Unified fire sensing concept and time-series regression machine learning of environment-based features for quantifiable "no-fire" models
This paper presents an in-depth exploration into fire sensing methodologies, with a specific focus on the development of a robust "No-Fire Detection Model." Various machine learning models were employed to construct a reliable predictive framework. The study emphasizes the identification a...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2023
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/172912 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | This paper presents an in-depth exploration into fire sensing methodologies, with a specific focus on the development of a robust "No-Fire Detection Model." Various machine learning models were employed to construct a reliable predictive framework. The study emphasizes the identification and quantification of stable environmental conditions indicative of the absence of fire incidents. By leveraging time-series regression techniques and environment-based features, the proposed "Unified Fire Sensing Concept" effectively delineates boundaries for recognizing deviations in environmental parameters. This research seeks to advance fire detection systems by providing a comprehensive understanding of "No-Fire" modeling, offering insights into adaptable methodologies for enhanced safety and reduced false alarms. |
|---|