Artificial neural network-based method for discriminating Compton scattering events in high-purity germaniumγ-ray spectrometer

To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.

Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping-particle swarm optimization convolutional neural network

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.

Implementation of a cusp-like for real-time digital pulse shaper in nuclear spectrometry

Pulse shaping,which improves signal-to-noise ratio excellently,has been extensively used in nuclear signal processing.This paper presents a cusp-like pulse-shaping technique developed through the recursive difference equation in time domain.It can be implemented in field programmable gate array hardware system.Another flat-topped cusp-like shaper is developed to optimize the time constant of pulse shaping and reduce the influence of ballistic deficit.The methods of both baseline restoration and pile-up rejection are described.The ^(137)Cs energy spectra measured with the digital cusp-like shaper are 6.6% energy resolution,while those by traditional analog pulse shaper are 7.2% energy resolution,under the same conditions.This technique offers flexibility,too,in adjusting the pulse shaper parameters.

Verification of a self-developed CFD-based multi-physics coupled code MPC-LBE for LBE-cooled reactor

To perform an integral simulation of a pool-type reactor using CFD code,a multi-physics coupled code MPC-LBE for an LBE-cooled reactor was proposed by integrating a point kinetics model and a fuel pin heat transfer model into self-developed CFD code.For code verification,a code-to-code comparison was employed to validate the CFD code.Furthermore,a typical BT transient benchmark on the LBE-cooled XADS reactor was selected for verification in terms of the integral or system performance.Based on the verification results,it was demonstrated that the MPC-LBE coupled code can perform thermal-hydraulics or safety analyses for analysis for processes involved in LBE-cooled pool-type reactors.

Fast nuclide identification based on a sequential Bayesian method

The rapid identification of radioactive substances in public areas is crucial.However,traditional nuclide identification methods only consider information regarding the full energy peaks of the gamma-ray spectrum and require long recording times,which lead to long response times.In this paper,a novel identification method using the event mode sequence(EMS)information of target radionuclides is proposed.The EMS of a target radionuclide and natural background radiation were established as two different probabilistic models and a decision function based on Bayesian inference and sequential testing was constructed.The proposed detection scheme individually processes each photon.When a photon is detected and accepted,the corresponding posterior probability distribution parameters are estimated using Bayesian inference and the decision function is updated.Then,value of the decision function is compared to preset detection thresholds to obtain a detection result.Experiments on different target radionuclides(137Cs and 60Co)were performed.The count rates of the regions of interest(ROI)in the backgrounds between[651,671],[1154,1186],and[1310,1350]keV were 2.35,5.14,and 0.57 CPS,respectively.The experimental results demonstrate that the average detection time was 6.0 s for 60Co(with an activity of 80400 Bq)at a distance of 60 cm from the detector.The average detection time was 7 s for 137Cs(with an activity of 131000 Bq)at a distance of 90 cm from the detector.The results demonstrate that the proposed method can detect radioactive substances with low activity.

Determination of gross α and β activities in Zouma River based on online HPGe gamma measurement system

This paper describes a low-cost and fast method to evaluate gross α and β^(-) radioactivities in natural water based on an online high-purity germanium detector gamma measurement system.The major gamma activities in natural water are provided by natural and artificial radionuclides such as ^(40) K,^(137)Cs,and radionuclides belonging to ^(238)U and ^(232)Th series.The main a emitters related to gamma emissions in natural water are ^(224)Ra(240.98 keV)and ^(226)Ra(186.21 keV),and the β^(-) emitters are ^(40) K(1460.85 keV),^(214)Bi(609.31 keV),^(208)Tl(583.19 keV),and ^(214)Pb(351.93 keV).The formula for gross α and β^(-) activity concentration is based on these radionuclides,and the short half-life decay products are considered in the calculation.The detection efficiency of the device across energy region(0–3 MeV)is obtained through Monte Carlo simulation,and a calibration experiment is conducted to verify the simulation results.Gamma radioactivity is measured continuously for 114 d in Pixian County and Dongfeng Canal located in the Zouma River,Chengdu,Sichuan Province,China.A comparison of the calculation results and monitoring data from the Sichuan Management and Monitoring Center Station of Radioactive Environment indicates that the percentage and absolute error of a activity concentration is lower than 53%and 0.02 Bq/L,respectively,and that of β-activity concentration is lower than 33.2%and 0.016 Bq/L,respectively.The method can rapidly determine gross α and β^(-) activity concentrations in natural water online.

Analog rise-time discriminator for CdZnTe detector

Due to variable time for charge collection,energy resolution of nuclear detectors declines,especially compound semiconductor detectors like cadmium zinc telluride(CdZnTe) detector.To solve this problem,an analog rise-time discriminator based on charge comparison principle is designed.The reference charge signal after attenuation is compared with the deconvoluted and delayed current signal.It is found that the amplitude of delayed current signal is higher than that of the reference charge signal when rise time of the input signal is shorter than the discrimination time,thus generating gating signal and triggering DMCA(digital multi-channel analyzer) to receive the total integral charge signal.When rise time of the input signal is longer than discrimination time,DMCA remains inactivated and the corresponding total integral charge signal is abandoned.Test results show that combination of the designed rise-time discriminator and DMCA can reduce hole tailing of CdZnTe detector significantly.Energy resolution of the system is 0.98%@662 keV,and it is still excellent under high counting rates.

