1 Introduction

Distinguishing useful information from irrelevant information is one of the basic concerns of any spectrometric method.However,extracting the correct information from spectral sections is a very complex task for traditional algorithms that identify radionuclides based on peak search because of the limited resolution of the equipment and the often-overlapped peaks[1].Thus,deconvolution,expert knowledge,and human participation are needed.

An artificial neural network(ANN)is a form of artificial intelligence that attempts to mimic the behavior of the human brain and nervous system[2].The ANN can extract information using the full spectral range[3].ANNs have been applied to many aspects of gamma spectral analysis[4–11],and feature extraction is the key step in the majority of these studies.To date,algorithms on radionuclide identi fication are based on feature extraction,and ANNs are a major technique for performing information extraction.For example,Stinnett et al.[12]proposed a method based on feature extraction and Bayesian classi fiers to identify isotopes.Yang et al.[13]developed an algorithm based on feature extraction and feedforward neural networks(NNs)to discriminate alpha–gamma radiation.However,manually supervised feature extraction requires specialized knowledge,and its parameters need to be determined,which is a complex problem.

Deep learning is a class of machine learning algorithm that uses a cascade of many layers of nonlinear processing units for feature extraction(when the number of hidden layers is smaller than the input dimension),or maps the feature to a higher-dimensional space(when the number of hidden layers is larger than the input dimension),which does not require domain knowledge[14].This algorithm has been applied to automatic speech recognition,image recognition,smart cars,robots,etc.[15–19].In this study,an analytical spectrometry method based on approximation coefficients and deep belief networks(DBNs)is proposed.Experiments with differentdetection times,different numbers of radionuclides,different detection distances,and moving radionuclides were designed to evaluate the identi fication performance of the proposed method.

2 Theories and methods

2.1 Overview of the proposed method

The proposed method is composed of DBN training and DBN prediction(Fig.1).The DBN is trained according to the flowchart shown in Fig.1a.This technique consists of spectrum simulation,extraction of approximation coef ficients of the spectrum,and training.First,the model of the related detector is designed,and simulation spectra of the radionuclides of interest are obtained using the Monte Carlo N-particle transport code(MCNP)(a software package forsimulating nuclearprocesses).Second,approximation coefficients of the spectrum are extracted by wavelet decomposition and normalized to eliminate the intensity difference between different spectra.Then,the normalized approximation coefficients are used as the input to the DBN.Finally,the DBN is trained using the samples of the simulation spectra,which are encoded as the input of the DBN and their target output.For DBN prediction,the trained DBN is used to predict the isotopic composition of the spectrum measured in the real environment using the process shown in Fig.1b.This process consists of spectral measurement and background subtraction,extraction of the approximation coefficients of the spectrum,and identi fication.First,radionuclide and background spectra are detected by the detector and smoothed to reduce noise interference.Then,the subtraction spectrum is obtained by subtracting the portion of the smoothed background spectrum according to the ratio of the radionuclide scan time to the background scan time from the smoothed radionuclide spectrum.Second,the approximation coefficients of the subtraction spectrum are extracted by wavelet decomposition and normalized to remove the effect of the change in spectrum intensity.Then,the normalized approximation coefficients are used as the input for the DBN.Finally,the trained DBN is used to predict the isotopic composition of the spectrum measured in the real environment,and the identi fication performance of the proposed method is evaluated by comparing the output of the DBN with its target output.

Fig.1 Schematic diagram of the proposed algorithm

2.2 Approximation coefficients

The low-frequency content is the most important part of many signals,because this component gives the signal its identity.The high-frequency content,on the other hand,imparts flavor,or nuance.Approximations and details are often discussed in wavelet analysis.Approximations are high-scale,low-frequency componentsofthe signal,whereas details are low-scale,high-frequency components.The original signal S would be decomposed into approximations and details after the signal underwent a singlelevel discrete wavelet transform.This process is accomplished by applying two complementary filters to the original signal,which emerges as two signals,as shown in Fig.2a.

