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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&apos;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&apos;s response to recommend the most suitable wavelet settings to be used for data smoothing. (C) 2023 The Author(s)

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20200 - Electrical engineering, Electronic engineering, Information engineering

Result continuities

  • Project

  • 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

  • UT code for WoS article

    001040834100001

  • EID of the result in the Scopus database

    2-s2.0-85159473892