2 citations · 2 across the 3 of their papers we have counts for
3 papers
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
Steven Goldenberg, Malachi Schram, Kishansingh Rajput +7
Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaus…
Multi-module based CVAE to predict HVCM faults in the SNS accelerator
Yasir Alanazi, Malachi Schram, Kishansingh Rajput +7
We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulat…
Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning
Majdi I. Radaideh, Chris Pappas, Mark Wezensky +2
Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its pow…