到了晚上，我以最快的速度洗漱完毕后，就爬到了床上，一边看书，一边等待着妈妈上床。不久，妈妈过来说：“一一，已经九点了，你赶紧先睡。我现在去洗澡，如果等会我来的时候你还没睡着，我就不跟你睡了。”我失望地“哦”了一声。躺在床上，我不断地竖起耳朵仔细听着外面的动静，在我想来，妈妈现在去洗澡的话，应该会有一些“哗啦哗啦”的流水声的，但现在外面什么声音都没有，只有爸爸书房里的灯光洒进我的房间。

于情于理，嫖宿幼女罪既不能在法律上做到罪责刑相适应，公众情绪对其也不能接受。全国人大代表孙晓梅和全国律协未成年人保护专业委员会主任佟丽华等人都是废除此罪的积极倡导者，他们倾向于将此罪归于强奸罪统一调整。

3.2 审评要点 COU是生物标志物资格认定中最重要的审评内容，也是生物标志物区别于其他药物研发工具的特点。它包括一个涵盖生物标志物名称、身份和在药物研发过程中使用目的的 “使用声明 （use statement）”，以及如何在特定环境中使用生物标志物的全面描述即 “合格使用的条件（conditions for qualified use）”[8]。 提供所需的数据以支持特定COU是申请人的义务。申请人在与FDA沟通的早期就应尽可能全面清楚地阐明COU，并在随后的资格认定程序中不断完善。

Fig.2 Schematic of wavelet decomposition.a Single-level and b multilevel discrete wavelet transforms

2.3 DBN model

Deep learning is a machine learning paradigm that focuses on learning deep hierarchical data models.In this algorithm,a hierarchy of intermediate representations is hypothesized to be needed to learn a high-level representation of the data[21].A DBN is one kind of deep learning.In machine learning,a DBN is a generative graphical model,or alternatively,a type of deep NN composed of multilayers of latent variables(hidden units),with connections between layers but not between units within each layer.A DBN can be viewed as a composition of restricted Boltzmann machines(RBMs),in which the hidden layer of each sub-network serves as the visible layer for the next[22,23].Its schematic network structure is shown in Fig.3.The structure comprises one visible layer,three hidden layers(h1,h2,h3),and one output layer[24].The number of RBMs is not constant and is determined by the nature of the data.RBMs contain a layer of visible units that represent the data and a layer of hidden units that learn to represent features that capture higher-order correlations in the data.This module is a primitive DBN.

The decomposition process can be iterated,with successive approximations being decomposed in turn,and the number of iterations is called the decomposition level(Fig.2b).The cA1,cA2,and cA3are the approximations of the original signal S,while cD1,cD2,and cD3are the details at different decomposition levels.Given that the analytical process is iterative,the processes can theoretically be continued inde finitely.In practice,the suitable number of levels is based on the nature of the signal,and a number from 3 to 5 is recommended.Approximation coefficients cAi(i=1,2,3,4,5)of the spectrum can be considered as the input to the DBN.The cAihas the same spectral shape as the original spectrum,but it is more smoothed.The originalspectrum (dimension:1024)detected by an(Tl)detector is decomposed in this study by three levels of discrete wavelet transforms(mother wavelet:Daubechies wavelets)using the MATLAB Wavelet Toolbox[20].The dimension of the extracted approximation coefficients is 128,which is much lower than the dimension of the original spectrum(dimension:1024).After normalization,the extracted approximation coef ficients are used as the input to the DBN.

An RBM has only a visible layer and a hidden layer,and these layers are connected by a matrix of symmetrically weighted connections W.No connections are found within a layer.Given a vector of activities v for the visible units,the hidden units are all conditionally independent,facilitating the sampling of a vector h from the factorial posterior distribution over hidden vectors p(h|v,W).Sampling from p(v|h,W)is also easy.A learning signal can be obtained by starting with an observed data vector on the visible units and alternating several times between sampling from p(h|v,W)and p(v|h,W).This signal is simply the difference between the pairwise correlations of the visible and hidden units at the beginning and end of the sampling.Several reports have provided more detailed descriptions of RBM[25,26].

