filtering
• Online Access: https://ieeexplore.ieee.org/document/8945323/

Gas source localization (including gas source declaration) is critical for environmental monitoring, pollution control and chemical safety. In this paper we approach the gas source declaration problem by constructing a tetrahedron, each vertex of which consists of a gas sensor and a three-dimensional (3D) anemometer. With this setup, the space sampled around a gas source can be divided into two categories, i.e. inside (“source in”) and outside (“source out”) the tetrahedron, posing gas source declaration as a classification problem. For the declaration of the “source in” or “source out” cases, we propose to directly take raw gas concentration and wind measurement data as features, and apply a median value filtering based extreme learning machine (M-ELM) method. Our experimental results show the efficacy of the proposed method, yielding accuracies of 93.2% and 100% for gas source declaration in the regular and irregular tetrahedron experiments, respectively. These results are better than that of the ELM-MFC (mass flux criterion) and other variants of ELM algorithms.