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홍성준(Seong-Jun Hong),방선배(Sun-Bea Bang),박진영(Jin-Yong Park),박광묵(Kawng-Muk Park) 한국조명·전기설비학회 2021 한국조명·전기설비학회 학술대회논문집 Vol.2021 No.11
The melting traces of copper wires are used as important clues for fire investigations. It is important to immediately determine whether the molten traces found at the fire site were melted by a flame or by an electrical cause. In this paper, we propose an AI-based melt-spot identification system as a method of judging molten traces at the fire site, and present the results learned by the AI algorithm.
전기화재 원인분석을 위한 실험실 데이터를 활용한 1차, 2차 단락흔 및 열흔 판별용 CNN 알고리즘 설계
조장훈(Jang-Hoon Jo),방준호(Junho Bang),유정훈(Jung-Hoon Yoo),선로빈(Robin Sun),홍성준(Seong-Jun Hong),방선배(Sun-Bea Bang) 대한전기학회 2021 전기학회논문지 Vol.70 No.11
In this paper, a new CNN algorithm is proposed to determine the direct cause of electric fires. We create 10,000-15,000 three types of data that can occur at a fire scene in our laboratory, and then train and verify it through the proposed CNN algorithm. As a result of the experiment and analysis, the classification accuracy of the primary and secondary arc beads was 86.2%, the accuracy of arc beads and molten marks was 93.6%. And also, the classification accuracy of the primary and secondary arc beads and molten marks was 92.4%. The results of this study are meaningful in that fire forensics can provide accurate identification results in a shorter time through artificial intelligence algorithms compared to the existing methods of identification through visual classification and physicochemical material analysis methods. In particular, the classification between primary and secondary arc beads is known to be a very difficult problem. However, the results of this study provided more than 86% classification ability.