Fig.3 Schematic overview of a deep belief network(DBN)

The variables of the final hidden layer represent the desired outputs,which can be encoded to the error backpropagation(BP)NN.The BP algorithm functions much better if the feature detectors(hidden units)in the hidden layers are initialized by learning a DBN that models the structure in the input data.The DBN algorithm was realized in this study using a MATLAB Toolbox named DeepLearnToolbox (https://github.com/rasmusbergpalm/DeepLearnToolbox),and the key parameters of the DBN used during the training process are as follows:the number of hidden layers is 1,and the dimension of the hidden layer is 1024.The dimension of the visible layer is 128 for extracting 128 approximation coefficients as the input to the DBN,the dimension of the output layer is 9 for selecting the spectra of 9 radionuclides(i.e.,57Co,75Se,60Co,133Ba,137Cs,192Ir,241Am,152Eu,and238Pu)as training samples;thus,the established DBN model is 128×1024×9.Each neuron in the output layer indicated whether the related radionuclide existed or not,and the radionuclide existed in the environment only when the output value of the related output layer neuron was greater than or equal to the threshold(0.85).

3 Experiments and results

伏牛山区中河南片区粮食秸秆资源可获得量。根据对小范围农业生物质进行现场测量和评定，并结合所走访农民得出，伏牛山区一些农作物的谷草比为：玉米为1∶1.2、小麦为1∶1.3。国家发改委在《生物质能开发利用管理文件和技术规定》上发布了“常见农作物秸秆草谷比”。因此，参考该文件，且综合考虑伏牛山区各县的实际，以粮食1∶1.3的谷草比参数来进行粮食秸秆资源的估算。

3.1 Assessment method

A gamma-ray spectrometry system,which consists of a 3 in×3 in NaI(Tl)detector(ORTEC Inc.),a PC-based multichannel analyzer(ORTEC Inc.),and MAESTRO software installed on the PC were used to assess the proposed method,and the MAESTRO 7.01(ORTEC Inc.)software was used to record the spectra.The energy of this detector ranged from 30 keV to 3000 keV,and the resolution was approximately 7.7%(at 662 keV).The spectra were recorded by sending a request to MAESTRO 7.01 software using its software development kit.Any spectrum that satis fied the requirements could be detected.Table 1 lists the radionuclides utilized in the experiment,and these radionuclides are labeled as Radio-1,Radio-2,and Radio-3.

在育种方面，采用低温等离子技术聚合进行种子的包衣处理，可以有效降低种子培育的时间和成本，同时具有防虫防病的作用。

Given that the DBN can only identify the trained radionuclides,and its predictive ability depends on the number of samples,adding samples of more radionuclides during the training process of the DBN is recommended.However,the majority of radionuclides are not available in the laboratory.Simulation spectra of the radionuclides of interest(i.e.,57Co,75Se,60Co,133Ba,137Cs,192Ir,241Am,152Eu,and238Pu)can be obtained by the MCNP,which models the mentioned detector based on its characteristics.All radionuclides except238Pu are industrial radionuclides.All simulation spectra are broadened with the response function of the mentioned detector.

综上所述，本研究结果证实，mtDNA拷贝数增加能提高MDSCs-IPCs转化率，mtDNA通过控制增加Wnt通路相关的sFRP，抑制TCF转录因子，促进MDSC细胞分化成IPC细胞，增加insulin和C-peptide生成。

Detection rate was used to assess the identi fication performance of the proposed algorithm.This rate is the ratio of the correctly identi fied data to the total amount of data,as shown in Eq.(1)[27],as follows:

Whether the trained DBN has learned the training samples properly should be veri fied.If not,the related parameters(section:DBN model)of the DBN should be adjusted until the mean square error of the DBN on the training set reaches an acceptable value.Table 2 lists the results of the DBN training.The trained DBN learned the high-level features of the training set completely.Theoretically,this DBN could predict the isotopic compositions of the spectrum measured in the real environment for all radionuclides(i.e.,57Co,75Se,60Co,133Ba,137Cs,192Ir,241Am,152Eu,and238Pu)with a detection rate of 100%.

