Wavelet-Based Filtration Procedure for Denoising the Predicted CO<sub>2</sub> Waveforms in Smart Home within the Internet of Things

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building&#8217;s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residen...

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Main Authors: Jan Vanus, Klara Fiedorova, Jan Kubicek, Ojan Majidzadeh Gorjani, Martin Augustynek
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/3/620
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spelling doaj-56dfe2f2befa4b8087fe3c35e7c426a82020-11-25T02:16:38ZengMDPI AGSensors1424-82202020-01-0120362010.3390/s20030620s20030620Wavelet-Based Filtration Procedure for Denoising the Predicted CO<sub>2</sub> Waveforms in Smart Home within the Internet of ThingsJan Vanus0Klara Fiedorova1Jan Kubicek2Ojan Majidzadeh Gorjani3Martin Augustynek4Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, FEECS, 708 Ostrava-Poruba, Czech RepublicDepartment of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, FEECS, 708 Ostrava-Poruba, Czech RepublicDepartment of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, FEECS, 708 Ostrava-Poruba, Czech RepublicDepartment of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, FEECS, 708 Ostrava-Poruba, Czech RepublicDepartment of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, FEECS, 708 Ostrava-Poruba, Czech RepublicThe operating cost minimization of smart homes can be achieved with the optimization of the management of the building&#8217;s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO<sub>2</sub> concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO<sub>2</sub> concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO<sub>2</sub> concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.https://www.mdpi.com/1424-8220/20/3/620intelligent buildingswavelet transformationpredictionartificial neural networkmultilayer perceptroncloud computinginternet of thingssmart home
collection DOAJ
language English
format Article
sources DOAJ
author Jan Vanus
Klara Fiedorova
Jan Kubicek
Ojan Majidzadeh Gorjani
Martin Augustynek
spellingShingle Jan Vanus
Klara Fiedorova
Jan Kubicek
Ojan Majidzadeh Gorjani
Martin Augustynek
Wavelet-Based Filtration Procedure for Denoising the Predicted CO<sub>2</sub> Waveforms in Smart Home within the Internet of Things
Sensors
intelligent buildings
wavelet transformation
prediction
artificial neural network
multilayer perceptron
cloud computing
internet of things
smart home
author_facet Jan Vanus
Klara Fiedorova
Jan Kubicek
Ojan Majidzadeh Gorjani
Martin Augustynek
author_sort Jan Vanus
title Wavelet-Based Filtration Procedure for Denoising the Predicted CO<sub>2</sub> Waveforms in Smart Home within the Internet of Things
title_short Wavelet-Based Filtration Procedure for Denoising the Predicted CO<sub>2</sub> Waveforms in Smart Home within the Internet of Things
title_full Wavelet-Based Filtration Procedure for Denoising the Predicted CO<sub>2</sub> Waveforms in Smart Home within the Internet of Things
title_fullStr Wavelet-Based Filtration Procedure for Denoising the Predicted CO<sub>2</sub> Waveforms in Smart Home within the Internet of Things
title_full_unstemmed Wavelet-Based Filtration Procedure for Denoising the Predicted CO<sub>2</sub> Waveforms in Smart Home within the Internet of Things
title_sort wavelet-based filtration procedure for denoising the predicted co<sub>2</sub> waveforms in smart home within the internet of things
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-01-01
description The operating cost minimization of smart homes can be achieved with the optimization of the management of the building&#8217;s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO<sub>2</sub> concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO<sub>2</sub> concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO<sub>2</sub> concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.
topic intelligent buildings
wavelet transformation
prediction
artificial neural network
multilayer perceptron
cloud computing
internet of things
smart home
url https://www.mdpi.com/1424-8220/20/3/620
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