Dust deposition and pollution are relevant issues in indoor environments, especially concerning human health and conservation of things and works. In this framework, several tools have been proposed in the last years in order to analyze dust deposition and extract useful information for addressing the phenomenon. In this paper, a novel approach for dust analysis and classification is proposed, employing machine learning and fuzzy logic to set up a simple and actual tool. The proposed approach is tested and compared with other already introduced similar techniques, in order to evaluate its performance.

Proietti, A., Liparulo, L., Leccese, F., & Panella, M. (2016). Shapes classification of dust deposition using fuzzy kernel-based approaches. MEASUREMENT, 77(January 2016), 344-350 [10.1016/j.measurement.2015.09.025].

Shapes classification of dust deposition using fuzzy kernel-based approaches

LECCESE, Fabio;
2016

Abstract

Dust deposition and pollution are relevant issues in indoor environments, especially concerning human health and conservation of things and works. In this framework, several tools have been proposed in the last years in order to analyze dust deposition and extract useful information for addressing the phenomenon. In this paper, a novel approach for dust analysis and classification is proposed, employing machine learning and fuzzy logic to set up a simple and actual tool. The proposed approach is tested and compared with other already introduced similar techniques, in order to evaluate its performance.
Proietti, A., Liparulo, L., Leccese, F., & Panella, M. (2016). Shapes classification of dust deposition using fuzzy kernel-based approaches. MEASUREMENT, 77(January 2016), 344-350 [10.1016/j.measurement.2015.09.025].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/309038
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