Tips for Effective Machine Learning in NDT/E

Machine Learning Techniques for Acoustic Data Processing in Additive Manufacturing In Situ Process Monitoring: A Review

(2023) Hossein Taheri et al.   MATERIALS EVALUATION

Artificial Intelligence, Machine Learning and Smart Technologies for Nondestructive Evaluation

(2022) Hossein Taheri et al.   SENSORS

Guidelines for effective unsupervised guided wave compression and denoising in long-term guided wave structural health monitoring

(2022) Kang Yang et al.   STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL

What Is Probability of Detection?

(2022) Matthew Cherry et al.   MATERIALS EVALUATION

A Survey on Bias and Fairness in Machine Learning

(2021) Ninareh Mehrabi et al.   ACM COMPUTING SURVEYS

Using artificial neural network and non‐destructive test for crack detection in concrete surrounding the embedded steel reinforcement

(2021) Muhammad Saleem et al.   Structural Concrete

Achieving robust damage mode identification of adhesive composite joints for wind turbine blade using acoustic emission and machine learning

(2020) D. Xu et al.   COMPOSITE STRUCTURES

Reconciling modern machine-learning practice and the classical bias–variance trade-off

(2019) Mikhail Belkin et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Machine Learning for Fluid Mechanics

(2019) Steven L. Brunton et al.   Annual Review of Fluid Mechanics

Techniques for interpretable machine learning

(2019) Mengnan Du et al.   COMMUNICATIONS OF THE ACM

Points of Significance: Principal component analysis

(2017) Jake Lever et al.   NATURE METHODS

Robust ultrasonic damage detection under complex environmental conditions using singular value decomposition

(2015) Chang Liu et al.   ULTRASONICS

Challenges and obstacles in the application of acoustic emission to process machinery

(2008) J Z Sikorska et al.   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING