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For measuring radioactive waste drum, the segmented gamma scanning (SGS) is a quick and effective procedure. The accuracy of reconstructed radioactivity in the drum is directly impacted by SGS efficiency matrix. To overcome the challenges of restricted experimental source and excessive workload in existing SGS efficiency calibration methods, a new SGS efficiency calibration method is presented. The point source efficiency function calculates the non-attenuation efficiency of each voxel in the calibrated segment. The voxel attenuation efficiency is corrected by the linear attenuation coefficient for segments’ absorption. The attenuation efficiency for the segment is calculated as the weighted average of all voxel attenuation efficiencies in a segment. The waste drum sample is filled with a random mixture of 0.33 g·cm−3aluminium silicate fibre, 0.64 g·cm−3wood fibre, 1.84 g·cm−3polyvinyl chloride plastic and a point source 60Co of 1.244 × 105Bq. The SGS system and the waste drum sample are used to complete SGS experimental measurement, efficiency calibration and activity reconstruction. Result shows that the relative deviations of reconstructed activity are − 15.39–30.98% for the extreme case where just a point source 60Co is placed at 16 positions in the drum with different heights and eccentric distances. This relative deviation rang of reconstructed activity meets the needs of most users. Compared with other methods which have complicated computational processes, the main benefit of this work is to present a low-performance and easy-to-implement additional alternative to commercial software or existing approaches.

The testing of radioactive waste drums is a fundamental aspect of nuclear waste disposal. To meet the need for rapid classification and measurement of radioactive wastes, we proposed a detection method based on array scintillation detectors to realize chromatographic gamma-scanning of casks of nuclear wastes. This method has been successfully developed and applied in the form of an array NaI(Tl) detector radioactive waste cask measurement system. The measurement system was divided into four main sections: (1) the radioactive source, (2) the mechanical and control system, (3) the detector system, and (4) the data processing system. The detector system comprises seven NaI(Tl) detectors, taking into account the detector layout, detection efficiency, and shielded room (to reduce the background of the environment). The results of the performance tests on the measurement system indicated that the detection limit was 6191.75 s−1, the energy resolution was 7.698% at 661.7 keV (137Cs), and the minimum detectable activity was 0.201 µCi for 137Cs and 0.143 µCi for 60Co. Finally, transmission measurements and image reconstruction of the three different media in the drum using a 137Cs radioactive source could effectively reconstruct the spatial location distribution of the media in the drum.

This work presents a Segmented Gamma Scanning (SGS) efficiency calibration method based on Monte Carlo simulation and function model, to calculate the efficiency matrix and reconstruct the radioactivity of the radioactive waste drum. The middle- or low-energy gamma rays emitted by the transmission source 152Eu, cannot transmit the high-density waste drum in the Segmented Gamma Scanning of the radioactive waste drum. Therefore, the accurate attenuation correction cannot be achieved through efficiency calibration, for the middle- or low-energy gamma rays emitted from the drum, which affects the accuracy of the reconstructed radioactivity. High-energy gamma rays have been employed to transmit the waste drum to determine the density of segment, and then the efficiency of segment is determined from the density of segment and the energy of gamma rays emitted from the waste drum. When the point source 137Cs is distributed in segments with varying densities in the waste drum sample, the relative deviations of reconstructed radioactivity fall in − 90.69% ~ 22.33%. The method is convenient and effective for the SGS efficiency calibration while measuring the high-density waste drum, and the relative deviations of reconstructed activity are acceptable on this basis. Compared with alternative methods, the process outlined in this method is more simple, operational, and practical. Further, it provides the user with a practical method in SGS efficiency calibration and the activity reconstruction of the waste drum.

