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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.11
- 수록면
- 43 - 49 (7page)
이용수
초록· 키워드
오류제보하기
In this paper, we propose a displacement measurement method based on deep learning using image data obtained from tensile tests of a material specimen. We focus on the fact that the sequential images during the tension are generated and the displacement of the specimen is represented in the image data. So, we designed sample generation model which makes sequential images of specimen. The behavior of generated images are similar to the real specimen images under tensile force. Using generated images, we trained and validated our model. In the deep neural network, sequential images are assigned to a multi-channel input to train the network. The multi-channel images are composed of sequential images obtained along the time domain. As a result, the neural network learns the temporal information as the images express the correlation with each other along the time domain. In order to verify the proposed method, we conducted experiments by comparing the deformation measuring performance of the neural network changing the displacement range of images.
목차
Abstract
I. Introduction
II. Related works
III. The Proposed Scheme
IV. Experiment and Results
V. Conclusions
REFERENCES
참고문헌 (13)
참고문헌 신청
[학술지(정기간행물)] - Feipeng Zhu / 2015 / Measurement of true stress–strain curves and evolution of plastic zone of low carbon steel under uniaxial tension using digital image correlation / Optics and Lasers in Engineering 65 : 81 ~ 88
[학술지(정기간행물)] - 민현규 / 2017 / New Non-Contact Measurement Method of Deformation at Tensile Test of Thin Film via Digital Image Correlation Technique / International Journal of Precision Engineering and Manufacturing 18 (11) : 1509 ~ 1517
[학술지(정기간행물)] - Y. Lecun / 1998 / Gradient-based learning applied to document recognition / Proc. of the IEEE 86 : 2278 ~ 2324
[학술지(정기간행물)] - S. Hochreiter / 1997 / Long short-term memory / Neural Computation 9 (8) : 1735 ~ 1780
[학술대회논문] - Xingjian Shi / 2015 / Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting / Computer Vision and Pattern Recognition, 2015. Proceedings CVPR '15, Computer Society Conference on
[학술지(정기간행물)] - 민현규 / 2017 / New Non-Contact Measurement Method of Deformation at Tensile Test of Thin Film via Digital Image Correlation Technique / International Journal of Precision Engineering and Manufacturing 18 (11) : 1509 ~ 1517
[학술지(정기간행물)] - Y. Lecun / 1998 / Gradient-based learning applied to document recognition / Proc. of the IEEE 86 : 2278 ~ 2324
[학술지(정기간행물)] - S. Hochreiter / 1997 / Long short-term memory / Neural Computation 9 (8) : 1735 ~ 1780
[학술대회논문] - Xingjian Shi / 2015 / Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting / Computer Vision and Pattern Recognition, 2015. Proceedings CVPR '15, Computer Society Conference on
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UCI(KEPA) : I410-ECN-0101-2019-004-000226470