반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

파라미터에 따른 인공지지체 불량 탐지 모델의 성능 비교

Performance Comparison of Scaffold Defect Detection Model by Parameters

  • 이송연 (한국기술교육대학교 대학원 메카트로닉스공학과) ;
  • 허용정 (한국기술교육대학교 메카트로닉스공학부)
  • Song Yeon Lee (Department of Mechatronics Engineering, Graduate School of Korea University of Technology and Education) ;
  • Yong Jeong Huh (School of Mechatronics Engineering, Korea University of Technology and Education)
  • 투고 : 2023.03.02
  • 심사 : 2023.03.22
  • 발행 : 2023.03.31
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초록

In this study, we compared the detection accuracy of the parameters of the scaffold failure detection model. A detection algorithm based on convolutional neural network was used to construct a failure detection model for scaffold. The parameter properties of the model were changed and the results were quantitatively verified. The detection accuracy of the model for each parameter was compared and the parameter with the highest accuracy was identified. We found that the activation function has a significant impact on the detection accuracy, which is 98% for softmax.

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참고문헌

  1. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Surface Defect Detection Model of 3D Printing Bone Plate Using Deep Learning Algorithm", J. of The Korean Society of Semiconductor & Display Technology, Vol.20, pp. 68-73, 2021
  2. Gee-Yeun Kim and Hyoung-Gook Kim, "Performanc Comparison of Lung Sound Classification Using Various Convolutional Neural Networks," J. of The Acoustical Society of Korea, Vol. 38, pp. 568-573, 2019
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  4. Chang-Hee Yang, Kyu-Sub Park, Young-Seop Kim and Yong-Hwan Lee, "Comparative Analysis for Emotion Expression Using Three Methods Based by CNN", J. of The Korean Society of Semiconductor & Display Technology, Vol.19, pp. 65-70, 2020
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  7. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Shape Warpage Defect Detection Medel of Scaffold Using Deep Learning Base CNN", J. of The Korean Society of Semiconductor & Display Technology, Vol.20, pp. 99-103, 2021