分类： 核科学技术 >> 辐射物理与技术 提交时间： 2024-06-19
摘要： In energy-dispersive X-ray fluorescence spectroscopy, the estimation of the pulse amplitude determines the accuracy of the spectrum measurement. The error generated by the amplitude estimation of the pulse output distorted by the measurement system leads to false peaks in the measured spectrum. To eliminate these false peaks and achieve an accurate estimation of the distorted pulse amplitude, a composite neural network model is proposed, which embeds long and short-term memory (LSTM) into the UNet structure. The UNet network realizes the fusion of pulse sequence features and the LSTM model realizes pulse amplitude estimation. The model is trained using simulated pulse datasets with different amplitudes and distortion times. For the pulse height estimation, the average relative error of the trained model on the test set was approximately 0.64%, which is 27.37% lower than that of the traditional trapezoidal shaping algorithm. Offline processing of a standard iron source further validated the pulse height estimation performance of the UNet-LSTM model. After estimating the amplitude of the distorted pulses using the model, the false-peak area was reduced by approximately 91% over the full spectrum and was corrected to the characteristic peak region of interest (ROI). The corrected peak area accounted for approximately 1.32% of the characteristic peak ROI area. The results indicate that the model can accurately estimate the height of distorted pulses and has substantial corrective effects on false peaks.
分类： 物理学 >> 核物理学 提交时间： 2023-10-06
摘要： To correct spectral peak drift and obtain more reliable net counts, this study proposes a long-short memory (LSTM) model fused with a convolutional neural network (CNN) to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples. A predefined mathematical model was used to train the CNNLSTM model and generate a dataset composed of distorted pulse sequences. The trained model was validated using simulated pulses. The relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping, CNN-LSTM, and LSTM models. As a result, for severely distorted pulses, the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35% compared with that of the triangular shaping algorithm. For slightly distorted pulses, the relative error of the CNN-LSTM model was reduced by 0.33% compared with that of the triangular shaping algorithm. The model was then evaluated considering two performance indicators, the correction ratio and the efficiency ratio, which represent the proportion of the increase in peak area of the two characteristic peak regions of interest (ROIs) to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI, respectively. Ten measurement results of the iron ore samples indicate that approximately 86.27% of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI, and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%. The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction, which is of great significance for X-ray spectroscopy and elemental content analyses.
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