The uncertainty of nuclide libraries in the analysis of the gamma spectra of low-and intermediate-level radioactive waste(LILW)using existing methods produces unstable results.To address this problem,a novel spectral ...The uncertainty of nuclide libraries in the analysis of the gamma spectra of low-and intermediate-level radioactive waste(LILW)using existing methods produces unstable results.To address this problem,a novel spectral analysis method is proposed in this study.In this method,overlapping peaks are located using a continuous wavelet transform.An improved quadratic convolution method is proposed to calculate the widths of the peaks and establish a fourth-order filter model to estimate the Compton edge baseline with the overlapping peaks.Combined with the adaptive sensitive nonlinear iterative peak,this method can effectively subtracts the background.Finally,a function describing the peak shape as a filter is used to deconvolve the energy spectrum to achieve accurate qualitative and quantitative analyses of the nuclide without the aid of a nuclide library.Gamma spectrum acquisition experiments for standard point sources of Cs-137 and Eu-152,a segmented gamma scanning experiment for a 200 L standard drum,and a Monte Carlo simulation experiment for triple overlapping peaks using the closest energy of three typical LILW nuclides(Sb-125,Sb-124,and Cs-134)are conducted.The results of the experiments indicate that(1)the novel method and gamma vision(GV)with an accurate nuclide library have the same spectral analysis capability,and the peak area calculation error is less than 4%;(2)compared with the GV,the analysis results of the novel method are more stable;(3)the novel method can be applied to the activity measurement of LILW,and the error of the activity reconstruction at the equivalent radius is 2.4%;and(4)The proposed novel method can quantitatively analyze all nuclides in LILW without a nuclide library.This novel method can improve the accuracy and precision of LILW measurements,provide key technical support for the reasonable disposal of LILW,and ensure the safety of humans and the environment.展开更多
In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamm...In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.展开更多
Research for detecting or obtaining radionuclide by gamma energy spectrum data acquisition and process system is one of the key issues about intelligent measurement of gamma-ray spectrum. For this reason, a software a...Research for detecting or obtaining radionuclide by gamma energy spectrum data acquisition and process system is one of the key issues about intelligent measurement of gamma-ray spectrum. For this reason, a software and hardware implementation schematic design based on ARM ( Advanced RISC Machines) + DSP ( Digital Signal Processor) architecture for gamma energy spectrum data acquisition and processing system is proposed. The paper discusses in detail some key technologies such as communication interface design between microcontroller ARM and digital signal processor DSP,distribution scheduling under multi-task in the ARM-Linux,DSP handling procedures for multi-channel A / D high-speed sample. At the same time,because the traditional Gaussian fitting to determine the boundary of peak is not ideal,it puts forward a weighting factor of Gaussian function least squares fitting realize boundary determined. Finally gamma-spectrum data from sodium iodide NaI( TI) scintillation detector is tested and processed in the new system. The results show that gamma energy spectrum data acquisition and process system is perfect functionality, stable and convergence in unimodal. Compared with data from conventional energy spectrometers,the system can keep better energy resolution in a wide range of pulse pass rate.展开更多
Identifying the unknown geometric and material information of a multi-shield object by analyzing the radiation signature measurements is always an important problem in national and global security. In order to identif...Identifying the unknown geometric and material information of a multi-shield object by analyzing the radiation signature measurements is always an important problem in national and global security. In order to identify the unknown shielding layer thicknesses of a source/shield system with gamma-ray spectra, we have developed a derivative-free inverse radiation transport model based on a differential evolution algorithm with global and local neighbourhoods(IRT-DEGL). In the present paper, the IRT-DEGL model is further extended for estimating the unknown thicknesses with random initial guesses and material mass densities of multi-shielding layers as well as their combinations. Using the detected gamma-ray spectra,the illustration of inverse studies is implemented and the main influence factors for inverse results are also analyzed.展开更多
