Model-Based Design is an efficient and cost-effective way to develop controls, signal processing, image processing, communications, mechatronics, and other embedded systems. Rather than re-lying on physical prototypes...Model-Based Design is an efficient and cost-effective way to develop controls, signal processing, image processing, communications, mechatronics, and other embedded systems. Rather than re-lying on physical prototypes and textual specifications, Model-Based Design uses a system model as an executable specification throughout development. It supports system- and component-level design and simulation, automatic code generation, and continuous test and verification. This paper is focused firstly on the so-called model-based design and aims at presenting an up-to-date state of the art in this important field. Secondly, it develops a model based design for wind energy systems. Mathematical formulations and numerical implementations for different components of wind energy systems are highlighted with Simscape language. Finally, results are derived from simulations.展开更多
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
Based on the HS 4-digit code trade data in UNCOMTRADE from 1995 to 2020, this paper analyzes the characteristics of the evolution of the global PG trade network using the complex network approach and analyzes the chan...Based on the HS 4-digit code trade data in UNCOMTRADE from 1995 to 2020, this paper analyzes the characteristics of the evolution of the global PG trade network using the complex network approach and analyzes the changes in its resilience at the overall and country levels, respectively. The results illustrated that:(1) The scale of the global PG trade network tends to expand, and the connection is gradually tightened, experiencing a change from a “supply-oriented” to a “supply-and-demand” pattern, in which the U.S., Russia, Qatar, and Australia have gradually replaced Canada, Japan, and Russia to become the core trade status, while OPEC countries such as Qatar, Algeria, and Kuwait mainly rely on PG exports to occupy the core of the global supply, and the trade status of other countries has been dynamically alternating and evolving.(2) The resilience of the global PG trade network is lower than that of the random network and decreases non-linearly with more disrupted countries. Moreover, the impact of the U.S. is more significant than the rest of countries. Simulations using the exponential random graph model(ERGM) model revealed that national GDP, institutional quality, common border and RTA network are the determinants of PG trade network formation, and the positive impact of the four factors not only varies significantly across regions and stages, but also increases with national network status.展开更多
A universal simulator capable of simulating virtually any user-defined electrochemical/chemical problems in one-dimensional diffusion geometry was developed based on an exponentially expanding grid modification of the...A universal simulator capable of simulating virtually any user-defined electrochemical/chemical problems in one-dimensional diffusion geometry was developed based on an exponentially expanding grid modification of the existing network approach. Some generalized reaction-diffusion governing equations of an arbitrary electrochemical/chemical process were derived, and program controlled automatic generation of the corresponding PSPICE netlist file was realized. On the basis of the above techniques, a universal simulator package was realized, which is capable of dealing with arbitrarily complex electrochemical/chemical problems with one-dimensional diffusion geometry such as planar diffusion, spherical diffusion, cylindrical diffusion and rotational disk diffusion-convection processes. The building of such a simulator is easy and thus it would be very convenient to have it updated for simulations of newly raised electrochemical problems.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 exponentially expanded space grid was incorporated into the network approach to overcome the problem of low simulation efficiency during the simulations of electrochemical problems with stiff kinetics or wide disp...The exponentially expanded space grid was incorporated into the network approach to overcome the problem of low simulation efficiency during the simulations of electrochemical problems with stiff kinetics or wide dispersion of diffusion coefficients, resulting in an effective electrochemical simulation method: exponentially expanded grid network approach (EEGNA). The stability and accuracy of the EEGNA for the simulation of various electrode processes coupled with different types of homogeneous reactions were investigated.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
文摘Model-Based Design is an efficient and cost-effective way to develop controls, signal processing, image processing, communications, mechatronics, and other embedded systems. Rather than re-lying on physical prototypes and textual specifications, Model-Based Design uses a system model as an executable specification throughout development. It supports system- and component-level design and simulation, automatic code generation, and continuous test and verification. This paper is focused firstly on the so-called model-based design and aims at presenting an up-to-date state of the art in this important field. Secondly, it develops a model based design for wind energy systems. Mathematical formulations and numerical implementations for different components of wind energy systems are highlighted with Simscape language. Finally, results are derived from simulations.
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.
基金funded by the National Natural Science Foundation of China Projects (Grant number 71703128)Anhui Provincial Higher Education Research Key Project (grant number: 2024AH052139)。
文摘Based on the HS 4-digit code trade data in UNCOMTRADE from 1995 to 2020, this paper analyzes the characteristics of the evolution of the global PG trade network using the complex network approach and analyzes the changes in its resilience at the overall and country levels, respectively. The results illustrated that:(1) The scale of the global PG trade network tends to expand, and the connection is gradually tightened, experiencing a change from a “supply-oriented” to a “supply-and-demand” pattern, in which the U.S., Russia, Qatar, and Australia have gradually replaced Canada, Japan, and Russia to become the core trade status, while OPEC countries such as Qatar, Algeria, and Kuwait mainly rely on PG exports to occupy the core of the global supply, and the trade status of other countries has been dynamically alternating and evolving.(2) The resilience of the global PG trade network is lower than that of the random network and decreases non-linearly with more disrupted countries. Moreover, the impact of the U.S. is more significant than the rest of countries. Simulations using the exponential random graph model(ERGM) model revealed that national GDP, institutional quality, common border and RTA network are the determinants of PG trade network formation, and the positive impact of the four factors not only varies significantly across regions and stages, but also increases with national network status.
基金Project supported by the National Natural Science Foundation of China (No. 20173054).
文摘A universal simulator capable of simulating virtually any user-defined electrochemical/chemical problems in one-dimensional diffusion geometry was developed based on an exponentially expanding grid modification of the existing network approach. Some generalized reaction-diffusion governing equations of an arbitrary electrochemical/chemical process were derived, and program controlled automatic generation of the corresponding PSPICE netlist file was realized. On the basis of the above techniques, a universal simulator package was realized, which is capable of dealing with arbitrarily complex electrochemical/chemical problems with one-dimensional diffusion geometry such as planar diffusion, spherical diffusion, cylindrical diffusion and rotational disk diffusion-convection processes. The building of such a simulator is easy and thus it would be very convenient to have it updated for simulations of newly raised electrochemical problems.
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 exponentially expanded space grid was incorporated into the network approach to overcome the problem of low simulation efficiency during the simulations of electrochemical problems with stiff kinetics or wide dispersion of diffusion coefficients, resulting in an effective electrochemical simulation method: exponentially expanded grid network approach (EEGNA). The stability and accuracy of the EEGNA for the simulation of various electrode processes coupled with different types of homogeneous reactions were investigated.
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.
