Notice of Removal: Performance analysis of deep learning architectures for ultrasonic NDE applications

Abstract

Research on Deep Learning algorithms has progressed rapidly in recent years. Since the inception of deep learning, numerous architectures have been proposed for various applications targeting pattern recognition, image, audio and information analysis. For example, often audio signal classifications use variations of Deep Belief Networks (DBN), while a Deep Neural Network (DNN) called AlexNet is widely used for handwriting and alphabet recognition. Convolutional Neural Network (CNN) and its derivatives are primarily used in machine vision and imaging applications. Convolutional Deep Belief Networks (CDBN) work as a combination of CNN and DBN architectures and can be applied to image, audio and multimodal data. There has been limited studies on the effectiveness of these architectures for ultrasonic NDE applications. Therefore, this work investigates new flaw detection methods based on recent advances on deep learning architectures.