Open Access
Issue
Int. J. Metrol. Qual. Eng.
Volume 13, 2022
Article Number 1
Number of page(s) 8
DOI https://doi.org/10.1051/ijmqe/2022001
Published online 19 January 2022
  1. V. Hristidis, S.C. Chen, T. Li, S. Luis, Y. Deng, Survey of data management and analysis in disaster situations, J. Syst. Softw. 83, 1701–1714 (2010) [CrossRef] [Google Scholar]
  2. Z. Bi, T. Le, H. Pan, H. Yang, Y. Jiang, Overview on the pre-processing method of image fire detection, Fire. Sci. Technol. 35, 87–91 (2016) [Google Scholar]
  3. N.D. Hansen, F. Steffensen, M. Valkvist, G. Jomaas, R. Van Coile, A fire risk assessment model for residential high-rises with a single stairwell, Fire. Saf. J. 95, 160–169 (2018) [CrossRef] [Google Scholar]
  4. R. Bogue, Sensors for fire detection, Sensor. Rev. 33, 99–103 (2013) [CrossRef] [Google Scholar]
  5. S. Duan, D. Mao, Research on fire flame image detection algorithm, Comput. Simulat. 33, 393–398 (2016) [Google Scholar]
  6. M. Lin, W. Chen, B. Liu, An intelligent fire-detection method based on image processing, Adv. Eng. Forum. 2, 172–175 (2011) [CrossRef] [Google Scholar]
  7. A.K. Muhammad, J. Ahmad, W. Sung, Early fire detection using convolutional neural networks during surveillance for effective disaster management, Neurocomputing 288, 30–42 (2018) [CrossRef] [Google Scholar]
  8. P. Foggia, A. Saggese, M. Vento, Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion, IEEE Trans. Circuits Syst. Video Technol. 25, 1545–1556 (2015) [CrossRef] [Google Scholar]
  9. Y. Mao, H. Wang, Y. Lu, L. Qin, Precise localization method for fire sources in large-space buildings, Comput. Appl. Softw. 33, 169–172 (2016) [Google Scholar]
  10. D. Li, Q. Liu, Y. He, H. Wang, L. Hu, Research on data fusion algorithm of fire detection signal in confined space, Fire. Sci. Technol. 40, 164–168 (2021) [Google Scholar]
  11. Y. He, Y. Dong, J. Lei, W. Zhang, R. Xue, Research on multi-sensor method for detecting aircraft cargo fire, Chin. Saf. Sci. J. 28, 74–79 (2018) [Google Scholar]
  12. Y. Kong, C. Lang, S. Feng, T. Wang, M. Yin, Fire detection based on two-stream ordinal regression deep network consumed, Chin. Sci. Pap. 12, 1590–1595 (2017) [Google Scholar]
  13. J. Fonollosa, A. Solórzano, S. Marco, Chemical sensor systems and associated algorithms for fire detection: a review, Sensors-Basel. 18, 553–553 (2018) [CrossRef] [Google Scholar]
  14. A. Gaur, A. Singh, A. Kumar, Fire Sensing Technologies: A Review, IEEE Sensors. J. 19, 3191–3202 (2019) [CrossRef] [Google Scholar]
  15. S. Wang, M. Berentsen, T. Kaiser, Signal processing algo-rithms for fire localization using temperature sensor arrays, Fire. Saf. J. 40, 689–697 (2005) [CrossRef] [Google Scholar]
  16. L. Kou, X. Wang, X. Guo, J. Zhu, H. Zhang, Deep learning based inverse model for building fire source location and intensity estimation, Fire. Saf. J. 121, 1–10 (2021) [Google Scholar]
  17. M. Michaelides, C. Panayiotou, Plume source position estimation using sensor networks, Proceedings of the 13th Mediterranean Conference on Control and Automation Limassol, IEEE, 2005, pp. 731–736 [Google Scholar]
  18. Z. Li, P. Miao, Y. Liu, Fire detection and position algo-rithm for large room based on wireless sensor network, J. Data. Acquis. Process. 29, 964–969 (2014) [Google Scholar]
  19. G.S. Birajdar, M. Baz, R. Singh, M. Rashid, A. Gehlot, S.V. Akram, S.S. Alshamrani, A.S. AlGhamdi, Realization of people density and smoke flow in buildings during fire accidents using raspberry and openCV, Sustainability-Basel. 13, 11082–11082 (2021) [CrossRef] [Google Scholar]
  20. L. Wu, H. Lei, D. Huang, G. Chen, X. Lu, Y. Yang, Smoke detection algorithm based on Gaussian mixture motion detection model and multiple features, Autom. Inf. Eng. 35, 1–5 (2014) [Google Scholar]
  21. Z. Liu, Diffusion model for fire smoke based on Gaussian distribution, Fire. Sci. Technol. 33, 1205–1207 (2014) [Google Scholar]
  22. G. Susto, A. Cenedese, M. Terzi, Time-series classification methods: Review and applications to power systems data, Big. Data. Appl. Pow. Syst. 9, 179–220 (2018) [CrossRef] [Google Scholar]

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