Neutron time-of-flight (ToF) measurement obtains the energy and distribution of neutrons by measuring their ToF at a fixed distance, in which accurately obtaining the flight time of neutrons is critical. However, analog integration + digital peak-seeking faces the problem of low accuracy, and waveform digitizer (WFD) encounters the issues of high sampling rates and massive transmitted data when acquiring neutron ToF signals. Neutron ToF signals have natural sparsity in the time domain, which meets the prerequisite of compressed sampling (CS) theory, and CS has the ability to undersample and reconstruct neutron ToF signals with a sampling rate lower than Nyquist’s theorem to reduce the data volume. Therefore, a new CS framework combined with discrete wavelet transform (DWT), Bernoulli measurement matrix, and sparsity adaptive matching pursuit (SAMP) reconstruction has been proposed in this work. The DWT was adopted for the optimal linear combination of atoms, the Bernoulli random matrix was applied to derive the low-dimensional observed signal from the high-dimensional linear projection, and the greedy SAMP algorithm was used to reconstruct the neutron ToF signal. The performance metrics of the percent residual difference (PRD) and correlation coefficient were employed to quantify the capability of this CS framework. The experimental results presented that the PRD of 6.7348%, the correlation coefficient of 0.9977, and the reconstruction time of 0.1108 s when the sampling rate was 20% of 2.5 Gs/s for the neutron ToF based on an electron-beam-driven photoneutron source. It indicated that the proposed CS framework can be used for the accurate reconstruction of neutron ToF signals, which alleviates the difficulty of storing and processing massive signals.

Improving the quality of microseismic recordings is a critical step in the microseismic data processing. We introduce a wavelet-weighted online dictionary learning (WWODL) denoising strategy for noisy microseismic recordings. We develop an adaptive parameters estimation approach for tunable $Q$ -factor wavelet transform (TQWT), which provides accurate periodic and nonstationary subband information from microseismic data for online dictionary learning (ODL). A sliding time window is employed to divide the obtained subbands into a series of patches of equal length, which are then assembled into a matrix and fed into the ODL. The subband kurtosis information is weighted to the constraint function of the ODL, further enhancing the sparse coding ability for each subband. Shortening the oscillation duration, an improved ODL is developed with a faster convergence speed in calculating sparse coefficients. Our results confirm that the WWODL can suppress high-frequency, low-frequency, and shared-bandwidth noises and has a minimal impact on the first arrival. The time consumption and the signal-to-noise ratio (SNR) of the WWODL are on average 1/8.675 and 56.82% higher than ODL, respectively.

Distinguishing useful microseismic signals is a critical step in microseismic monitoring. Here, we present the time series contrastive clustering (TSCC) method, an end-to-end unsupervised model for clustering microseismic signals that uses a contrastive learning network and a centroidal-based clustering model. The TSCC framework consists of two successive phases: pretraining and fine-tuning. In the pretraining phase, two random cropping augmentations are used to transform the time series microseismic data into two distinct but correlated views. Then, the multiscale temporal and instance contrasting learning are used to discriminate between negative and positive views, thus motivating the encoder to capture microseismic signal contextual information from multiple perspectives and generate distinct representations from unlabeled data. During the fine-tuning phase, the encoder weights are iteratively fine-tuned by simultaneously performing contrast learning and clustering. The corresponding loss is a weighted combination of the contrastive and clustering loss functions, which induces the encoder to learn representations that improve the clustering performance. The test results demonstrate that the proposed method can achieve better clustering accuracy (ACC) than popular clustering methods, including $k$ -means, deep embedding clustering (DEC), unsupervised clustering with deep convolutional autoencoders (DCAs), and deep clustering with self-supervision (DCSS). Moreover, the TSCC model can produce results comparable to supervised deep learning approaches while requiring no labeled data, manual feature extraction, or large training datasets. In practice, the TSCC model has a clustering ACC of 98.07% and a normalized mutual information (NMI) of 86.26%.

