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

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Nuclear Science and Techniques 2024年第2期35卷 64-84页

作者： Chun-Di Fan Guo-Qiang Zeng Hao-Wen Deng Lei Yan Jian Yang Chuan-Hao Hu Song Qing Yang Hou College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

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.

关键词： High-purity germaniumγ-ray spectrometer Pulse-shape discrimination Compton scattering artificial neural network Minimum detectable activity

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artificial neural network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

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International Journal of Nonferrous Metallurgy 2024年第1期11卷 1-19页

作者： Anukwonke Maxwell Chukwuma Chibueze Ikechukwu Godwills Cynthia C. Nwaeju Osakwe Francis Onyemachi Department of Metallurgical and Materials Engineering Nnamdi Azikiwe University Awka Nigeria Department of Mechanical Engineering Nigeria Martine University Okerenkoko Nigeria

In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).

关键词： Al-5%Mg Alloy Neodymium artificial neural network Fuzzy Logic Average Grain Size and Mechanical Properties

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Prediction of vibration response based on BP artificial neural network for simply supported beam bridge structures

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Shenyang Gongye Daxue Xuebao/Journal of Shenyang University of Technology 2024年第3期46卷 347-352页

作者： Zhao L. Department of Civil and Construction Engineering National Taiwan University of Science and Technology Taipei 10607 Taiwan Third Engineering Co. Ltd. China Railway 19th Bureau Group Liaoning Shenyang 110136 China

To address the problems of large workload, time-consuming calculation and inability to achieve rapid prediction of structural response in finite element models, a structural stress and displacement prediction model based on BP neural network algorithm for simply supported beam bridge was proposed. Taking a simply supported beam bridge as the engineering background, a three-dimensional finite element model of the beam system was established by using Midas/Civil finite element software. Taking into account parameter uncertainty, a comprehensive training and testing sample dataset was obtained by using the finite element model, and an artificial-neural-network-based bridge structure response prediction model was established. The research results show that the artificial neural network prediction model can achieve rapid prediction of structural response on the prerequisite of satisfying prediction accuracy. The maximum error between the measured value of load test and the predicted value by the model is within 12.46%, and the goodness of fit is above 0.9. This prediction model can be combined with the bridge health monitoring system in the later stage to achieve real-time analysis of the structural response of simply supported beam bridges, and can be applied to the structural response evaluation of simply supported beams. © 2024 Shenyang University of Technology. All rights reserved.

关键词： artificial neural network finite element model simply supported beam bridge stress structural evaluation structural response

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artificial neural network based modeling of liquid membranes for separation of dysprosium

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Journal of Rare Earths 2023年第3期41卷 440-445,I0005页

作者： Jawad Iqbal Arjun Tyagi Manish Jain Department of Applied Chemistry Delhi Technological UniversityDelhi110042India

In recent years,the liquid membrane process has been widely investigated to remove rare earth metals.However,transport modeling of this process requires the accurate values of several parameters,which are difficult to measure.Thus,the accurate simulation of this process is a challenging task.In this study,the artificial neural network(ANN)based approach is used to model the liquid membrane process for removing dysprosium.Experimental results from a previous study were used to train the ANN.Initially,the number of neurons in the hidden layer was optimized.The minimum mean squared error between experimental results and model predictions is found with ten neurons.Model predictions were successfully validated with experimental results with correlation factor(R)of 0.9987,which confirms the authenticity of the trained network.Trained ANN was then used to study the effects of different operating parameters on transport rate.The higher volume ratio of membrane solution to feed solution(3-4)with 50-60 min of operation,higher feed pH(5),HCl concentration in stripping solution of 2 mol/L,and moderate concentration of carrier species(0.5 mol/L)with 0.5×10^(-4) mol/L dysprosium initial concentration are found to be optimum values of operating conditions for maximizing the transport rate.

