Engineering topics

Kamuda, Mark

Publications and source records attributed to Kamuda, Mark.

3 records

DOE OSTI2018

A comparison of machine learning methods for automated gamma-ray spectroscopy

Pattern recognition algorithms such as artificial neural networks (NNs) and convolution neural networks (CNNs) are prime candidates to perform automated gamma-ray spectroscopy. The way these models train and operate mimic how trained spectroscopists identify spectra. These models have shown promise in identifying gamma-ray spectra with large calibration drift and unknown background radiation fields. In this work, two algorithms for mixtures of radioisotopes based on NN and CNN are presented and evaluated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

An automated isotope identification and quantification algorithm for isotope mixtures in low-resolution gamma-ray spectra

There is a need to develop an automated isotope identification and quantification algorithm that can perform well using low-resolution gamma-ray detectors. The algorithm should be able to perform well on spectra that contain a mixture of many isotopes as well as in cases where spectral features are difficult to analyze. Due to the low resolution of these detectors, spectra of isotope mixtures becomes complicated to identify when features overlap. Previous research applying machine learning algorithms to isotope identification has been promising. Further work is needed to demonstrate the limits of machine learning algorithms when applied to identifying mixtures of isotopes and spectra where features are not obvious. Because machine learning algorithms use abstract features of the spectrum, such as the shape of overlapping peaks and Compton continuum, they are a natural choice for analyzing isotope mixtures. In this work, an artificial neural network (ANN) has be trained to calculate the relative activities of 29 isotopes in a spectrum. The ANN is trained with simulated gamma-ray spectra, allowing custom datasets to be generated for specific identification tasks. The algorithms performance on simulated spectra without apparent features and on simulated isotope mixtures are both analyzed.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2017

Automated isotope identification algorithm using artificial neural networks

There is a need to develop an algorithm that can determine the relative activities of radio-isotopes in a large dataset of low-resolution gamma-ray spectra that contains a mixture of many radio-isotopes. Low-resolution gamma-ray spectra that contain mixtures of radio-isotopes often exhibit feature over-lap, requiring algorithms that can analyze these features when overlap occurs. While machine learning and pattern recognition algorithms have shown promise for the problem of radio-isotope identification, their ability to identify and quantify mixtures of radio-isotopes has not been studied. Because machine learning algorithms use abstract features of the spectrum, such as the shape of overlapping peaks and Compton continuum, they are a natural choice for analyzing radio-isotope mixtures. An artificial neural network (ANN) has be trained to calculate the relative activities of 32 radio-isotopes in a spectrum. Furthermore, the ANN is trained with simulated gamma-ray spectra, allowing easy expansion of the library of target radio-isotopes. In this paper we present our initial algorithms based on an ANN and evaluate them against a series measured and simulated spectra.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