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
|
Pdf
Indoor occupancy estimation using carbon dioxide.pdf Available under License Creative Commons Attribution. Download (554kB) | Preview |
Abstract
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.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Uncontrolled Keywords: | Neural Network With Random Weights (NNRW); MH-Z19; Camera Position |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Institute of Postgraduate Studies College of Engineering Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Pn. Hazlinda Abd Rahman |
| Date Deposited: | 23 Dec 2020 02:53 |
| Last Modified: | 23 Dec 2020 02:53 |
| URI: | http://umpir.ump.edu.my/id/eprint/27768 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |