Spectrometry analysis based on approximation coefficients and deep belief networks

Particle may sometimes have energy outside the range of radiation detection hardware so that the signal is saturated and useful information is lost. We have therefore investigated the possibility of using an Artificial Neural Network (ANN) to restore the saturated waveforms of γ signals. Several ANNs were tested, namely the Back Propagation (BP), Simple Recurrent (Elman), Radical Basis Function (RBF) and Generalized Radial Basis Function (GRBF) neural networks (NNs) and compared with the fitting method based on the Marrone model. The GBRFNN was found to perform best.

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Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks

Liu

Zhu

Roberts

et al. 2019

NUCL SCI TECH

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Analysis of influencing factors on the method for determining boron concentration and dose through dual prompt gamma detection

et al. 2021

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Fast nuclide identification based on a sequential Bayesian method

Zhang

Tan

et al. 2021

NUCL SCI TECH

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Spectrometry analysis based on approximation coefficients and deep belief networks

Cited by 12 publications

References 30 publications

Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks

Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks

Analysis of influencing factors on the method for determining boron concentration and dose through dual prompt gamma detection

Fast nuclide identification based on a sequential Bayesian method

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