Correction to:Fast nuclide identification based on a sequential Bayesian method

Correction to:NUCL SCI TECH(2021)32:143 https://doi.org/10.1007/s41365-021-00982-z In the original article,the funding information were missing and in this correction the funding information is given below:Funding This work was supported by the National Natural science Foundation of China(No.41774190),the Special-funded program on national key scientific instruments and equipment development(No.2017YFC0602105),and the Department of Science and Technology of Sichuan Province(No.2020JDRC0109).

SOPA-GA-CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet-of-Things

In recent years,deep learning has been applied to a variety of scenarios in Industrial Internet of Things(IIoT),including enhancing the security of IIoT.However,the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the designers.The authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT.A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks(SOPA-GA-CNN)is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks(CNNs)by genetic algorithms(GA)for the intrusion detection issue of IIoT.An efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters,including batch size,learning rate,weight optimiser and weight regularisation,and the architectures,such as the block-based network topology and the parameters of each CNN block.The experimental results on five intrusion detection datasets in IIoT,including secure water treatment,water distribution,Gas Pipeline,Botnet in Internet of Things and Power System Attack Dataset,have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy,precision,recall,F1-score,and the number of parameters of the deep learning models.

Method for rapid warning and activity concentration estimates in online waterγ-spectrometry systems

Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are distributed relatively uniformly and enter into a steady-state diffusion regime in the measurement chamber.To protect residents’health and ensure the safety of the living environment,better timeliness is required for this measurement method.To address this issue,this study established a mathematical model of the online waterγ-spectrometry system so that rapid warning and activity estimates can be obtained for water under non-steady-state(NSS)conditions.In addition,the detection efficiency of the detector for radionuclides during the NSS diffusion process was determined by applying the computational fluid dynamics technique in conjunction with Monte Carlo simulations.On this basis,a method was developed that allowed the online waterγ-spectrometry system to provide rapid warning and activity concentration estimates for radionuclides in water.Subsequent analysis of the NSS-mode measurements of^(40)K radioactive solutions with different activity concentrations determined the optimum warning threshold and measurement time for producing accurate activity concentration estimates for radionuclides.The experimental results show that the proposed NSS measurement method is able to give warning and yield accurate activity concentration estimates for radionuclides 55.42 and 69.42 min after the entry of a 10 Bq/L^(40)K radioactive solution into the measurement chamber,respectively.These times are much shorter than the 90 min required by the conventional measurement method.Furthermore,the NSS measurement method allows the measurement system to give rapid(within approximately 15 min)warning when the activity concentrations of some radionuclides reach their respective limits stipulated in the Guidelines for Drinking-water Quality of the WHO,suggesting that this method considerably enhances the warning capacity of in situ online waterγ-spectrometry systems.

Optimization of a robust collaborative-relay beamforming design for simultaneous wireless information and power transfer

We investigate a collaborative-relay beamforming design for simultaneous wireless information and power transfer. A non-robust beamforming design that assumes availability of perfect channel state information(CSI) in the relay nodes is addressed. In practical scenarios, CSI errors are usually inevitable; therefore, a robust collaborativerelay beamforming design is proposed. By applying the bisection method and the semidefinite relaxation(SDR)technique, the non-convex optimization problems of both non-robust and robust beamforming designs can be solved.Moreover, the solution returned by the SDR technique may not always be rank-one; thus, an iterative sub-gradient method is presented to acquire the rank-one solution. Simulation results show that under an imperfect CSI case, the proposed robust beamforming design can obtain a better performance than the non-robust one.

Adaptive constrained population extremal optimisation-based robust proportional-integral-derivation frequency control method for an islanded microgrid

The expected penetration of renewable sources is driving the islanded microgrid towards uncertainties,which have highly influence the reliability and complexities of frequency control.To alleviate the influence caused by load fluctuations and inherent variability of renewable sources,this article proposes an optimised robust proportional-integralderivation(PID)frequency control method by taking full advantage of a robust control strategy while simultaneously maintaining the basic characteristics of a PID controller.During the process of iterated optimisation,a weighted objective function is used to balance the tracking error performance,robust stability and disturbance attenuation performance.Then,the robust PID frequency(RPIDF)controller is determined by an adaptive constrained population extremal optimisation algorithm based on self-adaptive penalty constraint-handling technique.The proposed control method is examined on a typical islanded microgrid,and the control performance is evaluated under various disturbances and parametric uncertainties.Finally,the simulation results indicate that the fitness value of the proposed method is 1.7872,which is lower than 2.9585 and 3.0887 obtained by two other evolutionary algorithms-based RPIDF controllers.Moreover,the comprehensive simulation results fully demonstrate that the proposed method is superior to other comparison methods in terms of four performance indices on the most considered scenarios.