Density-based clustering of E-nose output from mold-contaminated buildings

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F21%3A00357158" target="_blank" >RIV/68407700:21110/21:00357158 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1063/5.0070164" target="_blank" >https://doi.org/10.1063/5.0070164</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/5.0070164" target="_blank" >10.1063/5.0070164</a>

Result language

angličtina

Original language name

Density-based clustering of E-nose output from mold-contaminated buildings

Original language description

Increase of humidity in building envelopes often leads to the growth of mold, which is one of important factors for evaluation of Sick Building Syndrome. The estimation of mold contamination level in buildings based on electronic nose application is considered as fast and early detection technique, however interpretation of readouts is quite complicated, mostly because the signals obtained from sensor arrays are multidimensional. Moreover, there is no single optimal reference method used in practice. The idea of the presented approach is to group the readouts from sensor array into homogeneous sets of observations, which are characterized by the different level of mold contamination. The signals analyzed in the original 8-dimensional space are characterized by high variability depending on the conditions prevailing in the tested rooms, while the set of readouts may have a rather complicated shape (spherical-shaped clusters or convex clusters). In such a situation, the cluster analysis method based on density of signals could be applied. The most well-known density- based clustering method is DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Unlike k-means or k-median, DBSCAN does not require the number of clusters as a parameter. Instead, it infers the number of clusters based on the data, and it can discover clusters of arbitrary shape (for comparison, k-means usually discovers spherical clusters). DBSCAN requires two parameters: ϵ (eps) and the minimum number of points required to form a dense region (minPts).

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

21100 - Other engineering and technologies

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Article name in the collection

AIP Conference Proceedings 2429

ISBN

978-0-7354-4139-2

ISSN

0094-243X

e-ISSN

1551-7616

Number of pages

6

Pages from-to

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Publisher name

AIP Conference Proceedings

Place of publication

New York

Event location

Kazimierz Dolny

Event date

Sep 1, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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