TY - GEN
T1 - THz Signal Identification for Intelligent Characterization Under High-Resolution Mode based on RFECNet
AU - Wang, Xingyu
AU - Xu, Yafei
AU - Wang, Rong
AU - Zhang, Liuyang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Artificial intelligence (AI) technology has shown great potential in the automatic and intelligent identification of internal defects in composites based on terahertz (THz) spectroscopy. Based on the powerful feature extraction capability of deep learning, the proposed deep learning framework-based three-dimensional intelligent characterization system is proposed to detect the glass fiber reinforced polymer (GFRP) debonding defects in terahertz nondestructive testing (THz NDT), in which the defect datasets are firstly established by the THz time domain spectroscopy (THz- TDS), and then the robust feature extraction capability network (RFECN et) is adopted to realize the automatic and intelligent defect location and imaging by accurately classifying different THz signals. A series of experiments have been performed to validate the effectiveness of proposed system, which will provide a new solution for intelligent and automatic THz characterization of internal debonding defects of composites.
AB - Artificial intelligence (AI) technology has shown great potential in the automatic and intelligent identification of internal defects in composites based on terahertz (THz) spectroscopy. Based on the powerful feature extraction capability of deep learning, the proposed deep learning framework-based three-dimensional intelligent characterization system is proposed to detect the glass fiber reinforced polymer (GFRP) debonding defects in terahertz nondestructive testing (THz NDT), in which the defect datasets are firstly established by the THz time domain spectroscopy (THz- TDS), and then the robust feature extraction capability network (RFECN et) is adopted to realize the automatic and intelligent defect location and imaging by accurately classifying different THz signals. A series of experiments have been performed to validate the effectiveness of proposed system, which will provide a new solution for intelligent and automatic THz characterization of internal debonding defects of composites.
KW - Debonding defects
KW - THz characterization
KW - THz nondestructive testing
KW - the robust feature extraction capability network (RFECNet)
UR - https://www.scopus.com/pages/publications/85191492542
U2 - 10.1109/ICSMD60522.2023.10490488
DO - 10.1109/ICSMD60522.2023.10490488
M3 - 会议稿件
AN - SCOPUS:85191492542
T3 - ICSMD 2023 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, Proceedings
BT - ICSMD 2023 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023
Y2 - 2 November 2023 through 4 November 2023
ER -