https://orcid.org/0000-0002-3792-5678
References
- T. AKYUREK, L. TUCKER, and S. USMAN, “Review and Characterization of Best Candidate Isotopes for Burnup Analysis and Monitoring of Irradiated Fuel,” Ann. Nucl. Energy, 69, 278 (2014); https://doi.org/10.1016/j.anucene.2013.12.014.
- M. TARVAINEN et al., “NDA Techniques for Spent Fuel Verification and Radiation Monitoring. Report on Activities 6a and 6b of Task JNT C799 (SAGOR). Finnish Support Programme to the IAEA Safeguards,” Radiation and Nuclear Safety Authority (STUK) (1997).
- C. WILLMAN et al., “Nondestructive Assay of Spent Nuclear Fuel with Gamma-Ray Spectroscopy,” Ann. Nucl. Energy, 33, 5, 427 (2006); https://doi.org/10.1016/j.anucene.2005.12.005.
- J. LEPPÄNEN et al., “The Serpent Monte Carlo Code: Status, Development and Applications in 2013,” Ann. Nucl. Energy, 82, 142 (2015); https://doi.org/10.1016/j.anucene.2014.08.024.
- S. M. HORNE et al., “GADRAS Version 18 User’s Manual,” Sandia National Laboratories (2019).
- S. SHARMA et al., “Anomaly Detection in Gamma Ray Spectra: A Machine Learning Perspective,” Proc. 2012 IEEE Symp. Computational Intelligence for Security and Defence Applications, IEEE (2012).
- S. GALIB et al., “A Comparative Study of Machine Learning Methods for Automated Identification of Radioisotopes Using NaI Gamma-Ray Spectra,” Nucl. Eng. Technol., 53, 12, 4072 (2021); https://doi.org/10.1016/j.net.2021.06.020.
- M. KAMUDA, J. ZHAO, and K. HUFF, “A Comparison of Machine Learning Methods for Automated Gamma-Ray Spectroscopy,” Nucl. Instrum. Methods Phys. Res., Sect. A, 954, 161385 (2020); https://doi.org/10.1016/j.nima.2018.10.063.
- D. CHERSOLA, G. LOMONACO, and G. MOROTTA, “Comparison Between SERPENT and MONTEBURNS Codes Applied to Burnup Calculations of GFR-Like Configuration,” Nucl. Eng. Des., 273, 542 (2014); https://doi.org/10.1016/j.nucengdes.2014.03.035.
- M. W. RAWOOL-SULLIVAN et al., “Combined MCNP/GADRAS Simulation of HPGe Gamma Spectra,” Proc. 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), p. 944, IEEE (2012).
- F. REITSMA et al., “The PBMR Steady-State and Coupled Kinetics Core Thermal-Hydraulics Benchmark Test Problems,” Nucl. Eng. Des., 236, 5, 657 (2006); https://doi.org/10.1016/j.nucengdes.2005.12.007.
- D. W. RUCK, S. K. ROGERS, and M. KABRISKY, “Feature Selection Using a Multilayer Perceptron,” J. Neural Network Comput., 2, 2, 40 (1990).
- A. VASWANI et al., “Attention Is All You Need,” Adv. Neural Inf. Process Syst., 30 (2017).
- O. DIM et al., “Verification of TRISO Fuel Burnup Using Machine Learning Algorithms,” Proc. INMM & ESARDA Joint Annual Mtg., Virtual Meeting, 2021, Institute of Nuclear Materials Management/European Safeguards Research and Development Association (2021).