Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
Titolok-NN and k-NN-ANN Combined Classifier to Assess MOX Gas Sensors Performances Affected by Drift Caused by Early Life Aging
Anno di pubblicazione2020
FormatoElettronico
Autore/iMarco Abbatangelo, Estefanía Núñez-Carmona, Veronica Sberveglieri, Elisabetta Comini, Giorgio Sberveglieri
Affiliazioni autoriDepartment of Information Engineering, University of Brescia, Brescia, via Branze, 38, 25123 Brescia, Italy; elisabetta.comini@unibs.it (E.C.); giorgio.sberveglieri@nasys.it (G.S.) 2 CNR-IBBR, Institute of Bioscience and Bioresources, via Madonna del Piano, 10, 50019 Sesto Fiorentino, Italy; estefania.nunezcarmona@ibbr.cnr.it (E.N.-C.); veronica.sberveglieri@ibbr.cnr.it (V.S.) 3 Nano Sensor Systems, NASYS Spin-O University of Brescia, Brescia, via Camillo Brozzoni, 9, 25125 Brescia, Italy
Autori CNR e affiliazioni
  • ESTEFANIA NUNEZ CARMONA
  • VERONICA SBERVEGLIERI
Lingua/e
  • inglese
AbstractThe drift of metal oxide semiconductor (MOX) chemical sensors is one of the most important topics in this field. The work aims to test the performance of MOX gas sensors over the aging process. Firstly, sensorswere testedwith ethanol to understand their behavior and response changes. In parallel, beers with different alcoholic content were analyzed to assess what happened in a real application scenario. With ethanol analysis, it was possible to quantify drift of the baseline of the sensors and changes that could affect their responses over time (from day 1 to day 51). Conversely, the beer dataset has been exploited to evaluate how two different classifiers perform the classification task based on the alcohol content of the samples. A hybrid k-nearest neighbors artificial neural network (k-NN-ANN) approach and "standard" k-NN were used to evaluate to distinguish among the samples when the measures were affected by drift. To achieve this goal, data acquired from day one to day six were used as training to predict data collected up to day 51. Overall, performances of the two methods were similar, even if the best result in terms of accuracy is reached by k-NN-ANN (96.51%).
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a-
Pagine totali-
RivistaChemosensors
Paese di pubblicazione: Svizzera
Lingua: inglese
ISSN: 2227-9040
Titolo chiave: Chemosensors
Numero volume della rivista-
Fascicolo della rivista-
DOI10.3390/chemosensors8010006
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveMOX sensors, drift counteraction, ANN, beer, k-NN, aging
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IBBR — Istituto di Bioscienze e Biorisorse
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
k-NN and k-NN-ANN Combined Classifier to Assess MOX Gas Sensors Performances A (documento privato )
Tipo documento: application/pdf