Transfer Learning in Automated Gamma Spectral Identification
Conference
·
OSTI ID:1724059
- Mission Support and Test Services, LLC, North Las Vega, NV (United States)
The models and weights of prior trained Convolutional Neural Networks (CNN) created to perform automated isotopic classification of time-sequenced gamma-ray spectra, were utilized to provide source domain knowledge as training on new domains of potential interest. The previous results were achieved solely using modeled spectral data. In this work we attempt to transfer the knowledge gained to the new, if similar, domain of solely measured data. The ability to train on modeled data and predict on measured data will be crucial in any successful data-driven approach to this problem space.
- Research Organization:
- Nevada National Security Site/Mission Support and Test Services LLC (NNSS/MSTS), North Las Vegas, NV (United States); Las Vegas, NV (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- DE-NA0003624
- OSTI ID:
- 1724059
- Report Number(s):
- DOE/NV/03624-0746; STIP WF - 19600407
- Resource Relation:
- Conference: SORMA West 2020 - 2020 IEEE SYMPOSIUM ON RADIATION MEASUREMENTS AND APPLICATIONS. University of California-Berkeley’s Clark Kerr Campus, 2601 Warring Street, Berkeley, CA 94720. CONFERENCE CANCELLED DUE TO COVID-19
- Country of Publication:
- United States
- Language:
- English
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