The selection of training samples for the DBN is an important part of the proposed method.This process determines the predictive ability of the DBN,so detailed information about training samples will be introduced.Experiments using different detection times,numbers of radionuclides,and detection distances were designed,because these parameters can change the shape of the spectrum.The effect of radionuclides in motion on the identi fication performance of the proposed method was evaluated,because radionuclides may have a certain velocity in the real environment.

Training samples were selected.Simulation spectra of radionuclides of interest(i.e.,57Co,75Se,60Co,133Ba,137Cs,192Ir,241Am,152Eu,and238Pu)were simulated by the MCNP,and the normalized simulation spectra generated 511(29–1=511)spectra according to the theory of combinations,which were all used as training samples forthe DBN.The conditions with 1 radionuclide corresponded to 9 combinations(i.e.,57Co,75Se,60Co,133Ba,137Cs,192Ir,241Am,152Eu,and238Pu),the conditions with 2 radionuclidescorresponded to 36 combinations(i.e.,57Co+75Se,57Co+60Co,…,152Eu+238Pu),and the number of combinations for the conditions with 3,4,5,6,7,8,and 9 radionuclides can be acquired according to the theory of combinations,which is how we obtained the 511 spectra.The 511 normalized spectra are encoded as the input to the DBN and their target output before DBN training,and the target output was composed of nine figures ranging from 0 to 1.For example,the target output of the57Co spectrum would be[1 0 0 0 0 0 0 0 0]if we sequenced the radionuclides as57Co,75Se,60Co,133Ba,137Cs,192Ir,241Am,152Eu,and238Pu.So,the target output of the75Se spectrum was[0 1 0 0 0 0 0 0 0],the target output of the 57Co+75Se spectrum was[1 1 0 0 0 0 0 0 0],and so on.

3.2 DBN training

The accurate radionuclide identi fication distance(ARID)of each radionuclide was calculated from the detection rate results with respect to the distance from the source to the detector.The ARID is the maximum distance at which detection rates are reliably ascertained to be above the required level[28].The detection rate of the acceptance level in this study is 98.3%,as recommended by the ANSI N42.35 standard[29].

Table 1 Radionuclides for source identi fication conducted in this study

Label Radio-1 Radio-2 Radio-3 Radionuclide 238Pu 60Co 137Cs Activity(μCi) 8.89 × 103 1.59 1.42

where TP indicates true positive,TN indicates true negative,FP indicates false positive,and FN indicates false negative.

3.3 Effect of detection time on the proposed method

Experiments using different detection times were conducted,because detection time can lead to a change in spectral intensity.The detector was installed at a fixed location,and the radionuclides were placed in front of the detector at point A(Fig.4).The background spectrum without any source was recorded for 300 s to generate the background template.The spectra of Radio-1,Radio-2,and Radio-3 placed at point A(Fig.4)were recorded to evaluate the detection time effect on the identi fication performance of the proposed method.The measurement was repeated 10 times for each condition,and detection timeswere 1,2,3,4,and 5 s.For example,the measurement for Radio-1 with monitoring point at A(Fig.4)and a detection time of 1 s would be repeated 10 times.The same rules apply to other detection times and radionuclides.A total of 150 spectra were recorded,which were all used as verifying samples.Ten spectra could be recorded at each detection time for each radionuclide,resulting in 50 spectra for each radionuclide.Each spectrum has a sample number,the value of which was determined by the measurement sequence.For example,10 Radio-1 spectra with a detection time of 1 s would have sample numbers 1–10,10 Radio-1 spectra with a detection time of 2 s would have sample numbers 11–20,etc.The same rules apply to Radio-2 and Radio-3.