This study proposes a novel feature extraction approach for radionuclide identification to increase the precision of identification of the gamma-ray energy spectrum set.For easier utilization of the information contai...This study proposes a novel feature extraction approach for radionuclide identification to increase the precision of identification of the gamma-ray energy spectrum set.For easier utilization of the information contained in the spectra,the vectors of the gamma-ray energy spectra from Euclidean space,which are fingerprints of the different types of radionuclides,were mapped to matrices in the Banach space.Subsequently,to make the spectra in matrix form easier to apply to image-based deep learning frameworks,the matrices of the gamma-ray energy spectra were mapped to images in the RGB color space.A deep convolutional neural network(DCNN)model was constructed and trained on the ImageNet dataset.The mapped gamma-ray energy spectrum images were applied as inputs to the DCNN model,and the corresponding outputs of the convolution layers and fully connected layers were transferred as descriptors of the images to construct a new classification model for radionuclide identification.The transferred image descriptors consist of global and local features,where the activation vectors of fully connected layers are global features,and activations from convolution layers are local features.A series of comparative experiments between the transferred image descriptors,peak information,features extracted by the histogram of the oriented gradients(HOG),and scale-invariant feature transform(SIFT)using both synthetic and measured data were applied to 11 classical classifiers.The results demonstrate that although the gamma-ray energy spectrum images are completely unfamiliar to the DCNN model and have not been used in the pre-training process,the transferred image descriptors achieved good classification results.The global features have strong semantic information,which achieves an average accuracy of 92.76%and 94.86%on the synthetic dataset and measured dataset,respectively.The results of the statistical comparison of features demonstrate that the proposed approach outperforms the peak-searching-based method,HOG,and SIFT on the synthetic and measured datasets.展开更多
The characteristic gamma-ray spectrum of TNT in the soil induced by DT neutrons is measured by the PFTNA demining system. The GEANT4 toolkit is used to simulate the whole experimental procedure. The simulated spectra ...The characteristic gamma-ray spectrum of TNT in the soil induced by DT neutrons is measured by the PFTNA demining system. The GEANT4 toolkit is used to simulate the whole experimental procedure. The simulated spectra are compared with the experimental spectra, and they are mainly consistent. The share of the background sources such as neutrons and gamma is obtained and the contribution that the experimental apparatus to the background, such as shielding, detector sleeve and moderator, is analyzed. The effective gamma signal(from soil and TNT) is 29% of the full spectrum signal, and the background signal, more than 68%, this is mainly produced by shielding and the detector sleeve. By gradually optimizing the shielding and the cadmium sheet of the detector sleeve, the share of the effective gamma signal increases to 47%, and the background signal reduces to 18%.展开更多
A Levenberg–Marquardt Gaussian fitting algorithm has been used for analyzing the overlap of three peaks(the 583-ke V peak of^(208)Tl, the 609-ke V peak of214 Bi, and the 662-ke V peak of^(137)Cs) using an in situ Na ...A Levenberg–Marquardt Gaussian fitting algorithm has been used for analyzing the overlap of three peaks(the 583-ke V peak of^(208)Tl, the 609-ke V peak of214 Bi, and the 662-ke V peak of^(137)Cs) using an in situ Na I(Tl) scintillation spectrometer. The algorithm, in addition,was compared with a genetic algorithm used for multiple deconvolution. The three fitted peak areas(583, 609, and662 ke V) were calculated from the measured gamma-ray spectra obtained from a simulation experiment in which a^(137) Cs source was buried at different soil depths(from 18 to38 cm). The application of the Levenberg–Marquardt algorithm yielded similar results compared to the genetic algorithm. A lack-of-fit test showed that the fitting is good when the instrumental noise levels were estimated from replicated analyses. The relative fitting error of the total net area and the residual standard deviation were within 5 %and 0.04, respectively, and the goodness of the fitting was better than 0.98. While the methods used in this paper give high performance, the results may lead to incorrect estimation when the signal-to-noise ratio is smaller than-30 d B. This study is useful for the determination of radioactive specific activity of^(137) Cs by in situ spectrometry.展开更多
We have established a set of laboratory measurements which is used for capturing element gammma spectrum. Standard captured gamma ray spectra for ten elements, including Si, Ca, Fe, are obtained using the measurements...We have established a set of laboratory measurements which is used for capturing element gammma spectrum. Standard captured gamma ray spectra for ten elements, including Si, Ca, Fe, are obtained using the measurements for the first time in China. We also simulated the capture gamma ray spectra of the ten elements using Monte Carlo methodology with the same parameters of our experimental measurements. Comparing the experiment and simulation results with the data from the International Atomic Energy Agency＇s Nuclear Data Center, we obtained the standard captured gamma ray spectra of the ten elements, which, as calibration spectra, are used to calibrate the raw spectrum in data processing. This method solved the key problem during the conversion from the original measuring spectrum to the yield of each element in the data processing. The method can effectively improve the accuracy of the element yield calculation.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12205190,11805121)the Science and Technology Commission of Shanghai Municipality(No.21ZR1435400).
