Data–driven modeling of nano-nose gas sensor arrays

Publication: ResearchArticle in proceedings – Annual report year: 2010

Standard

Data–driven modeling of nano-nose gas sensor arrays. / Alstrøm, Tommy Sonne; Larsen, Jan; Nielsen, Claus Højgård; Larsen, Niels Bent.

SPIE: Defense, Security and Sensing. Vol. 7697 2010.

Publication: ResearchArticle in proceedings – Annual report year: 2010

Harvard

Alstrøm, TS, Larsen, J, Nielsen, CH & Larsen, NB 2010, 'Data–driven modeling of nano-nose gas sensor arrays'. in SPIE: Defense, Security and Sensing. vol. 7697.

APA

Alstrøm, T. S., Larsen, J., Nielsen, C. H., & Larsen, N. B. (2010). Data–driven modeling of nano-nose gas sensor arrays. In SPIE: Defense, Security and Sensing. (Vol. 7697)

CBE

Alstrøm TS, Larsen J, Nielsen CH, Larsen NB. 2010. Data–driven modeling of nano-nose gas sensor arrays. In SPIE: Defense, Security and Sensing.

MLA

Vancouver

Alstrøm TS, Larsen J, Nielsen CH, Larsen NB. Data–driven modeling of nano-nose gas sensor arrays. In SPIE: Defense, Security and Sensing. Vol. 7697. 2010.

Author

Alstrøm, Tommy Sonne; Larsen, Jan; Nielsen, Claus Højgård; Larsen, Niels Bent / Data–driven modeling of nano-nose gas sensor arrays.

SPIE: Defense, Security and Sensing. Vol. 7697 2010.

Publication: ResearchArticle in proceedings – Annual report year: 2010

Bibtex

@inbook{afa1f7c35ae9409cab2dc2e9761a7408,
title = "Data–driven modeling of nano-nose gas sensor arrays",
author = "Alstrøm, {Tommy Sonne} and Jan Larsen and Nielsen, {Claus Højgård} and Larsen, {Niels Bent}",
year = "2010",
volume = "7697",
booktitle = "SPIE",

}

RIS

TY - GEN

T1 - Data–driven modeling of nano-nose gas sensor arrays

A1 - Alstrøm,Tommy Sonne

A1 - Larsen,Jan

A1 - Nielsen,Claus Højgård

A1 - Larsen,Niels Bent

AU - Alstrøm,Tommy Sonne

AU - Larsen,Jan

AU - Nielsen,Claus Højgård

AU - Larsen,Niels Bent

PY - 2010

Y1 - 2010

N2 - We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence the number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state-of-the-art machine learning methods and the Bayesian learning paradigm.

AB - We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence the number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state-of-the-art machine learning methods and the Bayesian learning paradigm.

KW - Principal Component Analysis (PCA)

KW - Non–negative Matrix Factorization (NMF)

KW - Polymer Coated Quartz Crystal Microbalance Sensor (QCM)

KW - Principal Component Regression (PCR)

KW - aussian Process Regression (GPR)

KW - Artificial Neural Network (ANN)

UR - http://www.imm.dtu.dk/pubdb/p.php?5869

VL - 7697

BT - SPIE

T2 - SPIE

ER -