dc.contributor | 資管博二 | |
dc.creator (作者) | 李博逸 | |
dc.creator (作者) | Li, Bo-Yi* | |
dc.creator (作者) | Wang, Chen-Shu | |
dc.creator (作者) | Yang, Chao-Wei | |
dc.creator (作者) | Lin, Wei-Chieh | |
dc.creator (作者) | Hung, Shang-Chih | |
dc.creator (作者) | Chiang, Song-Bor | |
dc.date (日期) | 2019-02 | |
dc.date.accessioned | 30-Oct-2019 11:13:56 (UTC+8) | - |
dc.date.available | 30-Oct-2019 11:13:56 (UTC+8) | - |
dc.date.issued (上傳時間) | 30-Oct-2019 11:13:56 (UTC+8) | - |
dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/127178 | - |
dc.description.abstract (摘要) | In this research, we aim to solve the luminaire allocation and configuration problem. Additionally, considering the difficult of movement or adjustment of fixed luminaire, the luminaire failure issue is also taken consideration in the proposed model. To verify the proposed lighting configuration based on the GA, the athletic fields is taken as an example. According to two experiments results, compare to the lighting simulation software DIALux, in scenes with average illuminance of 750 lux and 350 lux, the error rate was less than 5%. In scenes with average illuminance of 150, the error rate was relatively high. The disparity value of illuminance between each point was between 0.9 lux and 37.4 lux, and the average disparity value of illuminance was 16.6 lux. In the two experiments, it is notable that the lighting configuration proposed based on the GA in this research has extraordinary effects. The traditional method based on experience can be replaced. The result can be verified by the DIALux software recognized in the industry, proving the feasibility of the model. | |
dc.format.extent | 108 bytes | - |
dc.format.mimetype | text/html | - |
dc.relation (關聯) | 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), The Korean Institute of Information Scientists and Engineers | |
dc.title (題名) | Optimized Luminaire Allocation and Configuration with Luminaire Failure Compensation | |
dc.type (資料類型) | conference | |
dc.identifier.doi (DOI) | 10.1109/BIGCOMP.2019.8679262 | |
dc.doi.uri (DOI) | https://doi.org/10.1109/BIGCOMP.2019.8679262 | |