Based on the method of artificial neural network, a new approach has been devised to predict the mechanical property of E4303 electrode. The outlined predication model for determining the mechanical property of electr...Based on the method of artificial neural network, a new approach has been devised to predict the mechanical property of E4303 electrode. The outlined predication model for determining the mechanical property of electrode was built upon the production data. The research leverages a back propagation algorithm as the neural network’s learning rule. The result indicates that there are positive correlations between the predicted results and the practical production data. Hence, using the neural network, predication of electrode property can be realized. For the first time, this research provides a more scientific method for designing electrode.展开更多
To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Eart...To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.展开更多
In this paper, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were in...In this paper, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were investigated. The experiments were carried out based on a 3-level, 4-variable Box–Behnken design. The amount of zinc was considered as a function of four independent variables, namely irradiation power, irradiation time, nitric acid concentration, and temperature. The RSM results showed the quadratic polynomial model can be used to describe the relationship between the various factors and the response. Using the ANN analysis, the optimal configuration of the ANN model was found to be 4-10-1. After predicting the model using RSM and ANN, two methodologies were then compared for their predictive capabilities. The results showed that the ANN model is much more accurate in prediction as compared to the RSM.展开更多
In order to study the effect of alloy component on magnetic properties of NdFeB magnets, the experiment schemes are carried out by the uniform design theory, and the relationship between the component and the magnetic...In order to study the effect of alloy component on magnetic properties of NdFeB magnets, the experiment schemes are carried out by the uniform design theory, and the relationship between the component and the magnetic properties is established by artificial neural network(ANN) predicting model.The element contents of alloys are optimized by the ANN model.Meanwhile, the influences of mono-factor or multi-factor interaction on alloy magnetic properties are respectively discussed according to the curves ploted by ANN model.Simulation result shows that the predicted and measured results are in good agreement.The relative error is every low, the error is not more than 1.68% for remanence Br, 1.56% for maximal energy product (BH)m, and 7.73% for coercivity Hcj.Hcj can be obviously improved and Br can be reduced by increasing Nd or Zr content.Co and B have advantageous effects on increasing Br and disadvantageous effects on increasing Hcj.Influence of alloying elements on Hcj and Br are inverse, and the interaction among the alloying elements play an important role in the magnetic properties of NdFeB magnets.The ANN prediction model presents a new approach to investigate the nonlinear relationship between the component and the magnetic properties of NdFeB alloys.展开更多
This paper describes a model of property prediction for alloys using the mapping function and self-learning ability of artificial neural network. By learning from experimental data, the neural network induces the rela...This paper describes a model of property prediction for alloys using the mapping function and self-learning ability of artificial neural network. By learning from experimental data, the neural network induces the relationship between composition, processing and properties of alloys, and predicts the properties with given composition and processing parameters of new alloys.The verification of sealing alloys demonstrates that the artificial neural network is an effective method for materials design and properties prediction.展开更多
Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,...Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.展开更多
The Farmers Property Mortgage Policy is a strategic financial policy in western China, a relatively underdeveloped region. Many contradictions and conflicts exist in the process between the strong demand for the loans...The Farmers Property Mortgage Policy is a strategic financial policy in western China, a relatively underdeveloped region. Many contradictions and conflicts exist in the process between the strong demand for the loans by farmers and the strict risk control by the financial institutions. The rural finance corporations should use scientific analysis and investigation of the potential households for overall evaluation of the customers. These include historical credit rating, present family situation, and other related information. Three different data mining methods were applied in this paper to the specifically-collected household data. The objective was to study which factor could be the most important in determining loan demand for households, and in the meanwhile, to classify and predict the possibility of loan demand for the potential customers. The results obtained from the three methods indicated the similar outputs, income level, land area, the way of loan, and the understanding of policy were four main factors which decided the probability of one specific farmer applying for a credit loan. The results also embodied the difference within the three methods for classifying and predicting the loan anticipation for the testing households. The artificial neural network model had the highest accuracy of 91.4 which is better than the other two methods.展开更多
Total transmission plays an important role in efficiency improvement and wavefront control,and has made great progress in many applications,such as the optical film and signal transmission.Therefore,many traditional p...Total transmission plays an important role in efficiency improvement and wavefront control,and has made great progress in many applications,such as the optical film and signal transmission.Therefore,many traditional physical methods represented by transformation optics have been studied to achieve total transmission.However,these methods have strict limitations on the size of the photonic structure,and the calculation is complex.Here,we exploit deep learning to achieve this goal.In deep learning,the data-driven prediction and design are carried out by artificial neural networks(ANNs),which provide a convenient architecture for large dataset problems.By taking the transmission characteristic of the multi-layer stacks as an example,we demonstrate how optical materials can be designed by using ANNs.The trained network directly establishes the mapping from optical materials to transmission spectra,and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space.Our work paves the way for the optical material design with special properties based on deep learning.展开更多
文摘Based on the method of artificial neural network, a new approach has been devised to predict the mechanical property of E4303 electrode. The outlined predication model for determining the mechanical property of electrode was built upon the production data. The research leverages a back propagation algorithm as the neural network’s learning rule. The result indicates that there are positive correlations between the predicted results and the practical production data. Hence, using the neural network, predication of electrode property can be realized. For the first time, this research provides a more scientific method for designing electrode.
