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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 (&#x201C;source in&#x201D;) and outside (&#x201C;source out&#x201D;) the tetrahedron, posing gas source declaration as a classification problem. For the declaration of the &#x201C;source in&#x201D; or &#x201C;source out&#x201D; 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.

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