The study of nuclear mass is very important,and the neural network(NN)approach can be used to improve the prediction of nuclear mass for various models.Considering the number of valence nucleons of protons and neutron...The study of nuclear mass is very important,and the neural network(NN)approach can be used to improve the prediction of nuclear mass for various models.Considering the number of valence nucleons of protons and neutrons separately in the input quantity of the NN model,the root-mean-square deviation of binding energy between data from AME2016 and liquid drop model calculations for 2314 nuclei was reduced from 2.385 MeV to 0.203 MeV.In addition,some defects in the Weizsacker-Skyrme(WS)-type model were repaired,which well reproduced the two-neutron separation energy of the nucleus synthesized recently by RIKEN RI Beam Factory[Phys.Rev.Lett.125,(2020)122501].The masses of some of the new nuclei appearing in the latest atomic mass evaluation(AME2020)are also well reproduced.However,the results of neural network methods for predicting the description of regions far from known atomic nuclei need to be further improved.This study shows that such a statistical model can be a tool for systematic searching of nuclei beyond existing experimental data.展开更多
The Bayesian neural network approach has been employed to improve the nuclear magnetic moment predictions of odd-A nuclei.The Schmidt magnetic moment obtained from the extreme single-particle shell model makes large r...The Bayesian neural network approach has been employed to improve the nuclear magnetic moment predictions of odd-A nuclei.The Schmidt magnetic moment obtained from the extreme single-particle shell model makes large root-mean-square(rms)deviations from data,i.e.,0.949μN and 1.272μN for odd-neutron nuclei and odd-proton nuclei,respectively.By including the dependence of the nuclear spin and Schmidt magnetic moment,the machine-learning approach precisely describes the magnetic moments of odd-A uclei with rms deviations of 0.036μN for odd-neutron nuclei and 0.061μN for odd-proton nuclei.Furthermore,the evolution of magnetic moments along isotopic chains,including the staggering and sudden jump trend,which are difficult to describe using nuclear models,have been well reproduced by the Bayesian neural network(BNN)approach.The magnetic moments of doubly closed-shell±1 nuclei,for example,isoscalar and isovector magnetic moments,have been well studied and compared with the corresponding non-relativistic and relativistic calculations.展开更多
A detailed study of the capabilities of artificial neural networks to diagnoses cracks in massive concrete structures is presented. This paper includes the components of the expert system such as design thought, basic...A detailed study of the capabilities of artificial neural networks to diagnoses cracks in massive concrete structures is presented. This paper includes the components of the expert system such as design thought, basic structure, building of knowledge base and the implementation of neural network applied model. The realizing method of neural network based clustering algorithm in the knowledge base and self study is analyzed emphatically and stimulated by means of the computer. From the above study, some important conclusions have been drawn and some new viewpoints have been suggested.展开更多
The compressive strength of concrete is one of most important mechanical parameters in the performance assessment of existing reinforced concrete structures.According to various international codes,core samples are dr...The compressive strength of concrete is one of most important mechanical parameters in the performance assessment of existing reinforced concrete structures.According to various international codes,core samples are drilled and tested to obtain the concrete compressive strengths.Non-destructive testing is an important alternative when destructive testing is not feasible without damaging the structure.The commonly used non-destructive testing(NDT)methods to estimate the in-situ values include the Rebound hammer test and the Ultrasonic Pulse Velocity test.The poor reliability of these tests due to different aspects could be partially contrasted by using both methods together,as proposed.in the SonReb method.There are three techniques that are commonly used to predict the compressive strength of concrete based on the SonReb measurements:computational modeling,artificial intelligence,and parametric multi-variable regression models.In a previous study the accuracy of the correlation formulas deduced from the last technique has been investigated in comparison with the effective compressive strengths based on destructive test results on core drilled in adjacent locations.The aim of this study is to verify the accuracy of Artificial Neural Approach comparing the estimated compressive strengths based on NDT measured parameters with the same effective compressive strengths.The comparisons show the best performance of ANN approach.展开更多
