Classification of Mold-Infested Buildings Using Gas Sensors Readouts and Support Vector Machine

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F20%3A00342887" target="_blank" >RIV/68407700:21110/20:00342887 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1063/5.0025889" target="_blank" >https://doi.org/10.1063/5.0025889</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/5.0025889" target="_blank" >10.1063/5.0025889</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of Mold-Infested Buildings Using Gas Sensors Readouts and Support Vector Machine

Popis výsledku v původním jazyce

Increased humidity of building envelopes frequently leads to the appearance and growth of mold, which is one of the most important factors concerning Sick Building Syndrome evaluation. Detection of Volatile Organic Compounds (VOCs) emitted by the fungi can be performed using gas sensor arrays. Array output in a form of multidimensional electric signals has to be analyzed by means of appropriate statistical methods. The idea presented within this paper is to use Support Vector Machine (SVM) in the classification of the mold-infested buildings because of different admixture of fungal VOCs within their indoor atmosphere. The mappings used by SVM schemes are designed to ensure that dot products of pairs of input data vectors are computed in terms of the variables in the original space, by defining them in terms of a kernel function k(x,y) selected to suit the problem. Using different types of kernel function actual levels of contamination could be assessed based on readouts from a Metal Oxide Semiconductor (MOS) sensors array. SVM method is appropriated for situations where the functional form of the relationship between signals from sensor array and the classes of contamination is unknown or relationship is complex. The ultidimensional sensor readouts pertaining to the air sampled near the building envelopes in varying degree of mold-contamination, compared with clean and synthetic air, are interpreted and presented.

Název v anglickém jazyce

Classification of Mold-Infested Buildings Using Gas Sensors Readouts and Support Vector Machine

Popis výsledku anglicky

Increased humidity of building envelopes frequently leads to the appearance and growth of mold, which is one of the most important factors concerning Sick Building Syndrome evaluation. Detection of Volatile Organic Compounds (VOCs) emitted by the fungi can be performed using gas sensor arrays. Array output in a form of multidimensional electric signals has to be analyzed by means of appropriate statistical methods. The idea presented within this paper is to use Support Vector Machine (SVM) in the classification of the mold-infested buildings because of different admixture of fungal VOCs within their indoor atmosphere. The mappings used by SVM schemes are designed to ensure that dot products of pairs of input data vectors are computed in terms of the variables in the original space, by defining them in terms of a kernel function k(x,y) selected to suit the problem. Using different types of kernel function actual levels of contamination could be assessed based on readouts from a Metal Oxide Semiconductor (MOS) sensors array. SVM method is appropriated for situations where the functional form of the relationship between signals from sensor array and the classes of contamination is unknown or relationship is complex. The ultidimensional sensor readouts pertaining to the air sampled near the building envelopes in varying degree of mold-contamination, compared with clean and synthetic air, are interpreted and presented.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

<a href="/cs/project/GA19-01558S" target="_blank" >GA19-01558S: Vliv biofilmů na tepelně-vlhkostní chování fasádních materiálů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

AIP Conference Proceedings 2275

ISBN

978-0-7354-4005-0

ISSN

—

e-ISSN

1551-7616

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

AIP Publishing, APL, the American Institute of Physics

Místo vydání

Melville, NY

Místo konání akce

Eger

Datum konání akce

2. 9. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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