Journal ArticleDOI
Automatic defect prediction in glass fiber reinforced polymer based on THz-TDS signal analysis with neural networks
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TLDR
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.About:
This article is published in Infrared Physics & Technology.The article was published on 2021-06-01. It has received 11 citations till now. The article focuses on the topics: Recurrent neural network & Artificial neural network.read more
Citations
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Journal ArticleDOI
Machine learning-based defect characterization in anisotropic materials with IR-thermography synthetic data
Vahid Daghigh,Mohammad Naraghi +1 more
TL;DR: In this paper , the k-nearest neighbors (k-NN) machine learning algorithm is employed to provide a model for predicting a penny-shaped defect size, thickness, and location in composite laminates.
Journal ArticleDOI
Automatic detection of CFRP subsurface defects via thermal signals in long pulse and lock-in thermography
TL;DR: Wang et al. as discussed by the authors proposed a model to detect defects automatically by extracting the thermal signal characteristics of carbon fiber reinforced plastics (CFRP), which can detect defects with minimum aspect ratio (ratio of short side to depth).
Journal ArticleDOI
Near infrared spectroscopy quantification based on Bi-LSTM and transfer learning for new scenarios.
TL;DR: In this article , the authors proposed a deep transfer learning methodology based on an improved Bi-directional Long Short-Term Memory (Bi-LSTM) network for the first time to address the near infrared spectroscopy (NIR) model transfer issue between samples.
Journal ArticleDOI
Terahertz transfer characterization for composite delamination under variable conditions based on deep adversarial domain adaptation
Yafei Xu,Guanghui Lian,Hongkuan Zhou,Yushan Hou,Hao Zhang,Liuyang Zhang,Ruqiang Yan,Xuefeng Chen +7 more
TL;DR: Li et al. as discussed by the authors proposed an unsupervised CNN-DADA model to address the domain shift problem between different terahertz datasets by adversarial learning, which can fulfill the automatic localization and imaging of delamination defects with high accuracy and resolution even for the THz datasets with significant distribution discrepancies.
Journal ArticleDOI
Application of Deep Learning Techniques to Predict the Mechanical Strength of Al-Steel Explosive Clads
TL;DR: In this paper , the tensile and shear strengths of aluminum 6061-differently grooved stainless steel 304 explosive clads are predicted using deep learning algorithms, namely the conventional neural network (CNN), deep neural network(DNN), and recurrent neural networks (RNN).
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
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Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Journal ArticleDOI
A systematic analysis of performance measures for classification tasks
Marina Sokolova,Guy Lapalme +1 more
TL;DR: This paper presents a systematic analysis of twenty four performance measures used in the complete spectrum of Machine Learning classification tasks, i.e., binary, multi-class,multi-labelled, and hierarchical, to produce a measure invariance taxonomy with respect to all relevant label distribution changes in a classification problem.
Journal ArticleDOI
Fibre reinforced composites in aircraft construction
TL;DR: A review of recent advances using composites in modern aircraft construction is presented and it is argued that fibre reinforced polymers, especially carbon fibre reinforced plastics (CFRP), can and will in the future contribute more than 50% of the structural mass of an aircraft as discussed by the authors.