In this study, a successful linear matrix inequality approach is used to analyse a non-parameter perturbation of multi-delay Hopfield neural network by constructing an appropriate Lyapunov-Krasovskii functional. This ...In this study, a successful linear matrix inequality approach is used to analyse a non-parameter perturbation of multi-delay Hopfield neural network by constructing an appropriate Lyapunov-Krasovskii functional. This paper presents the comprehensive discussion of the approach and also extensive applications.展开更多
A multimodal biometric system is applied to recognize individuals for authentication using neural networks. In this paper multimodal biometric algorithm is designed by integrating iris, finger vein, palm print and fac...A multimodal biometric system is applied to recognize individuals for authentication using neural networks. In this paper multimodal biometric algorithm is designed by integrating iris, finger vein, palm print and face biometric traits. Normalized score level fusion approach is applied and optimized, encoded for matching decision. It is a multilevel wavelet, phase based fusion algorithm. This robust multimodal biometric algorithm increases the security level, accuracy, reduces memory size and equal error rate and eliminates unimodal biometric algorithm vulnerabilities.展开更多
Nuclear charge density distribution plays an important role in both nuclear and atomic physics,for which the two-parameter Fermi(2pF)model has been widely applied as one of the most frequently used models.Currently,th...Nuclear charge density distribution plays an important role in both nuclear and atomic physics,for which the two-parameter Fermi(2pF)model has been widely applied as one of the most frequently used models.Currently,the feedforward neural network has been employed to study the available 2pF model parameters for 86 nuclei,and the accuracy and precision of the parameter-learning effect are improved by introducing A^(1∕3)into the input parameter of the neural network.Furthermore,the average result of multiple predictions is more reliable than the best result of a single prediction and there is no significant difference between the average result of the density and parameter values for the average charge density distribution.In addition,the 2pF parameters of 284(near)stable nuclei are predicted in this study,which provides a reference for the experiment.展开更多
Recently,Artificial Neural Networks(ANNs)have been used for various scientific and engineering applications essentially because they allow the modeling of a process,which starts from the database containing the variab...Recently,Artificial Neural Networks(ANNs)have been used for various scientific and engineering applications essentially because they allow the modeling of a process,which starts from the database containing the variables that describe that particular process.They have already been applied to the study of landslides in particular,with reference to the indirect determination of the triggering展开更多
In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-o...In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.展开更多
In order to effectively cope with exponent increase of the complexity faced to the rock mechanics analysis problems and the large incompatibility existing between the information level required to model the rock mass ...In order to effectively cope with exponent increase of the complexity faced to the rock mechanics analysis problems and the large incompatibility existing between the information level required to model the rock mass and engineering and our obtainable information level at hand,the integrated approaches with intelligent characters are proposed. Many previous standard methods,such as precedent type analysis,rock classification,analytic method stress-based,basic numerical methods (BEM,FEM,DEM,hybrid),and their extended numerical methods (fully coupled) to be developed,can be selected respectively or integrated accordingly. It is alternative to develop basic/fully integrated system,and internet-based approaches. These novel methods can also be selected or integrated each other or with the standard methods to perform rock mechanics analysis. Some key techniques to develop these alternative methods are discussed. It may focus in future on developing fully integrated systems and internet-based approaches. Developing an environmental,virtual facility/space shall be firstly done for this collaborative research on internet.展开更多
The multiplicity distribution (P(nch)) of charged particles produced in a high energy collision is a key quantity to understand the mechanism of multiparticle production. This paper describes the novel application of ...The multiplicity distribution (P(nch)) of charged particles produced in a high energy collision is a key quantity to understand the mechanism of multiparticle production. This paper describes the novel application of an artificial neural network (ANN) black-box modeling approach based on the cascade correlation (CC) algorithm formulated to calculate and predict multiplicity distribution of proton-proton (antiproton) (PP and PP ) inelastic interactions full phase space at a wide range of center-mass of energy . In addition, the formulated cascade correlation neural network (CCNN) model is used to empirically calculate the average multiplicity distribution nch> as a function of . The CCNN model was designed based on available experimental data for = 30.4 GeV, 44.5 GeV, 52.6 GeV, 62.2 GeV, 200 GeV, 300 GeV, 540 GeV, 900 GeV, 1000 GeV, 1800 GeV, and 7 TeV. Our obtained empirical results for P(nch), as well as nch> for (PP and PP) collisions are compared with the corresponding theoretical ones which obtained from other models. This comparison shows a good agreement with the available experimental data (up to 7 TeV) and other theoretical ones. At full large hadron collider (LHC) energy ( = 14 TeV) we have predicted P(nch) and nch> which also, show a good agreement with different theoretical models.展开更多
