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...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/172912 |
| _version_ | 1826115762079662080 |
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| author | Wang, Xiaoni |
| author2 | Li King Ho, Holden |
| author_facet | Li King Ho, Holden Wang, Xiaoni |
| author_sort | Wang, Xiaoni |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:00:27Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/172912 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:00:27Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1729122024-01-06T16:50:15Z Unified fire sensing concept and time-series regression machine learning of environment-based features for quantifiable "no-fire" models Wang, Xiaoni Li King Ho, Holden School of Mechanical and Aerospace Engineering Tan Yan Hao HoldenLi@ntu.edu.sg Engineering::Mechanical engineering 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. Bachelor of Engineering (Mechanical Engineering) 2023-12-31T08:54:57Z 2023-12-31T08:54:57Z 2023 Final Year Project (FYP) Wang, X. (2023). Unified fire sensing concept and time-series regression machine learning of environment-based features for quantifiable "no-fire" models. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/172912 https://hdl.handle.net/10356/172912 en C168 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Mechanical engineering Wang, Xiaoni Unified fire sensing concept and time-series regression machine learning of environment-based features for quantifiable "no-fire" models |
| title | Unified fire sensing concept and time-series regression machine learning of environment-based features for quantifiable "no-fire" models |
| title_full | Unified fire sensing concept and time-series regression machine learning of environment-based features for quantifiable "no-fire" models |
| title_fullStr | Unified fire sensing concept and time-series regression machine learning of environment-based features for quantifiable "no-fire" models |
| title_full_unstemmed | Unified fire sensing concept and time-series regression machine learning of environment-based features for quantifiable "no-fire" models |
| title_short | Unified fire sensing concept and time-series regression machine learning of environment-based features for quantifiable "no-fire" models |
| title_sort | unified fire sensing concept and time series regression machine learning of environment based features for quantifiable no fire models |
| topic | Engineering::Mechanical engineering |
| url | https://hdl.handle.net/10356/172912 |
| work_keys_str_mv | AT wangxiaoni unifiedfiresensingconceptandtimeseriesregressionmachinelearningofenvironmentbasedfeaturesforquantifiablenofiremodels |