关键词： Rareearth metal separation artificial neural network Dysprosium separation Liquid membranes

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artificial neural network analysis of the day of the week anomaly in cryptocurrencies

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Financial Innovation 2023年第1期9卷 2558-2581页

作者： Nuray Tosunoğlu Hilal Abacı Gizem Ateş Neslihan SaygılıAkkaya Faculty of Economics and Administrative Sciences Ankara HacıBayram Veli UniversityAnkaraTurkey Faculty of Economics and Administrative Sciences ÇankırıKaratekin UniversityÇankırıTurkey Faculty of Economics and Administrative Sciences İnönüUniversityMalatyaTurkey Institute of Graduate Studies Ankara HacıBayram Veli UniversityAnkaraTurkey

Anomalies,which are incompatible with the efficient market hypothesis and mean a deviation from normality,have attracted the attention of both financial investors and researchers.A salient research topic is the existence of anomalies in cryptocurrencies,which have a different financial structure from that of traditional financial markets.This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market,which is hard to predict.It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods.An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies.On October 6,2021,Bitcoin(BTC),Ethereum(ETH),and Cardano(ADA),which are the top three cryptocurrencies in terms of market value,were selected for this study.The data for the analysis,consisting of the daily closing prices for BTC,ETH,and ADA,were obtained from the *** website from January 1,2018 to May 31,2022.The effectiveness of the established models was tested with mean squared error,root mean squared error,mean absolute error,and Theil’s U1,and R2 OOS was used for out-of-sample.The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models.When the models created with feedforward artificial neural networks are examined,the existence of the day-of-the-week anomaly is established for BTC,but no day-of-the-week anomaly for ETH and ADA was found.

关键词： Cryptocurrency Bitcoin Ethereum Cardano Day-of-the-week anomaly artificial neural network

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artificial neural network-based one-equation model for simulation of laminar-turbulent transitional flow

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Theoretical & Applied Mechanics Letters 2023年第1期13卷 50-57页

作者： Lei Wu Bing Cui Zuoli Xiao State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing 100871China Inspur Electronic Information Industry Co. LtdBeijing 100085China HEDPS and Center for Applied Physics and Technology College of EngineeringPeking UniversityBeijing 100871China Nanchang Innovation Institute Peking UniversityNanchang 330008China

A mapping function between the Reynolds-averaged Navier-Stokes mean flow variables and transition intermittency factor is constructed by fully connected artificial neural network(ANN),which replaces the governing equation of the intermittency factor in transition-predictive Spalart-Allmaras(SA)-γmodel.By taking SA-γmodel as the benchmark,the present ANN model is trained at two airfoils with various angles of attack,Mach numbers and Reynolds numbers,and tested with unseen airfoils in different flow states.The a posteriori tests manifest that the mean pressure coefficient,skin friction coefficient,size of laminar separation bubble,mean streamwise velocity,Reynolds shear stress and lift/drag/moment coefficient from the present two-way coupling ANN model almost coincide with those from the benchmark SA-γmodel.Furthermore,the ANN model proves to exhibit a higher calculation efficiency and better convergence quality than traditional SA-γmodel.

关键词： Transition Turbulence Eddy-viscosity model artificial neural network Intermittency factor

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artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

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International Journal of Agricultural and Biological Engineering 2023年第2期16卷 38-47页

作者： Apsornrat Numsong Jetsada Posom Somchai Chuan-Udom Department of Agricultural Engineering Faculty of EngineeringKhon Kaen UniversityThailand Applied Engineering for Important Crops of the North East Research Group Khon Kaen UniversityThailand

This research proposes an artificial neural network(ANN)-based repair and maintenance(R&M)cost estimation model for agricultural machinery.The proposed ANN model can achieve high estimation accuracy with small data requirement.In the study,the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters.The model inputs are geographical regions,harvest area,and curve fitting coefficients related to historical cost data;and the ANN output is the estimated R&M cost.Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm.The R&M costs are estimated using the ANN-based model,and results are compared with those of conventional mathematical estimation model.The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%,indicating the proposed ANN model’s high predictive accuracy.The proposed ANN-based model is useful for setting the service rates of agricultural machinery,given the significance of R&M cost in profitability.The novelty of this research lies in the use of curve-fitting coefficients in the ANN-based estimation model to improve estimation accuracy.Besides,the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility.Moreover,with minor modifications,the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin.