基金Supported by National Natural Science Foundation of China(12175170,11675066)the Fundamental Research Funds for the Central Universities(lzujbky-2017-ot04)Feitian Scholar Project of Gansu province。
文摘The study of nuclear mass is very important,and the neural network(NN)approach can be used to improve the prediction of nuclear mass for various models.Considering the number of valence nucleons of protons and neutrons separately in the input quantity of the NN model,the root-mean-square deviation of binding energy between data from AME2016 and liquid drop model calculations for 2314 nuclei was reduced from 2.385 MeV to 0.203 MeV.In addition,some defects in the Weizsacker-Skyrme(WS)-type model were repaired,which well reproduced the two-neutron separation energy of the nucleus synthesized recently by RIKEN RI Beam Factory[Phys.Rev.Lett.125,(2020)122501].The masses of some of the new nuclei appearing in the latest atomic mass evaluation(AME2020)are also well reproduced.However,the results of neural network methods for predicting the description of regions far from known atomic nuclei need to be further improved.This study shows that such a statistical model can be a tool for systematic searching of nuclei beyond existing experimental data.
基金Supported by the National Natural Science Foundation of China(11675063,11875070,11205068)the Open fund for Discipline Construction,Institute of Physical Science and Information Technology,Anhui University。
文摘The Bayesian neural network approach has been employed to improve the nuclear magnetic moment predictions of odd-A nuclei.The Schmidt magnetic moment obtained from the extreme single-particle shell model makes large root-mean-square(rms)deviations from data,i.e.,0.949μN and 1.272μN for odd-neutron nuclei and odd-proton nuclei,respectively.By including the dependence of the nuclear spin and Schmidt magnetic moment,the machine-learning approach precisely describes the magnetic moments of odd-A uclei with rms deviations of 0.036μN for odd-neutron nuclei and 0.061μN for odd-proton nuclei.Furthermore,the evolution of magnetic moments along isotopic chains,including the staggering and sudden jump trend,which are difficult to describe using nuclear models,have been well reproduced by the Bayesian neural network(BNN)approach.The magnetic moments of doubly closed-shell±1 nuclei,for example,isoscalar and isovector magnetic moments,have been well studied and compared with the corresponding non-relativistic and relativistic calculations.
文摘A detailed study of the capabilities of artificial neural networks to diagnoses cracks in massive concrete structures is presented. This paper includes the components of the expert system such as design thought, basic structure, building of knowledge base and the implementation of neural network applied model. The realizing method of neural network based clustering algorithm in the knowledge base and self study is analyzed emphatically and stimulated by means of the computer. From the above study, some important conclusions have been drawn and some new viewpoints have been suggested.
文摘The compressive strength of concrete is one of most important mechanical parameters in the performance assessment of existing reinforced concrete structures.According to various international codes,core samples are drilled and tested to obtain the concrete compressive strengths.Non-destructive testing is an important alternative when destructive testing is not feasible without damaging the structure.The commonly used non-destructive testing(NDT)methods to estimate the in-situ values include the Rebound hammer test and the Ultrasonic Pulse Velocity test.The poor reliability of these tests due to different aspects could be partially contrasted by using both methods together,as proposed.in the SonReb method.There are three techniques that are commonly used to predict the compressive strength of concrete based on the SonReb measurements:computational modeling,artificial intelligence,and parametric multi-variable regression models.In a previous study the accuracy of the correlation formulas deduced from the last technique has been investigated in comparison with the effective compressive strengths based on destructive test results on core drilled in adjacent locations.The aim of this study is to verify the accuracy of Artificial Neural Approach comparing the estimated compressive strengths based on NDT measured parameters with the same effective compressive strengths.The comparisons show the best performance of ANN approach.
