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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Online Access: | http://eprints.utm.my/92705/1/MohdIbrahimShapiai2020_IndoorOccupancyEstimationUsingCarbonDioxide.pdf |
| _version_ | 1796865476972773376 |
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| 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 |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T20:57:36Z |
| format | Conference or Workshop Item |
| id | utm.eprints-92705 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:57:36Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | utm.eprints-927052021-10-28T10:13:41Z http://eprints.utm.my/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/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 |
| 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 |
| title | 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_short | 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 |
| topic | T Technology (General) |
| url | http://eprints.utm.my/92705/1/MohdIbrahimShapiai2020_IndoorOccupancyEstimationUsingCarbonDioxide.pdf |
| work_keys_str_mv | AT ramlimuhammadfaris indooroccupancyestimationusingcarbondioxideconcentrationandneuralnetworkwithrandomweights AT muniandykishendran indooroccupancyestimationusingcarbondioxideconcentrationandneuralnetworkwithrandomweights AT adamasrul indooroccupancyestimationusingcarbondioxideconcentrationandneuralnetworkwithrandomweights AT abnasirahmadfakhri indooroccupancyestimationusingcarbondioxideconcentrationandneuralnetworkwithrandomweights AT shapiaimohdibrahim indooroccupancyestimationusingcarbondioxideconcentrationandneuralnetworkwithrandomweights |