稳健性(进化)
编码(内存)
计算机科学
卷积神经网络
人工神经网络
人工智能
模式识别(心理学)
信号(编程语言)
热成像
卷积(计算机科学)
循环神经网络
前馈神经网络
热红外
算法
红外线的
生物化学
化学
物理
光学
基因
程序设计语言
作者
Zheng Wang,Siyan Zhang,Ahmed M. Omer,Zhuoqiao Wu,Ning Tao,Cunlin Zhang,Dong‐Sheng Yang,Hai Zhang,Qiang Fang,Xavier Maldague,Jianqiao Meng,Yu-Xia Duan
标识
DOI:10.1080/10589759.2024.2304719
摘要
Pulsed thermography is a technique of significant interest in non-destructive testing, particularly in defect detection and depth characterisation of composite materials. This study presents an innovative methodology for simultaneously detecting defects and estimating depth using a combination of sequenced thermal signal encoding and a two-dimensional convolution neural network (CNN) model. We compare the results of the proposed method with those obtained from the feed-forward neural network (FFNN), a one-dimensional CNN, and a long short-term memory recurrent neural network (LSTM-RNN). The findings demonstrate that the proposed approach exhibits superior accuracy and robustness compared to the benchmarks.
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