文摘The uncertainty of nuclide libraries in the analysis of the gamma spectra of low-and intermediate-level radioactive waste(LILW)using existing methods produces unstable results.To address this problem,a novel spectral analysis method is proposed in this study.In this method,overlapping peaks are located using a continuous wavelet transform.An improved quadratic convolution method is proposed to calculate the widths of the peaks and establish a fourth-order filter model to estimate the Compton edge baseline with the overlapping peaks.Combined with the adaptive sensitive nonlinear iterative peak,this method can effectively subtracts the background.Finally,a function describing the peak shape as a filter is used to deconvolve the energy spectrum to achieve accurate qualitative and quantitative analyses of the nuclide without the aid of a nuclide library.Gamma spectrum acquisition experiments for standard point sources of Cs-137 and Eu-152,a segmented gamma scanning experiment for a 200 L standard drum,and a Monte Carlo simulation experiment for triple overlapping peaks using the closest energy of three typical LILW nuclides(Sb-125,Sb-124,and Cs-134)are conducted.The results of the experiments indicate that(1)the novel method and gamma vision(GV)with an accurate nuclide library have the same spectral analysis capability,and the peak area calculation error is less than 4%;(2)compared with the GV,the analysis results of the novel method are more stable;(3)the novel method can be applied to the activity measurement of LILW,and the error of the activity reconstruction at the equivalent radius is 2.4%;and(4)The proposed novel method can quantitatively analyze all nuclides in LILW without a nuclide library.This novel method can improve the accuracy and precision of LILW measurements,provide key technical support for the reasonable disposal of LILW,and ensure the safety of humans and the environment.
基金the National Natural Science Foundation of China(No.42127807)Natural Science Foundation of Sichuan Province(Nos.23NSFSCC0116 and 2022NSFSC12333)the Nuclear Energy Development Project(No.[2021]-88).
文摘In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.
基金Sponsored by the Natural Science Fundation of Jiangxi Province(Grant No.20114BAB211026 and No.20122BA-B201028)Open Science Fund from Key Laboratory of Radioactive Geology and Exploration Technology Fundamental Science for National Defense,East China Institute of Technology(Grant No.2010RGET11)
文摘Research for detecting or obtaining radionuclide by gamma energy spectrum data acquisition and process system is one of the key issues about intelligent measurement of gamma-ray spectrum. For this reason, a software and hardware implementation schematic design based on ARM ( Advanced RISC Machines) + DSP ( Digital Signal Processor) architecture for gamma energy spectrum data acquisition and processing system is proposed. The paper discusses in detail some key technologies such as communication interface design between microcontroller ARM and digital signal processor DSP,distribution scheduling under multi-task in the ARM-Linux,DSP handling procedures for multi-channel A / D high-speed sample. At the same time,because the traditional Gaussian fitting to determine the boundary of peak is not ideal,it puts forward a weighting factor of Gaussian function least squares fitting realize boundary determined. Finally gamma-spectrum data from sodium iodide NaI( TI) scintillation detector is tested and processed in the new system. The results show that gamma energy spectrum data acquisition and process system is perfect functionality, stable and convergence in unimodal. Compared with data from conventional energy spectrometers,the system can keep better energy resolution in a wide range of pulse pass rate.
基金supported by the National Natural Science Foundation of China(Nos.11605163 and 21504085)the China Academy of Engineering Physics Foundation for Development of Science and Technology(No.201580103014 and No.2015B0301063)+1 种基金the Foundation for Special Talents in China Academy of Engineering Physics(No.TP201502-3)the Sichuan Science and Technology Development Foundation for Young Scientists(No.2017Q0050)
文摘Identifying the unknown geometric and material information of a multi-shield object by analyzing the radiation signature measurements is always an important problem in national and global security. In order to identify the unknown shielding layer thicknesses of a source/shield system with gamma-ray spectra, we have developed a derivative-free inverse radiation transport model based on a differential evolution algorithm with global and local neighbourhoods(IRT-DEGL). In the present paper, the IRT-DEGL model is further extended for estimating the unknown thicknesses with random initial guesses and material mass densities of multi-shielding layers as well as their combinations. Using the detected gamma-ray spectra,the illustration of inverse studies is implemented and the main influence factors for inverse results are also analyzed.
