Statistical modelling of neural networks in γ-spectrometry
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Cited by (34)
Machine Learning technique for isotopic determination of radioisotopes using HPGe γ-ray spectra
2023, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated EquipmentArtificial neural network modeling in environmental radioactivity studies – A review
2022, Science of the Total EnvironmentCitation Excerpt :Due to their representativity and possibilities to solve mapping problems with a high number of input and output data, ANNs have been found to be useful for multivariate calibration in nuclear spectrometry. Vigneron et al. (1996) have been exploring their performances for spectrometric data analysis and estimation of parameters in the measurement of uranium enrichment. In this application, the region of interest for determining the total uranium activity is narrowed to the KαX energy range from 83 keV to 103 keV, which contains the main uranium lines and is narrow enough to assume constant efficiency.
Estimation of uranium concentration in ore samples with machine learning methods on HPGe gamma-ray spectra
2022, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated EquipmentSpectral unmixing applied to fast identification of γ-emitting radionuclides using NaI(Tl) detectors
2020, Applied Radiation and IsotopesCitation Excerpt :Combined to full-spectrum analysis, this property is important to take into account optimally the variability of the observed spectrum in decision-making at low-statistics. Contrary to classifiers such as artificial neural networks (Keller et al., 1995; Vigneron et al., 1996), the multiplicative update rule for spectral unmixing does not need a training phase based on a large dataset. The algorithm was first investigated in the case of the identification of radionuclides usually applied for RPM's testing (57Co, 60Co, 133Ba, 137Cs, 241Am) for the prevention of illegal nuclear material trafficking.
A comparison of machine learning methods for automated gamma-ray spectroscopy
2020, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated EquipmentCitation Excerpt :There have also been a number of published papers which apply NNs to automated isotope identification. NNs have been applied to peak fitting [3], isotope identification [4,5], and activity estimation [4,6,7]. Previous work applying NNs to spectroscopy have focused on fully-connected architectures.
Gamma spectral analysis by artificial neural network coupled with Monte Carlo simulations
2020, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated EquipmentCitation Excerpt :Common and attractive algorithms are Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled conjugate gradient (SCG) [6,8]. ANN has been used in a few NAA studies such as identification [10–14] and quantification [15,16] analysis of chemical elements using ANN, performed for the purposes of explosive detection [11,17,18], drug identification [17], radon contamination [19], uranium detection [20], gamma spectra unfolding [10], uncertainty estimation in gamma spectra [21], analysis of interaction types [22], anomaly detection in gamma spectra [23], and fast neutron spectral analysis [24]. The purpose of this study lies not only in element identification, but also quantification using ANN.