Table 2 Results of DBN training

Radionuclide 57Co 75Se 60Co 133Ba 137Cs Detection rate(%) 100 100 100 100 100 Radionuclide 192Ir 241Am 152Eu 238Pu Detection rate(%) 100 100 100 100

Fig.4(Color online)Sketch of the first experimental environment

Fig.5(Color online)Output values of deep belief network(DBN)at different sample numbers when only238Pu exists in the environment

We focused only on the output neurons of the DBN for 238Pu,60Co,and137Cs to select three radionuclides(38Pu,60Co,and137Cs)to evaluate the identi fication performance of the proposed method in the real environment,and all the results related to verifying samples in this study were computed by analyzing the output value of the above three output neurons.Figure 5 shows the results for samples with different detection times when only238Pu exists in the environment.The output value of the DBN for238Pu is greater than the threshold(0.85)at different times when only238Pu exists in the environment.The output value for 60Co or137Cs is less than the threshold,indicating the presence of238Pu in the environment,but the absence of 60Co and137Cs.The results are consistent with the actual situation because of the normalization of approximation coefficients,which can eliminate the effect of the change in spectral intensity.Therefore,the identi fication performance of the proposed method is not affected by the detection time if the minimum net count of the spectrum of related radionuclides is satis fied.Figures 6 and 7 illustrate similar results.The net count of the spectrum of each radionuclide measured at point A(Fig.4)with a detection time of 5 s,and the count of background spectrum with a detection time of 5 s are listed in Table 3.The dose rate of the background without a shielding apparatus was measured in the laboratory(approximately 0.1 μSv/h).

3.4 Effect of the number of radionuclides on the proposed method

Experiments with different numbers of radionuclides were performed,because this parameter can change the spectral shape.The detector was installed at a fixed location,and the radionuclides were placed in front of the detector at point A(Fig.4).The spectra of the cases with different numbers of radionuclides(i.e.,Radio-1,Radio-2,Radio-3,Radio-1+Radio-2,Radio-1+Radio-3,Radio-2+Radio-3,and Radio-1+Radio-2+Radio-3)placed at point A(Fig.4)were recorded to evaluate the effect of the number of radionuclides on the identi fication performance of the proposed method.The measurement was repeated 10 times for each condition,and the detection time was 5 s.For example,the measurement for Radio-1 with monitoring point at A(Fig.4)and a detection time of 5 s would be repeated 10 times.The same rules apply to other combinations.Hence,70 spectra were recorded,which were all used as verifying samples.Three combinations(i.e.,Radio-1,Radio-2,and Radio-3)correspond to the condition with 1 radionuclide.Three combinations(i.e.,Radio-1+Radio-2, Radio-1+Radio-3, and Radio-2+Radio-3)correspond to thecondition with two radionuclides.One combination(i.e.,Radio-1+Radio-2+Radio-3)corresponds to the condition with three radionuclides.

Fig.6(Color online)Output values of DBN at different sample numbers when only60Co exists in the environment

Fig.7(Color online)Output values of DBN at different sample numbers when only137Cs exists in the environment

Table 3 Count of radionuclides and background spectrum with a detection time of 5 s

Radionuclide 238Pu 60Co 137Cs Net count 3049 8161 3585 Background 2614 2614 2614

Table 4 lists the results of samples with different numbers of radionuclides.All radionuclides(i.e.,238Pu,60Co,and137Cs)have a detection rate of 100%,which demonstrates that the proposed algorithm can predict the isotopic compositions of the spectra of multiple radionuclides.This phenomenon is due to the fact that the DBN was trained using samples with mixed spectra of multiple radionuclides.However,the training samples are composed of simulation spectra which differ from the spectra measured in the real environment,especially in the low-energyregion,although their shapes are similar.This phenomenon may re flect the fact that the DBN has strong tolerance,indicating promising machine learning for spectrometry analysis.

据园区管委会副主任余亮茹介绍，上海化工区是国家新型工业化产业示范基地、国家级经济技术开发区、国家生态工业示范园区、国家循环经济工作先进单位，连续6年蝉联全国化工园区20强榜首。

Table 4 Single radionuclide detection rates(%)of the samples with different numbers of radionuclides

Number 238Pu 60Co 137Cs 1 100 100 100 2 100 100 100 3 100 100 100

3.5 Effect of detection distance on the proposed method

Experiments with different detection distances were conducted,because this parameter can also lead to a change in the spectral shape.The detector was installed at a fixed location,and the radionuclides were placed in front of the detector at points A,B,…,O(15 points)at 10-cm intervals(Fig.8).The spectra of the radionuclides were recorded to evaluate the effect of detection distance on the identi fication performance of the proposed method.The measurement was repeated 10 times for each condition,and the detection time was 10 s.For example,the measurement for Radio-1 with monitoring point at A(Fig.8)and a detection time of 10 s would be repeated 10 times.The same rules apply to other monitoring points or radionuclides.Thus,450 spectra were recorded,which were all used as verifying samples.

