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...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Wang, Xiaoni
مؤلفون آخرون: Li King Ho, Holden
التنسيق: Final Year Project
اللغة:English
منشور في: Nanyang Technological University 2023
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/172912
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الوصف
الملخص: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.