AbstractThe alteration of the morphology of bentonite, a key barrier to isolating high-level radioactive waste in deep geological repositories under long-term interaction with groundwater, was studied. Bentonite colloids were prepared from Gaomiaozi bentonite, and its various properties were analyzed by various characterization methods. The effects of various factors on the adsorption of Cs(I) and Co(II) on bentonite colloids were investigated by batch static adsorption experiments. The results showed that the colloids were mainly composed of montmorillonite and were lamellar in shape, with a zeta potential of −enton mV and an average size of approximately 209.10 nm. The adsorption of Cs(I) and Co(II) by the colloids was a rapid process, and 31.78 and 88.24 mg/g, respectively, were adsorbed at equilibrium. Pseudo-second-order kinetic fitting showed that chemisorption plays a dominant role and acid-base interactions affect adsorption by influencing the stability of colloids and the chemical form of simulated nuclides. This work can be helpful for evaluating the safety of waste repositories.

Boron-lined gaseous neutron detectors are being widely used in neutron detection to replace 3He proportional counters, and the boron film’s parameters comprise the key factors influencing the performance of such detectors. However, the method of characterizing boron film is relatively simple at present. In this study, boron films stuck to ultrathin glass substrate with different mass proportions of epoxy to natural boron (MPENBs) were prepared. A variety of characterization methods, including scanning electron microscopy, energy-dispersive X-ray spectroscopy, white-light interferometry, and multiple tape tests, were used to test the boron films simultaneously, and the test results are discussed herein. Moreover, neutron imaging was conducted to analyze the uniformity of boron-10 atoms. These characterization results demonstrate that the optimized MPENB formulation is 0.16 with the boron atomic ratio of chemical elements (ARCE) at approximately 68.8% and surface roughness Sa = 1.457 μm and that the structure of boron film is uniform and fluffy, contributing to improving the boron-lined method.

Aimed at locating the temperature abnormal event of nuclear waste drums in a nuclear waste temporary storage repository by a Raman-based distributed temperature sensor, a principal component analysis (PCA)-based method for application is proposed. The effectiveness of the proposed method is verified in the physical simulation device of the nuclear waste drums. First, some samples of the temperature abnormal event with known location are taken as the reference samples, and their features are extracted by PCA. Then, the features of the test sample data to be located are also extracted by PCA. The Euclidean distance is used to measure the similarity between the features of the test sample and the feature of each reference sample. Finally, we find the reference sample that is most similar to a test sample, the location of which is considered the location of the temperature anomaly event for the test sample. The experimental results show that the proposed method can accurately locate the temperature abnormal event of the nuclear waste drums, and the accuracy rate can reach 96%. The method that is proposed in this paper can reliably locate the temperature abnormal event generated by the nuclear waste temporary storage repository induced by external factors such as landslides or earthquakes, and provide technical support for nuclear safety.

Radiation portal monitors (RPMs) are globally employed in ports to decrease the illicit trafficking of nuclear materials. Most RPMs are generally considered as threshold devices because they utilize large plastic scintillators, which have limited energy resolution and no position sensitivity, to count the gamma-rays and neutrons emitted by nuclear materials. Hence, we designed an RPM detector system with NaI(Tl) detectors inserted into the plastic scintillators to realize gamma-ray source identification and positioning. In this work, a 2D gamma-ray source positioning algorithm based on the attenuation law and solidangle variation was studied with the simulation results, and the variation of the positioning deviations with the real positions was spline interpolated to calibrate the calculated position. The testing experiment showed a small root mean square error (RMSE) for both x(0.86 cm) and y(1.52 cm) after calibration.

Attitude determination is a significant aspect of the navigation technology. The determination of the attitudes based on accelerometer and magnetometer fusion is basically an applied mathematical problem. In this article, we obtained the proposed quaternionic symbolic solution for accelerometer and magnetometer fusion which is quite different from existing methods. The proposed symbolic solution has the advantages of high accuracy and consumes much less time. Besides, the normalization problem of input data was discussed. Biquaternions ware introduced to describe the existence of the complex quaternion. Several experiments were carried out to verify the proposed symbolic solution.

