A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252466" target="_blank" >RIV/61989100:27240/23:10252466 - isvavai.cz</a>
Result on the web
<a href="https://www.cell.com/heliyon/fulltext/S2405-8440(23)03321-2" target="_blank" >https://www.cell.com/heliyon/fulltext/S2405-8440(23)03321-2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.heliyon.2023.e16114" target="_blank" >10.1016/j.heliyon.2023.e16114</a>
Alternative languages
Result language
angličtina
Original language name
A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring
Original language description
The study deals with detection of the occupation of Intelligent Building (IB) using data obtained from indirect methods with Big Data Analysis within IoT. In the area of daily living activity monitoring, one of the most challenging tasks is occupancy prediction, giving us information about people's mobility in the building. This task can be done via monitoring of CO2 as a reliable method, which has the ambition to predict the presence of the people in specific areas. In this paper, we propose a novel hybrid system, which is based on the Support Vector Machine (SVM) prediction of the CO2 waveform with the use of sensors that measure indoor/outdoor temperature and relative humidity. For each such prediction, we also record the gold standard CO2 signal to objectively compare and evaluate the quality of the proposed system. Unfortunately, this prediction is often linked with a presence of predicted signal activities in the form of glitches, often having an oscillating character, which inaccurately approximates the real CO2 signals. Thus, the difference between the gold standard and the prediction results from SVM is increasing. Therefore, we employed as the second part of the proposed system a smoothing procedure based on Wavelet transformation, which has ambitions to reduce inaccuracies in predicted signal via smoothing and increase the accuracy of the whole prediction system. The whole system is completed with an optimization procedure based on the Artificial Bee Colony (ABC) algorithm, which finally classifies the wavelet's response to recommend the most suitable wavelet settings to be used for data smoothing. (C) 2023 The Author(s)
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Heliyon
ISSN
2405-8440
e-ISSN
2405-8440
Volume of the periodical
9
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
21
Pages from-to
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UT code for WoS article
001040834100001
EID of the result in the Scopus database
2-s2.0-85159473892