Publications

Abstract not provided.

This report is intended to detail the findings of our investigation of the applicability of machine learning to the task of aftershock identification. The ability to automatically identify nuisance aftershock events to reduce analyst workload when searching for events of interest is an important step in improving nuclear monitoring capabilities and while waveform cross - correlation methods have proven successful, they have limitations (e.g., difficulties with spike artifacts, multiple aftershocks in the same window) that machine learning may be able to overcome. Here we apply a Paired Neural Network (PNN) to a dataset consisting of real, high quality signals added to real seismic noises in order to work with controlled, labeled data and establish a baseline of the PNN's capability to identify aftershocks. We compare to waveform cross - correlation and find that the PNN performs well, outperforming waveform cross - correlation when classifying similar waveform pairs, i.e., aftershocks.

Abstract not provided.

Automatic detection of defects in as-built parts is a challenging task due to the large number of potential manufacturing flaws that can occur. X-Ray computed tomography (CT) can produce high-quality images of the parts in a non-destructive manner. The images, however, are grayscale valued, often have artifacts and noise, and require expert interpretation to spot flaws. In order for anomaly detection to be reproducible and cost effective, an automated method is needed to find potential defects. Traditional supervised machine learning techniques fail in the high reliability parts regime due to large class imbalance: there are often many more examples of well-built parts than there are defective parts. This, coupled with the time expense of obtaining labeled data, motivates research into unsupervised techniques. In particular, we build upon the AnoGAN and f-AnoGAN work by T. Schlegl et al. and created a new architecture called PandaNet. PandaNet learns an encoding function to a latent space of defect-free components and a decoding function to reconstruct the original image. We restrict the training data to defect-free components so that the encode-decode operation cannot learn to reproduce defects well. The difference between the reconstruction and the original image highlights anomalies that can be used for defect detection. In our work with CT images, PandaNet successfully identifies cracks, voids, and high z inclusions. Beyond CT, we demonstrate PandaNet working successfully with little to no modifications on a variety of common 2-D defect datasets both in color and grayscale.

Abstract not provided.