To improve the visible light absorption and photocatalytic activity of titanium dioxide nanotube arrays (TONTAs), ZnFe2O4(ZFO) nanocrystals were perfused into pristine TONTA pipelines using a novel bias voltage-assisted perfusion method. ZFO nanocrystals were well anchored on the inner walls of the pristine TONTAs when the ZFO suspensions (0.025 mg mL−1) were kept under a 60 V bias voltage for 1 h. After annealing at 750 °C for 2 h, the heterostructured ZFO/Fe2TiO5(FTO)/TiO2composite nanotube arrays were successfully obtained. Furthermore, Fe3+was reduced to Fe2+when solid solution reactions occurred at the interface of ZFO and the pristine TONTAs. Introducing ZFO significantly enhanced the visible light absorption of the ZFO/FTO/TONTAs relative to that of the annealed TONTAs. The coexistence of type I and staggered type II band alignment in the ZFO/FTO/TONTAs facilitated the separation of photogenerated electrons and holes, thereby improving the efficiency of the ZFO/FTO/TONTAs for photocatalytic degradation of methylene blue when irradiated with simulated sunlight.

To improve the stacking effect in seismic signal processing, we use a weighted stack algorithm that considers the differences in signal-to-noise ratios (SNR) between seismic traces. The statistical properties of the weights in this weighted stack algorithm, such as variance and standard deviation, are the same as those of the SNR in each trace. We propose using a windowing technique to expand the weight in each seismic trace in “time,” resulting in a two-dimensional weight that varies with both “space” and “time.” A simulation test is conducted under the same conditions. The result shows that the improved two-dimensional weight is more effective. Then, we apply the two-dimensional weight to the multiple detection technique and the multiple coverage technique to determine the optimal condition for the use of such a weighted stack; we find that it is most effective when there are differences in the SNRs or regular noise interference in each seismic trace. It is thus clear that our weighted stack algorithm is more suitable for the multiple coverage techniques.

A new statistical fitting approach, named Statistical Distribution-Based Analytic (SDA) method, is proposed to fit single Gaussian-shaped Kαand KβX-ray peaks recorded by Si(PIN) and silicon drift detector (SDD). In this method, we use the discrete distribution theory to calculate standard deviation of energy resolution σ. The calibration of σand energy (E) for two detectors between the energy ranges of 4.5–26 keV are also completed by measuring characteristic X-ray spectra of nineteen types of pure elements. With the spectrum fraction (SF) parameter proposed in this paper, the SDA method can be used to resolve overlapping peaks. In measured spectra, the Gaussian part of X-ray peaks can be fitted by a Gaussian function with two parameters, σand SF. This new fitting approach is simpler than traditional methods and it achieves relatively good results when fitting the complex X-ray spectra of national standard alloy samples detected by Si(PIN) and SDD detectors. The χr2values are obtained for each spectrum to assess fitting results, and the SDA fitting method gives a preferable fit for the SDD detector.

Abstract: In this paper we investigate the dynamics of totally asymmetric exclusion process (TASEPs) on a one-dimensional lattice with long-range hopping and parallel update. The model is inspired by information traffic over networks. Compared to other TASEP models, particles in our model will firstly try to hop to the last site with a probability q. The probability q may reflect network reliability or available bandwidth. A mean-field approach is developed to deal with the long-range hopping. Theoretical calculations for stationary particle currents, density profiles and a phase diagram have been obtained. There are two possible stationary phases (LD and HD) in the system, corresponding to free flow and jammed flow, respectively. Interestingly, bulk density in the LD phase tends to zero, while in the HD phase it is the same as that of the normal TASEP. The LD and HD phase regions are obtained numerically. The steady-state currents and density profiles obtained from computer simulations show a very good agreement with theoretical predictions. The model and theoretical results may provide a better understanding of information traffic flow over networks. [Copyright &y& Elsevier]

