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: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
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IOP Publishing
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/27768/13/Indoor%20occupancy%20estimation%20using%20carbon%20dioxide.pdf http://umpir.ump.edu.my/id/eprint/27768/ https://doi.org/10.1088/1757-899X/769/1/012011 |
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| author | Muhammad Faris, Ramli Kishendran, Muniandy Asrul, Adam Ahmad Fakhri, Ab. Nasir Mohd Ibrahim, Shapiai |
| author_facet | Muhammad Faris, Ramli Kishendran, Muniandy Asrul, Adam Ahmad Fakhri, Ab. Nasir Mohd Ibrahim, Shapiai |
| author_sort | Muhammad Faris, Ramli |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.ump.umpir.27768 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2020 |
| publisher | IOP Publishing |
| record_format | eprints |
| spelling | my.ump.umpir.277682020-12-23T02:53:12Z http://umpir.ump.edu.my/id/eprint/27768/ Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights Muhammad Faris, Ramli Kishendran, Muniandy Asrul, Adam Ahmad Fakhri, Ab. Nasir Mohd Ibrahim, Shapiai QA76 Computer software 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. IOP Publishing 2020 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/27768/13/Indoor%20occupancy%20estimation%20using%20carbon%20dioxide.pdf Muhammad Faris, Ramli and Kishendran, Muniandy and Asrul, Adam and Ahmad Fakhri, Ab. Nasir and Mohd Ibrahim, Shapiai (2020) Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights. In: IOP Conference Series: Materials Science and Engineering, The 6th International Conference on Software Engineering & Computer Systems , 25-27 September 2019 , Pahang, Malaysia. pp. 1-9., 769 (012011). ISSN 1757-899X (Published) https://doi.org/10.1088/1757-899X/769/1/012011 |
| spellingShingle | QA76 Computer software Muhammad Faris, Ramli Kishendran, Muniandy Asrul, Adam Ahmad Fakhri, Ab. Nasir Mohd Ibrahim, Shapiai 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 | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/27768/13/Indoor%20occupancy%20estimation%20using%20carbon%20dioxide.pdf http://umpir.ump.edu.my/id/eprint/27768/ https://doi.org/10.1088/1757-899X/769/1/012011 |
| url_provider | http://umpir.ump.edu.my/ |