Q
Qiang Wang
Publications - 6
Citations - 41
Qiang Wang is an academic researcher. The author has contributed to research in topics: Terahertz radiation & Recurrent neural network. The author has an hindex of 2, co-authored 6 publications receiving 10 citations.
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Journal ArticleDOI
Defect Depth Determination in Laser Infrared Thermography Based on LSTM-RNN
TL;DR: Results show that background noises in the original thermal signals can be effectively reduced by the TSR method, which is helpful for the models to learn the signal characteristics.
Journal ArticleDOI
Automatic defect prediction in glass fiber reinforced polymer based on THz-TDS signal analysis with neural networks
TL;DR: A novel approach to predict the defect depths in GFRP based on terahertz time-domain spectroscopy signal analysis with neural networks is reported, which shows that in general the one-dimension convolutional neural network model outperforms the long-short term memory recurrent neural network and the bidirectional LSTM-RNN models.
Journal ArticleDOI
Time Segmented Image Fusion Based Multi- Depth Defects Imaging Method in Composites With Pulsed Terahertz
TL;DR: The experimental results show that the proposed time-domain piecewise imaging method can effectively improve the terahertz imaging of deeper defects in GFRP sample especially for multi-depth defects and possesses the highest SNR and defect edge contrast.
Journal ArticleDOI
Pixel-based thermal sequence processing algorithm based on R2 fractile threshold of non-linear fitting in active infrared thermography
TL;DR: In this paper, a pixel-based thermal sequences processing based on R2 (coefficient of determination, COD) fractile threshold of non-linear fitting is proposed for defect detection in active laser infrared thermography inspection to an aviation CFRP laminate with artificial defects.
Proceedings ArticleDOI
Super-Resolution Imaging Using Very Deep Convolutional Network in Terahertz NDT Field
TL;DR: Wang et al. as discussed by the authors proposed a Terahertz image super-resolution (SR) method based on very deep Convolutional Networks (VDCN) in non-destructive testing (NDT) application.