基金Project supported by the National Natural Science Foundations of China (Grant Nos. 70571030 and 90610031)the Society Science Foundation from Ministry of Education of China (Grant No. 08JA790057)the Advanced Talents’ Foundation and Student’s Foundation of Jiangsu University (Grant Nos. 07JDG054 and 07A075)
文摘In this study, a successful linear matrix inequality approach is used to analyse a non-parameter perturbation of multi-delay Hopfield neural network by constructing an appropriate Lyapunov-Krasovskii functional. This paper presents the comprehensive discussion of the approach and also extensive applications.
文摘A multimodal biometric system is applied to recognize individuals for authentication using neural networks. In this paper multimodal biometric algorithm is designed by integrating iris, finger vein, palm print and face biometric traits. Normalized score level fusion approach is applied and optimized, encoded for matching decision. It is a multilevel wavelet, phase based fusion algorithm. This robust multimodal biometric algorithm increases the security level, accuracy, reduces memory size and equal error rate and eliminates unimodal biometric algorithm vulnerabilities.
基金supported by the Natural Science Foundation of Jilin Province (No. 20220101017JC)the National Natural Science Foundation of China (Nos. 11675063, 11875070, and 11935001)+1 种基金Key Laboratory of Nuclear Data foundation (JCKY2020201C157)the Anhui Project (Z010118169)
文摘Nuclear charge density distribution plays an important role in both nuclear and atomic physics,for which the two-parameter Fermi(2pF)model has been widely applied as one of the most frequently used models.Currently,the feedforward neural network has been employed to study the available 2pF model parameters for 86 nuclei,and the accuracy and precision of the parameter-learning effect are improved by introducing A^(1∕3)into the input parameter of the neural network.Furthermore,the average result of multiple predictions is more reliable than the best result of a single prediction and there is no significant difference between the average result of the density and parameter values for the average charge density distribution.In addition,the 2pF parameters of 284(near)stable nuclei are predicted in this study,which provides a reference for the experiment.
文摘Recently,Artificial Neural Networks(ANNs)have been used for various scientific and engineering applications essentially because they allow the modeling of a process,which starts from the database containing the variables that describe that particular process.They have already been applied to the study of landslides in particular,with reference to the indirect determination of the triggering
文摘In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.
基金Nature Science Foundation of China under Grant no.50179034.
文摘In order to effectively cope with exponent increase of the complexity faced to the rock mechanics analysis problems and the large incompatibility existing between the information level required to model the rock mass and engineering and our obtainable information level at hand,the integrated approaches with intelligent characters are proposed. Many previous standard methods,such as precedent type analysis,rock classification,analytic method stress-based,basic numerical methods (BEM,FEM,DEM,hybrid),and their extended numerical methods (fully coupled) to be developed,can be selected respectively or integrated accordingly. It is alternative to develop basic/fully integrated system,and internet-based approaches. These novel methods can also be selected or integrated each other or with the standard methods to perform rock mechanics analysis. Some key techniques to develop these alternative methods are discussed. It may focus in future on developing fully integrated systems and internet-based approaches. Developing an environmental,virtual facility/space shall be firstly done for this collaborative research on internet.
文摘The multiplicity distribution (P(nch)) of charged particles produced in a high energy collision is a key quantity to understand the mechanism of multiparticle production. This paper describes the novel application of an artificial neural network (ANN) black-box modeling approach based on the cascade correlation (CC) algorithm formulated to calculate and predict multiplicity distribution of proton-proton (antiproton) (PP and PP ) inelastic interactions full phase space at a wide range of center-mass of energy . In addition, the formulated cascade correlation neural network (CCNN) model is used to empirically calculate the average multiplicity distribution nch> as a function of . The CCNN model was designed based on available experimental data for = 30.4 GeV, 44.5 GeV, 52.6 GeV, 62.2 GeV, 200 GeV, 300 GeV, 540 GeV, 900 GeV, 1000 GeV, 1800 GeV, and 7 TeV. Our obtained empirical results for P(nch), as well as nch> for (PP and PP) collisions are compared with the corresponding theoretical ones which obtained from other models. This comparison shows a good agreement with the available experimental data (up to 7 TeV) and other theoretical ones. At full large hadron collider (LHC) energy ( = 14 TeV) we have predicted P(nch) and nch> which also, show a good agreement with different theoretical models.