Figure 9 shows the results of samples with different detection distances.The ARIDs of Radio-1(238Pu with an activity of 8.89× 103μCi and a detection time of 10 s),Radio-2(60Co with an activity of 1.59 μCi and a detection time of 10 s),and Radio-3(137Cs with an activity of 1.42 μCi and a detection time of 10 s)are greater than or equal to 60,70,and 60 cm,respectively.The corresponding minimum detectable activities(MDAs)of Radio-1(238Pu with a detection distance of 60 cm and a detection time of 10 s),Radio-2(60Co with a detection distance of 70 cm and a detection time of 10 s),and Radio-3(60Cs with a detection distance of 60 cm and a detection time of 10 s)are less than or equal to 8.89×103,1.59,and 1.42 μCi,respectively[30,31].Thus,the identi fication performance of the proposed method is not affected by the detection distance under certain conditions,because the effect of detection distance on the proposed method can be eliminated by the normalization of approximation coef ficients.The detection rate of radionuclides(i.e.,238Pu,60Co,and137Cs)is over 60%when the detection distance is greater than the ARID of the related radionuclide(Fig.9a–c).This result is due to the fact that the detection rate is a statistic,and the numerator TN of Eq.(1)remains almost constant,although the numerator TP in Eq.(1)decreases with an increase in distance.This phenomenon is due to the low percentage of error in identi fication,which indicates the proposed method is a robust method.

Fig.8(Color online)Sketch of the second experimental environment

Fig.9(Color online)Single radionuclide detection rates(%)of samples with different detection distances:a238Pu,b60Co,and c137Cs

The spectra of radionuclides(238Pu,60Co,and137Cs)placed in front of the detector according to their ARIDs are plotted to further visualize the identi fication performance of the proposed method(Fig.10).The signal-to-noise ratio(SNR)is extremely low in this condition,and the corresponding characteristic peaks of radionuclides are not obvious(fewer counts in the photopeak region).However,the proposed method can recognize these radionuclides with a detection rate of 100%.Therefore,the proposed method is suitable for fast radionuclide identi fication,because it requires a lower spectrum count.

3.6 Effect of radionuclides in motion on the proposed method

Fig.10 Spectra of radionuclides with the monitoring point at their ARIDs:a Radio-1,b Radio-2,and c Radio-3

Radionuclides may be carried by people or transported by car in the real environment.Thus,these molecules have a certain velocity.The effect of radionuclides in motion on the identi fication performance of the proposed method should be evaluated.The detector was installed at a fixed location,and the radionuclides were placed in front of the detector at points A,B,…,O at 10-cm intervals(Fig.8).An experiment was designed to assess the effect of radionuclides in motion on the identi fication performance of the proposed method.A person with Radio-1,Radio-2,and Radio-3 approached point A from the starting point O along the line segment OA at a speed of approximately 0.375 m/s.The whole process took approximately 4 s,and 33 spectra were recorded at intervals of approximately 0.1 s.All spectra were used as verifying samples.

Figure 11 shows the results of the samples of the moving radionuclides.The output value of the DBN for137Cs is greater than the threshold(0.85)at time 2.5 s,indicating the identi fication of137Cs.The corresponding value for238Pu is also greater than the threshold(0.85)at time 2.76 s,indicating the identi fication of238Pu.The corresponding value for60Co is greater than the threshold(0.85)at time 3.0 s,indicating the identi fication of60Co.Thus,the output value of the DBN for related radionuclides is always beyond the thresholdafterthecorrespondingradionuclideisrecognized.The proposed method exhibits enhanced identi fication performance for the spectra of moving radionuclides and requires only a short time to identify all radionuclides in the environment.This characteristic is recommended for fast radionuclide identi fication.

