Citazione: Vincenzo Di Lecce, Marco Calabrese, Discriminating Gaseous Emission Patterns in Low-Cost Sensor Setups, Proceedings of the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2011), Sep 19-21, 2011, Ottawa, Canada, pp. 1-6, ISBN:978-1-61284-924-9, ISSN: 2159-1547.
Abstract: This work presents a two-step heuristic that employs extremely low-cost sensors for gaseous emission event discrimination. These events are triggered by particular patterns of sensor responses possibly occurring when a certain gas is emitted; patterns are then used to produce human understandable inference rules describing the kind of emission measured. The technique, challenged by the high crosssensitivity of the employed sensors, is based on two steps: first,sensor response patterns are extracted (unsupervisedly) from measurement signals by means of a recently proposed computational intelligence technique; second, a ‘credibility index’ is applied (supervisedly) to each pattern via fuzzy membership functions. The outcome is a set of IF THEN statements weighted by fuzzy constraints. Experiments show that such inferences allow for accurate gaseous emission event discrimination.
Keyword: low-cost sensors; cross-sensitivity; gas emission event discrimination; patterns; membership functions; inference;