• Title/Summary/Keyword: 이화원

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A system design for textile defect detection using pattern matching (패턴매칭을 이용한 섬유결함 검출시스템의 설계)

  • Kang, Hyunsoo;Kim, Jongjun;Song, Nagun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2010.11a
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    • pp.474-477
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    • 2010

  • 본 논문에서는 패턴인식을 이용한 의류의 결함을 자동으로 탐색하는 시스템을 설계하였다. 이는 히스토그램을 기반으로 하여 영상의 특징을 추출하고 템플릿 매칭을 이용해서 패턴을 추적하도록 하였스며, 또한, SSIM(Structural Similarity) Index를 통해 추적된 패턴과 원 패턴의 유사도를 HVS(Human Vision System)을 기준으로 하여 결함을 판별할수 있도록 하였다.

  • https://doi.org/10.3745/PKIPS.y2010m11a.474 인용 PDF

A Suggestion of the Direction of Construction Disaster Document Management through Text Data Classification Model based on Deep Learning (딥러닝 기반 분류 모델의 성능 분석을 통한 건설 재해사례 텍스트 데이터의 효율적 관리방향 제안)

  • Kim, Hayoung;Jang, YeEun;Kang, HyunBin;Son, JeongWook;Yi, June-Seong
    • Korean Journal of Construction Engineering and Management
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    • v.22 no.5
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    • pp.73-85
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    • 2021

  • This study proposes an efficient management direction for Korean construction accident cases through a deep learning-based text data classification model. A deep learning model was developed, which categorizes five categories of construction accidents: fall, electric shock, flying object, collapse, and narrowness, which are representative accident types of KOSHA. After initial model tests, the classification accuracy of fall disasters was relatively high, while other types were classified as fall disasters. Through these results, it was analyzed that 1) specific accident-causing behavior, 2) similar sentence structure, and 3) complex accidents corresponding to multiple types affect the results. Two accuracy improvement experiments were then conducted: 1) reclassification, 2) elimination. As a result, the classification performance improved with 185.7% when eliminating complex accidents. Through this, the multicollinearity of complex accidents, including the contents of multiple accident types, was resolved. In conclusion, this study suggests the necessity to independently manage complex accidents while preparing a system to describe the situation of future accidents in detail.

  • https://doi.org/10.6106/KJCEM.2021.22.5.073 인용 PDF KSCI