关键词： repair and maintenance cost estimation model artificial neural network curve fitting coefficients combine harvesters

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artificial-neural-network-based storage method for three-dimensional temperature field data during friction stir welding

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China Welding 2022年第3期31卷 1-7页

作者：韩策张乾史清宇唐天祥刘新张弓陈高强 Department of Mechanical Engineering Tsinghua UniversityBeijing 100084China Informatization and Industrialization Integration Research Institute China Academy of Information and Communications TechnologyBeijing 100191China

In this paper,a new storage method for the three-dimensional temperature field data based on artificial neural network(ANN)was proposed.A multilayer perceptron that takes the coordinate(x,y,z)as inputs and temperature T as output,is used to fit the three-dimension-al welding temperature field.Effect of number of ANN layers and number of neurons on the fitting errors is investigated.It is found that the errors decrease with the number of hidden layers and neural numbers per layers generally.When the number of hidden layers increases from 1 to 6,the maximum temperature error decreases from 74.74℃to less than 2℃.The three-dimensional temperature field data is obtained by finite element simulation,and the experimental verification is completed by comparing the simulation peak temperatures with the measured results.As an example,an ANN with 4 hidden layers and 12 neurons in each layer were applied to test the performance of the proposed method in storage of the three-dimensional temperature field data during friction stir welding.It is found that the average error between the temperature data stored in ANN and the original simulation data that stored point-by-point is 0.517℃,and the error on the maximum temper-ature is 0.193℃,while the occupied disk space is only 0.27%of that is required in the conventional point-by-point storage.

关键词： friction stir welding temperature field artificial neural network numerical simulation

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An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network

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Chinese Journal of Aeronautics 2022年第4期35卷 102-117页

作者： Peng LU Nianhua WANG Xinghua CHANG Laiping ZHANG Yadong WU School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China School of Intelligent Manufacturing Engineering Chongqing University of Arts and ScienceChongqing 402160China Stake Key Laboratory of Aerodynamics China Aerodynamics Research and Development CenterMianyang 621000China Unmanned Systems Research Center National Innovation Institute of Defense TechnologyBeijing 100071China School of Computer Science and Technology Sichuan University of Science&EngineeringYibin 644005China

Based on the author’s previous research, a novel hybrid grid generation technique is developed by introducing an artificial neural network(ANN) approach for realistic viscous flow simulations. An initial hybrid grid over a typical geometry with anisotropic quadrilaterals in the boundary layer and isotropic triangles in the off-body region is generated by the classical mesh generation method to train two ANNs on how to predict the advancing direction of the new point and to control the grid size. After inputting the initial discretized fronts, the ANN-based Advancing Layer Method(ALM) is adopted to generate the anisotropic quadrilaterals in boundary layers. When the high aspect ratio of the anisotropic grid reaches a specified value, the ANN-based Advancing Front Method(AFM) is adopted to generate isotropic triangles in the off-body computational domain.The initial isotropic triangles are smoothed to further improve the grid quality. Three typical cases are tested and compared with experimental data to validate the effectiveness of grids generated by the ANN-based hybrid grid generation method. The experimental results show that the two ANNs can predict the advancing direction and the grid size very well, and improve the adaptability of the isotropic/anisotropic hybrid grid generation for viscous flow simulations.

关键词： Advancing front method Advancing layer method Anisotropic quadrilateral grid generation artificial neural network Isotropic triangular grid generation Machine learning

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A two-dimensional MoS_(2) array based on artificial neural network learning for high-quality imaging

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Nano Research 2023年第7期16卷 10139-10147页

作者： Long Chen Siyuan Chen Jinchao Wu Luhua Chen Shuai Yang Jian Chu Chengming Jiang Sheng Bi Jinhui Song School of Mechanical Engineering Dalian University of TechnologyDalian 116024China School of Computer Science Southwest Petroleum UniversityChengdu 610500China

As the basis of machine vision,the biomimetic image sensing devices are the eyes of artificial intelligence.In recent years,with the development of two-dimensional(2D)materials,many new optoelectronic devices are developed for their outstanding performance.However,there are still little sensing arrays based on 2D materials with high imaging quality,due to the poor uniformity of pixels caused by material defects and fabrication technique.Here,we propose a 2D MoS_(2)sensing array based on artificial neural network(ANN)learning.By equipping the MoS_(2)sensing array with a“brain”(ANN),the imaging quality can be effectively improved.In the test,the relative standard deviation(RSD)between pixels decreased from about 34.3%to 6.2%and 5.49%after adjustment by the back propagation(BP)and Elman neural networks,respectively.The peak signal to noise ratio(PSNR)and structural similarity(SSIM)of the image are improved by about 2.5 times,which realizes the re-recognition of the distorted image.This provides a feasible approach for the application of 2D sensing array by integrating ANN to achieve high quality imaging.

关键词： two-dimensional MoS_(2) sensing array artificial neural network individual difference imaging quality

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