Applying Machine Learning to the Classification of DC-DC Converters (Milestone 2 Deliverable Report)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Since extending the Autodetector to a convolutional neural network (CNN) machine learning classifier model, an effort has been executed to demonstrate its ability to distinguish not only a switching DC-DC converter as high voltage, but identify the make and model of a converter on which it was trained. This was achieved by collecting data in a noisy environment, pre-processing the time domain data to obtain composite images using a method that improves upon that of the prior research, then validating a trained CNN model to an accuracy of 100% on a selected candidate converter.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1670255
- Report Number(s):
- SAND--2020-10478R; 691038
- Country of Publication:
- United States
- Language:
- English
Similar Records
Applying Machine Learning to the Classification of DC-DC Converters. NA-22 Final Report
Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem
A comparison of machine learning methods for automated gamma-ray spectroscopy
Technical Report
·
Mon Nov 30 23:00:00 EST 2020
·
OSTI ID:1735789
Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem
Journal Article
·
Thu May 19 00:00:00 EDT 2022
· Annals of Nuclear Energy
·
OSTI ID:1976818
A comparison of machine learning methods for automated gamma-ray spectroscopy
Journal Article
·
Mon Oct 01 00:00:00 EDT 2018
· Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
·
OSTI ID:1524430