• Title/Summary/Keyword: Fire prevention technology

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A Study on Fire Risk Assessment of the Temple Using Fire Loads (화재하중을 통한 사찰의 화재 위험성 평가에 관한 연구)

  • Kim, Su-Young;Shin, Young-Ju;Park, Young-Ju;Lee, Hae-Pyeong
    • Proceedings of the Korea Institute of Fire Science and Engineering Conference
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    • 2008.11a
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    • pp.409-415
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    • 2008

  • In this study, we considered the fire risk assessment of the temple using fire loads and the classification of combustibles. The building construction materials were classified as walls, beam-columns, floorings, ceiling panels and the loading combustibles were classified into fixed materials and movable materials. As a result, we confirmed that the total fire load of the Palsangjeon was $368\;kg/m^2$. The building construction materials accounted for approximately 93.8 percent of the total fire load and the loading combustibles accounted for approximately 6.2 percent.

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Efficient Forest Fire Detection using Rule-Based Multi-color Space and Correlation Coefficient for Application in Unmanned Aerial Vehicles

  • Anh, Nguyen Duc;Van Thanh, Pham;Lap, Doan Tu;Khai, Nguyen Tuan;Van An, Tran;Tan, Tran Duc;An, Nguyen Huu;Dinh, Dang Nhu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.2
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    • pp.381-404
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    • 2022

  • Forest fires inflict great losses of human lives and serious damages to ecological systems. Hence, numerous fire detection methods have been proposed, one of which is fire detection based on sensors. However, these methods reveal several limitations when applied in large spaces like forests such as high cost, high level of false alarm, limited battery capacity, and other problems. In this research, we propose a novel forest fire detection method based on image processing and correlation coefficient. Firstly, two fire detection conditions are applied in RGB color space to distinguish between fire pixels and the background. Secondly, the image is converted from RGB to YCbCr color space with two fire detection conditions being applied in this color space. Finally, the correlation coefficient is used to distinguish between fires and objects with fire-like colors. Our proposed algorithm is tested and evaluated on eleven fire and non-fire videos collected from the internet and achieves up to 95.87% and 97.89% of F-score and accuracy respectively in performance evaluation.

  • https://doi.org/10.3837/tiis.2022.02.002 인용 PDF KSCI HTML