KJCKorea
Journal Central

@article{ART002406737},
author={Choi Hyeon-Joon and Kang, Dong-Joong},
title={Tensile Properties Estimation Method Using Convolutional LSTM Model},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2018},
volume={23},
number={11},
pages={43-49},
doi={10.9708/jksci.2018.23.11.043}

TY - JOUR
AU - Choi Hyeon-Joon
AU - Kang, Dong-Joong
TI - Tensile Properties Estimation Method Using Convolutional LSTM Model
JO - Journal of The Korea Society of Computer and Information
PY - 2018
VL - 23
IS - 11
PB - The Korean Society Of Computer And Information
SP - 43
EP - 49
SN - 1598-849X
AB - 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.
KW - Deep learning;Convolutional LSTM;Displacement measurement;Tensile test and Image processing;Sequential images;Regression model
DO - 10.9708/jksci.2018.23.11.043
ER -

Choi Hyeon-Joon and Kang, Dong-Joong. (2018). Tensile Properties Estimation Method Using Convolutional LSTM Model. Journal of The Korea Society of Computer and Information, 23(11), 43-49.

Choi Hyeon-Joon and Kang, Dong-Joong. 2018, "Tensile Properties Estimation Method Using Convolutional LSTM Model", Journal of The Korea Society of Computer and Information, vol.23, no.11 pp.43-49. Available from: doi:10.9708/jksci.2018.23.11.043

Choi Hyeon-Joon, Kang, Dong-Joong "Tensile Properties Estimation Method Using Convolutional LSTM Model" Journal of The Korea Society of Computer and Information 23.11 pp.43-49 (2018) : 43.

Choi Hyeon-Joon, Kang, Dong-Joong. Tensile Properties Estimation Method Using Convolutional LSTM Model. 2018; 23(11), 43-49. Available from: doi:10.9708/jksci.2018.23.11.043

Choi Hyeon-Joon and Kang, Dong-Joong. "Tensile Properties Estimation Method Using Convolutional LSTM Model" Journal of The Korea Society of Computer and Information 23, no.11 (2018) : 43-49.doi: 10.9708/jksci.2018.23.11.043

Choi Hyeon-Joon; Kang, Dong-Joong. Tensile Properties Estimation Method Using Convolutional LSTM Model. Journal of The Korea Society of Computer and Information, 23(11), 43-49. doi: 10.9708/jksci.2018.23.11.043

Choi Hyeon-Joon; Kang, Dong-Joong. Tensile Properties Estimation Method Using Convolutional LSTM Model. Journal of The Korea Society of Computer and Information. 2018; 23(11) 43-49. doi: 10.9708/jksci.2018.23.11.043

Choi Hyeon-Joon, Kang, Dong-Joong. Tensile Properties Estimation Method Using Convolutional LSTM Model. 2018; 23(11), 43-49. Available from: doi:10.9708/jksci.2018.23.11.043

Choi Hyeon-Joon and Kang, Dong-Joong. "Tensile Properties Estimation Method Using Convolutional LSTM Model" Journal of The Korea Society of Computer and Information 23, no.11 (2018) : 43-49.doi: 10.9708/jksci.2018.23.11.043