基金China Energy Engineering Group Planning&Engineering Co.,Ltd.Concentrated Development Scientific Research Project Under Grant No.GSKJ2-T11-2019。
文摘To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.
文摘In this paper, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were investigated. The experiments were carried out based on a 3-level, 4-variable Box–Behnken design. The amount of zinc was considered as a function of four independent variables, namely irradiation power, irradiation time, nitric acid concentration, and temperature. The RSM results showed the quadratic polynomial model can be used to describe the relationship between the various factors and the response. Using the ANN analysis, the optimal configuration of the ANN model was found to be 4-10-1. After predicting the model using RSM and ANN, two methodologies were then compared for their predictive capabilities. The results showed that the ANN model is much more accurate in prediction as compared to the RSM.
文摘In order to study the effect of alloy component on magnetic properties of NdFeB magnets, the experiment schemes are carried out by the uniform design theory, and the relationship between the component and the magnetic properties is established by artificial neural network(ANN) predicting model.The element contents of alloys are optimized by the ANN model.Meanwhile, the influences of mono-factor or multi-factor interaction on alloy magnetic properties are respectively discussed according to the curves ploted by ANN model.Simulation result shows that the predicted and measured results are in good agreement.The relative error is every low, the error is not more than 1.68% for remanence Br, 1.56% for maximal energy product (BH)m, and 7.73% for coercivity Hcj.Hcj can be obviously improved and Br can be reduced by increasing Nd or Zr content.Co and B have advantageous effects on increasing Br and disadvantageous effects on increasing Hcj.Influence of alloying elements on Hcj and Br are inverse, and the interaction among the alloying elements play an important role in the magnetic properties of NdFeB magnets.The ANN prediction model presents a new approach to investigate the nonlinear relationship between the component and the magnetic properties of NdFeB alloys.
文摘This paper describes a model of property prediction for alloys using the mapping function and self-learning ability of artificial neural network. By learning from experimental data, the neural network induces the relationship between composition, processing and properties of alloys, and predicts the properties with given composition and processing parameters of new alloys.The verification of sealing alloys demonstrates that the artificial neural network is an effective method for materials design and properties prediction.
文摘Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.
文摘The Farmers Property Mortgage Policy is a strategic financial policy in western China, a relatively underdeveloped region. Many contradictions and conflicts exist in the process between the strong demand for the loans by farmers and the strict risk control by the financial institutions. The rural finance corporations should use scientific analysis and investigation of the potential households for overall evaluation of the customers. These include historical credit rating, present family situation, and other related information. Three different data mining methods were applied in this paper to the specifically-collected household data. The objective was to study which factor could be the most important in determining loan demand for households, and in the meanwhile, to classify and predict the possibility of loan demand for the potential customers. The results obtained from the three methods indicated the similar outputs, income level, land area, the way of loan, and the understanding of policy were four main factors which decided the probability of one specific farmer applying for a credit loan. The results also embodied the difference within the three methods for classifying and predicting the loan anticipation for the testing households. The artificial neural network model had the highest accuracy of 91.4 which is better than the other two methods.
基金supported by the National Key Research and Development Program of China under Grant No.2020YFA0710100the National Natural Science Foundation of China under Grants No.92050102,No.11874311,and No.11504306the Fundamental Research Funds for the Central Universities under Grant No.20720200074。
文摘Total transmission plays an important role in efficiency improvement and wavefront control,and has made great progress in many applications,such as the optical film and signal transmission.Therefore,many traditional physical methods represented by transformation optics have been studied to achieve total transmission.However,these methods have strict limitations on the size of the photonic structure,and the calculation is complex.Here,we exploit deep learning to achieve this goal.In deep learning,the data-driven prediction and design are carried out by artificial neural networks(ANNs),which provide a convenient architecture for large dataset problems.By taking the transmission characteristic of the multi-layer stacks as an example,we demonstrate how optical materials can be designed by using ANNs.The trained network directly establishes the mapping from optical materials to transmission spectra,and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space.Our work paves the way for the optical material design with special properties based on deep learning.