In the process of neutron detection, due to the inelastic scattering and slow neutron capture, a neutron-gamma hybrid radiation field is formed, which increases the complexity of neutron detection. Organic scintillators are widely used in fast neutron detection because of their high scintillation efficiency, short decay time, and good detection efficiency. Pulse shape discrimination (PSD) is a critical technology to discriminate neutrons and gamma rays in organic scintillators according to the decay time of different particles. Traditional PSD methods include time-domain methods and frequency-domain methods. In recent years, various machine learning (ML) models, such as artificial neural network (ANN), support vector machine (SVM), and gaussian mixture model (GMM) have also been applied to neutron-gamma discrimination. In this work, we briefly analyze the luminescence mechanism of organic scintillators, the PSD principle, and some critical physical characteristics of organic scintillators. Later, we review organic scintillators and neutron-gamma discrimination methods by classification and describe various evaluation indexes of neutron-gamma discrimination methods’ performance in organic scintillators. Finally, the development trend of organic scintillators and PSD methods is prospected.

Traditional alpha contamination monitors have a low detection efficiency, and can miss contamination on less accessible surfaces, uneven surfaces and piping inner surfaces etc. New measuring equipment with alarming system designed to be used to solve these problems is discussed in this paper. This system uses Long-Range Alpha Detection (LRAD) technology, which monitors alpha contamination by measuring the ionization in ambient air formed by alpha particles. The whole monitor system contains an iron chamber detector, a high quality MCU system, and adopts Σ-Δ signal processing technology. It is designed to be used primarily in the field and in the laboratory measurements. The monitor is battery-powered and can operate continuously for up to ten hours. The test result indicates that the available detect length L is more than 2m, the relative detect efficiency η is better than 40%. This kind of alpha surface contamination monitor has been well used at some radiation site and has been proven to meet the needs.

The tritium surface contamination monitor based on gas-flow proportional counter is introduced in this paper. The detector is very advantageous for detecting low energy beta particles. Its detection efficiency is 84%, higher than the similar type monitors. Besides, Plateau Length is longer than 300V, Plateau slope is less than 1%/100V, Lowest Detection Limit is 0.221 Bq/cm2, the relative expansion uncertainty is 0.56%/h. The capabilities are superiority and steadiness. As a result, it can be well used to monitor tritium contamination in working sites or other places, with great value for usage and wide application fields.

With the rapid increase of port throughout, the number of vessels increases and the vessel traffic form become more complex. Therefore, how to realize the maximum utilization of limited navigation resources becomes one of the hottest issues. The consensus and coordination control of multi-agent systems could be adopted in the vessel traffic systems to improve the navigation efficiency and ensure safety in the water traffic. This paper reviews related work of the application of multi-agent system consensus control in the vessel traffic. In-depth analysis and discussion is made on swarming, distributed coordination control, consensus with external disturbances, and formation problems in the water traffic system. And also some challenging problems and prospects are presented.

With the wide application of lithium batteries (LIBs) in electrified transportation and smart grids, especially in the pure electric vehicle industry, the accurate health maintenance monitoring of LIBs has emerged as critical to safe battery operation. Although many data-driven methods with state of health (SOH) estimation for LIBs have been proposed, the problems of industrial application and computational cost still need to be improved further. In contrast, this article carried out a low-complexity SOH estimation method for LIBs. Specifically, the seven health indicators are extracted firstly to characterize battery health status from voltage, current, temperature, and other data that can be obtained online. Then, the optimized Gaussian process regression (GPR) algorithm is proposed with proper computational cost. Ultimately, by combining a multi-indirect features extraction and optimized GPR algorithm, the online SOH estimation for LIBs was established and verified with NASA experiment data. The experimental results show that the maximum MAPE of SOH estimation from the proposed method is 1.4496 and the minimum MAPE only reaches 0.5635. More importantly, the optimized GPR for SOH estimation can achieve a maximum 65.37% improvement under multiple evaluation criteria compared to traditional GPR. The method proposed in this article is helpful for realizing online SOH estimation in battery management systems.