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Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights

This study presents the indoor occupancy estimation using carbon dioxide concentration and neural network with random weights (NNRW). The utilization of carbon dioxide concentration is as an alternative to overcome the limitation of existing techniques, such as dependency to favourable lighting cond...

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Main Authors: Ramli, Muhammad Faris, Muniandy, Kishendran, Adam, Asrul, Ab. Nasir, Ahmad Fakhri, Shapiai, Mohd. Ibrahim
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/92705/1/MohdIbrahimShapiai2020_IndoorOccupancyEstimationUsingCarbonDioxide.pdf
http://eprints.utm.my/id/eprint/92705/
http://dx.doi.org/10.1088/1757-899X/769/1/012011
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spelling my.utm.927052021-10-28T10:13:41Z http://eprints.utm.my/id/eprint/92705/ Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights Ramli, Muhammad Faris Muniandy, Kishendran Adam, Asrul Ab. Nasir, Ahmad Fakhri Shapiai, Mohd. Ibrahim T Technology (General) This study presents the indoor occupancy estimation using carbon dioxide concentration and neural network with random weights (NNRW). The utilization of carbon dioxide concentration is as an alternative to overcome the limitation of existing techniques, such as dependency to favourable lighting condition and camera position. Whereas, NNRW provides a generalized and fast learning speed classification. In this study, MH-Z19 sensor is used to acquire carbon dioxide concentration and the NNRW is a multiclass estimation method. The numbers of the occupants are divided into three different classes, which are 15 occupants, 30 occupant and 50 occupant classes. Result indicates that the NNRW classifier has obtained training and testing accuracy, about 100 percent and 52 percent, respectively. 2020 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/92705/1/MohdIbrahimShapiai2020_IndoorOccupancyEstimationUsingCarbonDioxide.pdf Ramli, Muhammad Faris and Muniandy, Kishendran and Adam, Asrul and Ab. Nasir, Ahmad Fakhri and Shapiai, Mohd. Ibrahim (2020) Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights. In: 6th International Conference on Software Engineering and Computer Systems, ICSECS 2019, 25 - 27 September 2019, Kuantan, Pahang. http://dx.doi.org/10.1088/1757-899X/769/1/012011
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Ramli, Muhammad Faris
Muniandy, Kishendran
Adam, Asrul
Ab. Nasir, Ahmad Fakhri
Shapiai, Mohd. Ibrahim
Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights
description This study presents the indoor occupancy estimation using carbon dioxide concentration and neural network with random weights (NNRW). The utilization of carbon dioxide concentration is as an alternative to overcome the limitation of existing techniques, such as dependency to favourable lighting condition and camera position. Whereas, NNRW provides a generalized and fast learning speed classification. In this study, MH-Z19 sensor is used to acquire carbon dioxide concentration and the NNRW is a multiclass estimation method. The numbers of the occupants are divided into three different classes, which are 15 occupants, 30 occupant and 50 occupant classes. Result indicates that the NNRW classifier has obtained training and testing accuracy, about 100 percent and 52 percent, respectively.
format Conference or Workshop Item
author Ramli, Muhammad Faris
Muniandy, Kishendran
Adam, Asrul
Ab. Nasir, Ahmad Fakhri
Shapiai, Mohd. Ibrahim
author_facet Ramli, Muhammad Faris
Muniandy, Kishendran
Adam, Asrul
Ab. Nasir, Ahmad Fakhri
Shapiai, Mohd. Ibrahim
author_sort Ramli, Muhammad Faris
title Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights
title_short Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights
title_full Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights
title_fullStr Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights
title_full_unstemmed Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights
title_sort indoor occupancy estimation using carbon dioxide concentration and neural network with random weights
publishDate 2020
url http://eprints.utm.my/id/eprint/92705/1/MohdIbrahimShapiai2020_IndoorOccupancyEstimationUsingCarbonDioxide.pdf
http://eprints.utm.my/id/eprint/92705/
http://dx.doi.org/10.1088/1757-899X/769/1/012011
_version_ 1715189677717192704
score 13.211869

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