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Hydra: Computer Vision for Data Quality Monitoring
Hydra is a system which utilizes computer vision to perform near real time data quality management, initially developed for Hall-D in 2019.
Evaluating DTW Measures via a Synthesis Framework for Time-Series Data
To address that, we propose a synthesis framework to model the variation between two time-series data sequences for comparison.
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators
To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability of particle accelerators.
Uncertainty Aware Deep Learning for Particle Accelerators
Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics.
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems.
Multi-module based CVAE to predict HVCM faults in the SNS accelerator
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 Modulators (HVCMs).
Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator
In order to improve the day-to-dayoperations and maximize the delivery of the science, new analytical techniques are being exploredfor anomaly detection, classification, and prognostications.
