... employed to efficiently carry out the probability of detection study for reliability assessment and sensitivity analysis based on the propagation of uncertainties. Guided Waves based SHM Deep Learning Machine learning Digital Twin Global Sensitivity Analysis Uncertainty Propagation...

... wavefields of guided Lamb waves is very time-consuming. To tackle this problem, one possible solution is to acquire the guided Lamb waves in a low-resolution form and then apply a compressive sensing (CS) or a deep learning-based super-resolution approach to that low-resolution form of full wavefields data...

... composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods...

... between 11 and 16 dB (depending on the angular sparsity) ; showing that efficient CT inspection can be performed from only few dozens of images X-ray CT reconstruction sparse view sinogram interpolation reconstruction denoising deep learning convolutional neural networks Proceedings...

... Abstract Aiming at the problem of image quality reduction caused by sparse array in guided wave detection, an enhanced algorithm based on improved compressive sensing and deep learning is proposed in this paper so as to realize high-quality imaging with a small number of sensors...

... processing methods for NDT applications. In this study, the results of a comparative blind-test between Human VT analysts are also presented. Keywords: Crack Detection, Convolutional Neural Network, Deep Learning, Nuclear Power Plant, Reactor Pressure Vessel Inspection, Bottom Mounted Nozzles NOMENCLATURE...