基金supported by the National Defense Fundamental Research Project(No.JCKY2020404C004)Sichuan Science and Technology Program(No.22NSFSC0044).
文摘This study proposes a novel feature extraction approach for radionuclide identification to increase the precision of identification of the gamma-ray energy spectrum set.For easier utilization of the information contained in the spectra,the vectors of the gamma-ray energy spectra from Euclidean space,which are fingerprints of the different types of radionuclides,were mapped to matrices in the Banach space.Subsequently,to make the spectra in matrix form easier to apply to image-based deep learning frameworks,the matrices of the gamma-ray energy spectra were mapped to images in the RGB color space.A deep convolutional neural network(DCNN)model was constructed and trained on the ImageNet dataset.The mapped gamma-ray energy spectrum images were applied as inputs to the DCNN model,and the corresponding outputs of the convolution layers and fully connected layers were transferred as descriptors of the images to construct a new classification model for radionuclide identification.The transferred image descriptors consist of global and local features,where the activation vectors of fully connected layers are global features,and activations from convolution layers are local features.A series of comparative experiments between the transferred image descriptors,peak information,features extracted by the histogram of the oriented gradients(HOG),and scale-invariant feature transform(SIFT)using both synthetic and measured data were applied to 11 classical classifiers.The results demonstrate that although the gamma-ray energy spectrum images are completely unfamiliar to the DCNN model and have not been used in the pre-training process,the transferred image descriptors achieved good classification results.The global features have strong semantic information,which achieves an average accuracy of 92.76%and 94.86%on the synthetic dataset and measured dataset,respectively.The results of the statistical comparison of features demonstrate that the proposed approach outperforms the peak-searching-based method,HOG,and SIFT on the synthetic and measured datasets.
文摘The characteristic gamma-ray spectrum of TNT in the soil induced by DT neutrons is measured by the PFTNA demining system. The GEANT4 toolkit is used to simulate the whole experimental procedure. The simulated spectra are compared with the experimental spectra, and they are mainly consistent. The share of the background sources such as neutrons and gamma is obtained and the contribution that the experimental apparatus to the background, such as shielding, detector sleeve and moderator, is analyzed. The effective gamma signal(from soil and TNT) is 29% of the full spectrum signal, and the background signal, more than 68%, this is mainly produced by shielding and the detector sleeve. By gradually optimizing the shielding and the cadmium sheet of the detector sleeve, the share of the effective gamma signal increases to 47%, and the background signal reduces to 18%.
基金supported by the National Natural Science Foundation of China(No.41474107)
文摘A Levenberg–Marquardt Gaussian fitting algorithm has been used for analyzing the overlap of three peaks(the 583-ke V peak of^(208)Tl, the 609-ke V peak of214 Bi, and the 662-ke V peak of^(137)Cs) using an in situ Na I(Tl) scintillation spectrometer. The algorithm, in addition,was compared with a genetic algorithm used for multiple deconvolution. The three fitted peak areas(583, 609, and662 ke V) were calculated from the measured gamma-ray spectra obtained from a simulation experiment in which a^(137) Cs source was buried at different soil depths(from 18 to38 cm). The application of the Levenberg–Marquardt algorithm yielded similar results compared to the genetic algorithm. A lack-of-fit test showed that the fitting is good when the instrumental noise levels were estimated from replicated analyses. The relative fitting error of the total net area and the residual standard deviation were within 5 %and 0.04, respectively, and the goodness of the fitting was better than 0.98. While the methods used in this paper give high performance, the results may lead to incorrect estimation when the signal-to-noise ratio is smaller than-30 d B. This study is useful for the determination of radioactive specific activity of^(137) Cs by in situ spectrometry.
基金sponsored by the National S&T Major Special Project(No. 2011ZX05020-008)
文摘We have established a set of laboratory measurements which is used for capturing element gammma spectrum. Standard captured gamma ray spectra for ten elements, including Si, Ca, Fe, are obtained using the measurements for the first time in China. We also simulated the capture gamma ray spectra of the ten elements using Monte Carlo methodology with the same parameters of our experimental measurements. Comparing the experiment and simulation results with the data from the International Atomic Energy Agency＇s Nuclear Data Center, we obtained the standard captured gamma ray spectra of the ten elements, which, as calibration spectra, are used to calibrate the raw spectrum in data processing. This method solved the key problem during the conversion from the original measuring spectrum to the yield of each element in the data processing. The method can effectively improve the accuracy of the element yield calculation.
