Adaptive K-NN for the detection of air pollutants with a sensor array (Articolo in rivista)

Type
Label
  • Adaptive K-NN for the detection of air pollutants with a sensor array (Articolo in rivista) (literal)
Anno
  • 2004-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/JSEN.2004.823653 (literal)
Alternative label
  • Roncaglia A; Elmi I; Dori L; Rudan M (2004)
    Adaptive K-NN for the detection of air pollutants with a sensor array
    in IEEE sensors journal
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Roncaglia A; Elmi I; Dori L; Rudan M (literal)
Pagina inizio
  • 248 (literal)
Pagina fine
  • 256 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1271275&contentType=Journals+%26+Magazines&searchField%3DSearch_All%26queryText%3DAdaptive+K-NN+for+the+detection+of+air+pollutants+with+a+sensor+array (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 4 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Roncaglia A, Elmi I, Dori L - CNR IMM Bologna Italy; Rudan M - Univ Bologna, DEIS, I-40136 Bologna, Italy (literal)
Titolo
  • Adaptive K-NN for the detection of air pollutants with a sensor array (literal)
Abstract
  • The field of air-quality monitoring is gaining increasing interest, with regard to both indoor environment and air-pollution control in open space. This work introduces a pattern recognition technique based on adaptive K-nn applied to a multisensor system, optimized for the recognition of some relevant tracers for air pollution in outdoor environment, namely benzene, toluene, and xylene (BTX), NO2, and CO. The pattern-recognition technique employed aims at recognizing the target gases within an air sample of unknown composition and at estimating their concentrations. It is based on PCA and K-nn classification with an adaptive vote technique based on the gas concentrations of the training samples associated to the K-neighbors. The system is tested in a controlled environment composed of synthetic air with a fixed humidity rate (30%) at concentrations in the ppm range for BTX and NO2, in the range of 10 ppm for CO. The pattern recognition technique is experimented on a knowledge base composed of a limited number of samples (130), with the adoption of a leave-one-out procedure in order to estimate the classification probability. In these conditions, the system demonstrates the capability to recognize the presence of the target gases in controlled conditions with a high hit-rate. Moreover, the concentrations of the individual components of the test samples are successfully estimated for BTX and NO2 in more than 80% of the considered cases, while a lower hit-rate (69%) is reached for CO. (literal)
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