Application of Random Forest Method for Analysis of Gas Sensor Readouts From Mold-threatened Buildings

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F23%3A00370799" target="_blank" >RIV/68407700:21110/23:00370799 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1063/5.0147200" target="_blank" >http://dx.doi.org/10.1063/5.0147200</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/5.0147200" target="_blank" >10.1063/5.0147200</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of Random Forest Method for Analysis of Gas Sensor Readouts From Mold-threatened Buildings

Popis výsledku v původním jazyce

Humidity increase in buildings frequently leads to the growth of mold, which is one of significant factors for evaluation of Sick Building Syndrome. The assessment of mold contamination level in buildings based on gas sensors array readouts is considered as a cheap and fast detection technique; nonetheless, interpretation of signals is quite complex, mostly because the signals obtained from sensors are multidimensional. Furthermore, there is no sole reference method used in practice. The signals analyzed in the original multi-dimensional space are characterized by high variability, depending on the conditions in the tested buildings. In such a situation, the random forest methodology can be applied, which till now has been successfully involved to a wide range of prediction problems and has few parameters to tune. Aside from being simple to use, the method is generally recognized for its accuracy and ability to deal with small sample sizes and high-dimensional feature spaces. At the same time, it is easily parallelizable and therefore has the potential to deal with large real-life systems. This supervised learning procedure operates according to the simple but effective “divide and conquer” principle: sample fractions of the data, grow a randomized tree predictor on each small piece, then paste (aggregate) these predictors together.

Název v anglickém jazyce

Application of Random Forest Method for Analysis of Gas Sensor Readouts From Mold-threatened Buildings

Popis výsledku anglicky

Humidity increase in buildings frequently leads to the growth of mold, which is one of significant factors for evaluation of Sick Building Syndrome. The assessment of mold contamination level in buildings based on gas sensors array readouts is considered as a cheap and fast detection technique; nonetheless, interpretation of signals is quite complex, mostly because the signals obtained from sensors are multidimensional. Furthermore, there is no sole reference method used in practice. The signals analyzed in the original multi-dimensional space are characterized by high variability, depending on the conditions in the tested buildings. In such a situation, the random forest methodology can be applied, which till now has been successfully involved to a wide range of prediction problems and has few parameters to tune. Aside from being simple to use, the method is generally recognized for its accuracy and ability to deal with small sample sizes and high-dimensional feature spaces. At the same time, it is easily parallelizable and therefore has the potential to deal with large real-life systems. This supervised learning procedure operates according to the simple but effective “divide and conquer” principle: sample fractions of the data, grow a randomized tree predictor on each small piece, then paste (aggregate) these predictors together.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20506 - Coating and films

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Central European Symposium on Thermophysics 2022 (CEST 2022)

ISBN

—

ISSN

1551-7616

e-ISSN

1551-7616

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

AIP Conference Proceedings

Místo vydání

New York

Místo konání akce

Olomouc

Datum konání akce

31. 8. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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