Trapezoidal pile-up nuclear pulse parameter identification method based on deep learning transformer model

Wang, Qingtai; Hong-quan, Huang; Ma, Xing-Ke; Shen, Zhi-Wen; Zhong, Cheng-Lin; Ding, Wei-Cheng; Zhou, Wei; Zhou, Jianbin

doi:10.1016/j.apradiso.2022.110515

Cited by 3 publications

(1 citation statement)

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“…For the energy spectrum, deconvolution methods based on constrained optimization have achieved a high-resolution boost [13,14] and may represent a complementary approach to the above pulse throughput enhancing method to compensate for resolution deterioration. With the rapid development of computing power and complex model equation-solving methods and algorithms, some works have used artificial intelligence methods [15][16][17][18] to restore pileup pulses. However, the pileup effect occurs because a certain number of piled-up pulses are rejected to maintain resolution and cannot be recognized.…”

Section: Introductionmentioning

confidence: 99%

An Ultra-Throughput Boost Method for Gamma-Ray Spectrometers

Li,

Zhou,

Zhang

et al. 2024

Energies

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(1) Background: Generally, in nuclear medicine and nuclear power plants, energy spectrum measurements and radioactive nuclide identification are required for evaluation of strong radiation fields to ensure nuclear safety and security; thereby, damage is prevented to nuclear facilities caused by natural disasters or the criminal smuggling of nuclear materials. High count rates can lead to signal accumulation, negatively affecting the performance of gamma spectrometers, and in severe cases, even damaging the detectors. Higher pulse throughput with better energy resolution is the ultimate goal of a gamma-ray spectrometer. Traditionally, pileup pulses, which cause dead time and affect throughput, are rejected to maintain good energy resolution. (2) Method: In this paper, an ultra-throughput boost (UTB) off-line processing method was used to improve the throughput and reduce the pileup effect of the spectrometer. Firstly, by fitting the impulse signal of the detector, the response matrix was built by the functional model of a dual exponential tail convolved with the Gaussian kernel; then, a quadratic programming method based on a non-negative least squares (NNLS) algorithm was adopted to solve the constrained optimization problem for the inversion. (3) Results: Both the simulated and experimental results of the UTB method show that most of the impulses in the pulse sequence from the scintillator detector were restored to δ-like pulses, and the throughput of the UTB method for the NaI(Tl) spectrometer reached 207 kcps with a resolution of 7.71% @661.7 keV. A reduction was also seen in the high energy pileup phenomenon. (4) Conclusions: We conclude that the UTB method can restore individual and piled-up pulses to δ-like sequences, effectively boosting pulse throughput and suppressing high-energy tailing and sum peaks caused by the pileup effect at the cost of a slight loss in energy resolution.

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Section: Introductionmentioning

confidence: 99%

An Ultra-Throughput Boost Method for Gamma-Ray Spectrometers

Li,

Zhou,

Zhang

et al. 2024

Energies

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Evaluation of neural network models and quality forecasting based on process time‐series data

Wang,

Liu,

Dong

et al. 2023

Can J Chem Eng

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Complex industrial process modelling is critically important within the context of industrial intelligence. In recent years, soft sensor techniques based on neural networks have become increasingly popular for modelling nonlinear industrial processes. This paper proposes an integrated framework of neural network modelling and evaluation for nonlinear dynamic processes. This framework achieves an integrated solution for modelling, prediction, evaluation, and network structure parameter selection. It can be applied to noisy sensors and dense data in the time domain. The framework's proposed evaluation mechanism employs two novel evaluation metrics, the variational auto‐encoder (VAE)‐based Kullback–Leibler (KL) divergence metric and the maximum likelihood estimation‐based J metric, which both evaluate the model by mining the statistical properties of the residuals. The framework models the dynamic process with a model order based‐gated recurrent units (MOb‐GRU) neural network and a modified transformer model. Numerical experiments demonstrate that the evaluation mechanism functions properly in scenarios with multiple signal‐to‐noise ratios and multiple noise statistical properties and that the framework produces accurate modelling results.

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