Open Access
Issue |
Int. J. Metrol. Qual. Eng.
Volume 16, 2025
|
|
---|---|---|
Article Number | 1 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/ijmqe/2024020 | |
Published online | 07 January 2025 |
- B. Sund, H. Jaldell, Security officers responding to residential fire alarms: estimating the effect on survival and property damage, Fire Saf. J. 97, 1–11 (2018) [CrossRef] [Google Scholar]
- S. Chen, J. Ren, Y. Yan, M. Sun, F. Hu, H. Zhao, Multi-sourced sensing and support vector machine classification for effective detection of fire hazard in early stage, Comput. Electr. Eng. 101, 108046 (2022) [CrossRef] [Google Scholar]
- S.L. Rose-Pehrsson, R.E. Shaffer, S.J. Hart, F.W. Williams, D.T. Gottuk, B.D. Strehlen, S.A. Hill, Multi-criteria fire detection systems using a probabilistic neural network, Sensors Actuators B: Chem. 69, 325–335 (2000) [CrossRef] [Google Scholar]
- I. Bakas, K.J. Kontoleon, Performance evaluation of artificial neural networks (Ann) predicting heat transfer through masonry walls exposed to fire, Appl. Sci. 11, 11435 (2021) [CrossRef] [Google Scholar]
- J. Fonollosa, A. Solórzano, S. Marco, Chemical sensor systems and associated algorithms for fire detection: a review, Sensors 18, 553 (2018) [CrossRef] [PubMed] [Google Scholar]
- D. Minoli, K. Sohraby, B. Occhiogrosso, IoT considerations, requirements, and architectures for smart buildings—energy optimization and next-generation building management systems, IEEE Internet Things J. 4, 269–283 (2017) [CrossRef] [Google Scholar]
- B. Sarwar, I.S. Bajwa, N. Jamil, S. Ramzan, N. Sarwar, An intelligent fire warning application using IoT and an adaptive neuro-fuzzy inference system, Sensors 19, 3150 (2019) [CrossRef] [PubMed] [Google Scholar]
- F.Z. Rachman, N. Yanti, H. Hadiyanto, S. Suhaedi, Q. Hidayati, M. Widagda, B.A. Saputra, Design of the early fire detection based fuzzy logic using multisensor, in: IOP Conference Series: Materials Science and Engineering, 2020, pp. 012039 [CrossRef] [Google Scholar]
- R. Sowah, K.O. Ampadu, A. Ofoli, K. Koumadi, G.A. Mills, J. Nortey, Design and implementation of a fire detection and control system for automobiles using fuzzy logic, in: 2016 IEEE industry applications society annual meeting, 2016, pp. 1–8 [Google Scholar]
- A.E. Çetin, K. Dimitropoulos, B. Gouverneur, N. Grammalidis, O. Günay, Y.H. Habiboǧlu, B.U. Töreyin, S. Verstockt, Video fire detection-review, Digit. Signal Process. 23, 1827–1843 (2013) [CrossRef] [Google Scholar]
- N. Du, Y. Wang, Z.L. Liu, Design of building fire detection system based on ZigBee, Appl. Mech. Mater. 427, 1432–1435 (2013) [CrossRef] [Google Scholar]
- S. Gangopadhyay, M.K. Mondal, A wireless framework for environmental monitoring and instant response alert, in: 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), 2016, pp. 1–6 [Google Scholar]
- J. Zhang, C. Chen, J. Peng, J. Liang, Early warning method and system of building environmental security based on TinyML and CloudML technology, in: 2nd International Conference on Information Technology and Intelligent Control (CITIC 2022), 2022, pp. 122–128 [Google Scholar]
- X. Zhou, H. Li, J. Wang, J. Zhao, Q. Xie, L. Li, J. Liu, J. Yu, CloudFAS: cloud-based building fire alarm system using Building Information Modelling, J. Build. Eng. 53, 104571 (2022) [CrossRef] [Google Scholar]
- L. Salhi, T. Silverston, T. Yamazaki, T. Miyoshi, Early detection system for gas leakage and fire in smart home using machine learning, in: 2019 IEEE International Conference on Consumer Electronics (ICCE), 2019, pp. 1–6 [Google Scholar]
- I. Bakas, K.J. Kontoleon, Performance evaluation of artificial neural networks (Ann) predicting heat transfer through masonry walls exposed to fire, Appl. Sci. 11, 11435 (2021) [CrossRef] [Google Scholar]
- S. Chen, J. Ren, Y. Yan, M. Sun, F. Hu, H. Zhao, Multi-sourced sensing and support vector machine classification for effective detection of fire hazard in early stage, Comput. Electr. Eng. 101, 108046 (2022) [CrossRef] [Google Scholar]
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