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 cross-sensitivity 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.

Discriminating Gas Emission Patterns in Low-Cost Sensor Setups / DI LECCE, Vincenzo; Calabrese, M.. - STAMPA. - (2011), pp. 97-102. (Intervento presentato al convegno 9th IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2011 tenutosi a Ottawa, Canada nel September 19-21, 2011) [10.1109/CIMSA.2011.6059926].

Discriminating Gas Emission Patterns in Low-Cost Sensor Setups

DI LECCE, Vincenzo;
2011-01-01

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 cross-sensitivity 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.
2011
9th IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2011
978-1-61284-924-9
Discriminating Gas Emission Patterns in Low-Cost Sensor Setups / DI LECCE, Vincenzo; Calabrese, M.. - STAMPA. - (2011), pp. 97-102. (Intervento presentato al convegno 9th IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2011 tenutosi a Ottawa, Canada nel September 19-21, 2011) [10.1109/CIMSA.2011.6059926].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/17989
Citazioni
  • Scopus 6
  • ???jsp.display-item.citation.isi??? ND
social impact