A supervised learning approach for prediction of x-ray computed tomography data from ultrasonic testing data
暂无分享,去创建一个
John C. Aldrin | Josiah Dierken | John T. Welter | John Wertz | Daniel Sparkman | Sarah Wallentine | David Zainey | J. Wertz | J. Aldrin | M. Uchic | J. Welter | D. Sparkman | J. Dierken | Mark Flores | Norm Schehl | Mike D. Uchic | N. Schehl | Sarah Wallentine | M. Flores | David Zainey
[1] Somnath Ghosh,et al. A framework for automated analysis and simulation of 3D polycrystalline microstructures. , 2008 .
[2] John C. Aldrin,et al. Fundamentals of angled-beam ultrasonic NDE for potential characterization of hidden regions of impact damage in composites , 2018 .
[3] John C. Aldrin,et al. Gaussian process regression of chirplet decomposed ultrasonic B-scans of a simulated design case , 2018 .
[4] G. S. Watson,et al. Smooth regression analysis , 1964 .
[5] E. Nadaraya. On Estimating Regression , 1964 .
[6] John C. Aldrin,et al. Volumetric characterization of delamination fields via angle longitudinal wave ultrasound , 2017 .
[7] Mark David Flores,et al. Damage tolerance and assessment of unidirectional carbon fiber composites: An experimental and numerical study , 2016 .