Fig.11(Color online)Output values of DBN for samples of moving radionuclides at different times.Output channels of DBN for a238Pu,b60Co,and c137Cs

Fig.12 Spectra of radionuclides(i.e.,Radio-1+Radio-2+Radio-3)at their time of identi fication.a t=2.5 s,b t=2.76 s,and c t=3.0 s

Fig.13(Color online)Identi fication results of MAESTRO 7.01 software for spectra of radionuclides(i.e.,Radio-1+Radio-2+Radio-3)at the times of a t=2.5 s,b t=2.76 s,and c t=3.0 s

Figure 12 demonstrates the spectra in radionuclide identi fication.The spectrum has a low SNR at each time of identi fication of the related radionuclide,and its corresponding characteristic peak cannot be observed.The proposed method is not based on peak detection,but on information from the full spectrum.The proposed algorithm makes a judgment using information from the full spectrum,contrary to the method based on peak detection.Specialized knowledge,deconvolution,and other parameters previously used were not needed after the DBN was trained.

图书馆学情报学期刊率先在中国发起开放获取运动，或发表宣言，或开放出版，或开放存储，或构建平台，以实际行动履行学术期刊的社会责任。

The identi fication results of the spectra contained within Fig.12a–c using MAESTRO 7.01 software(a software for gamma-ray spectrometry analysis)were all ‘No peaks found,’as shown in Fig.13.This is primarily because there were lower counts of radionuclide spectrum in the photopeak region,so the MAESTRO 7.01 software could not find a peak that satis fied its default requirement.Experimental results demonstrate once again that the proposed method is not based on peak detection,but on information from the full spectrum.

4 Conclusion

A method of spectrometry analysis based on approximation coefficients and a DBN was proposed in this study.This method consisted of DBN training and DBN prediction.Approximation coefficients of the spectrum were extracted by wavelet decomposition.The DBN was trained using samplesofsimulationspectraofradionuclidesofinterestand wasusedtopredicttheisotopiccompositionsofthespectrum measured in the real environment.Experimental results show that the identi fication performance of the proposed method was not affected by the detection time,number of radionuclides,or detection distance when the MDA of a single radionuclide was satis fied.The radionuclides used in this study were238Pu(8.89 × 103 μCi),60Co(1.59 μCi),and137Cs(1.42 μCi).The dose rate of the background withoutashieldingapparatuswasmeasuredinthelaboratory(approximately 0.1 μSv/h).The proposed method showed better performance for predicting the isotopic compositions of the spectrum of moving radionuclides.The proposed method identi fied the isotopic compositions in the spectrum using information from the full spectrum,contrary to the method based on peak detection.

The proposed method may exhibit enhanced identi fication performance for the spectrum of radionuclides with higher energy,because the DBN is trained using samples of the simulation spectra of radionuclides of interest,and the difference between the spectrum simulated by the MCNP and that measured in the real environment in the low-energy(＜100 keV)region is larger.In summary,a novel method for spectrometry analysis was developed which overcomes the dif ficulties caused by the insuf ficient availability of the majority of radionuclides in the laboratory.Further work,such as using different detectors(e.g.,lanthanum bromide scintillation detector,cadmium zinc telluride detector,high-purity germanium detector),and the addition of gamma-ray shielding will be performed to verify the identi fication performance of the proposed method.In addition,complex methods combined with the latest advanced algorithms,such as optimization algorithms(e.g.,genetic algorithms,particle swarm algorithms),feature extraction algorithms(Karhunen–Loeve transform,singular-value decomposition),and fuzzy logic algorithms,will be studied to develop more intelligent methods for spectrometry analysis.The proposed method can be executed in a mobile or an embedded system because of the simplicity of the trained DBN.In this study,the algorithm was executed on a Windows-based mini-PC.The proposed method may potentially be suitable for radionuclide identi fication of radiation monitoring instruments because of its high stability and low execution time.

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