Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combin...Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combinations of controllable parameters(microhardness and clad thickness).The microhardness and clad thickness are recorded at all the experimental runs and studied using Taguchi S/N ratio and the optimum controllable parametric combination is obtained.However,an artificial neural network(ANN)identifies different sets of optimal combinations from Taguchi method but they both got almost the same clad thickness and hardness values.The micro-hardness of cladded layer is found to be6.22 times(HV_(0.5)752)the SS304 hardness(HV_(0.5)121).The presence of nitride ceramics results in a higher micro hardness.The cladded surface is free from cracks and pores.The average clad thickness is found to be around 0.6 mm.展开更多
Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,...Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated.This work proposes an adaptive Bayesian inversion method solved using artificial neural network(ANN)based Markov Chain Monte Carlo simulation.The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples,whereby the computational workload can be greatly reduced.It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database.The enrichment samples are obtained from the posterior distribution after iteration,which allows a more accurate and rapid manner to the target posterior.The method was then applied to the hydraulic analysis of an earth dam.After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location,it was validated by the pore water pressure monitoring values at the upstream saturated location.In addition,the uncertainty in the permeability coefficient was reduced,from 0.5 to 0.05.It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.展开更多
在挤出机单螺杆计量段二维解析建模的基础上,采用交叉验证方法构建人工神经网络(artificial neural network,ANN)模型并对其进行了超参数优化,以有效地映射挤出机工作条件和结构参数与生产率和功耗之间的复杂非线性关系。提出利用ANN代...在挤出机单螺杆计量段二维解析建模的基础上,采用交叉验证方法构建人工神经网络(artificial neural network,ANN)模型并对其进行了超参数优化,以有效地映射挤出机工作条件和结构参数与生产率和功耗之间的复杂非线性关系。提出利用ANN代理模型,结合NSGA-Ⅱ(non-dominated sorting genetic algorithmⅡ)算法对螺杆计量段的结构参数进行多目标优化,并通过TOPSIS(technique for order preference by similarity to an ideal solution)法得到最优生产率和功耗组合的结构参数。相关工作对单螺杆计量段结构参数的智能化设计具有理论指导意义。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
A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei.The Jastrow factor and backflow transformation were represented by artif...A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei.The Jastrow factor and backflow transformation were represented by artificial neural networks.With this newly developed wave function,variational Monte Carlo calculations were carried out for3H and3He nuclei starting from a nuclear Hamiltonian based on the leadingorder pionless effective field theory.The obtained ground-state energy and charge radii were successfully benchmarked against the results of the highly-accurate hypersphericalharmonics method.The backflow transformation plays a crucial role in improving the nodal surface of the Slater determinant and,thus,providing accurate ground-state energy.展开更多
Chemometric amylose modeling for global calibration,using whole grain near infrared transmittance spectra and sample selection,was used in an artificial neural network(ANN) ,to assess the global and local models gener...Chemometric amylose modeling for global calibration,using whole grain near infrared transmittance spectra and sample selection,was used in an artificial neural network(ANN) ,to assess the global and local models generated,based on samples of newly bred Indica,Japonica and rice. Global sample sets had a wide range of sample variation for amylose content(0 to 25.9%) . The local sample set,Japonica sample,had relatively low amylose content and a narrow sample variation(amylose;12.3% to 21.0%) . For sample selection the CENTER algorithm was applied to generate calibration,validation and stop sample sets. Spectral preprocessing was found to reduce the optimum number of partial least squares(PLS) components for amylose content and thus enhance the robustness of the local calibration. The best model was found to be an ANN global calibration with spectral preprocessing;the next was a PLS global calibration using standard spectra. These results pose the question whether an ANN algorithm with spectral preprocessing could be developed for global and local calibration models or whether PLS without spectral preprocessing should be developed for global calibration models. We suggest that global calibration models incorporating an ANN may be used as a universal calibration model.展开更多
The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and co...The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.展开更多
While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using po...While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96).展开更多
In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on or...In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental design.The experimental data of residual stress and microhardness were measured in the same depth.The residual stress and microhardness laws were investigated and analyzed.Artificial neural network(ANN)with four layers(4-N-(N-1)-2)was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap LSP.The experimental data were divided as training-testing sets in pairs.Laser energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as outputs.The prediction performances with different network configuration of developed ANN models were compared and analyzed.The developed ANN model with network configuration of 4-7-6-2 showed the best predict performance.The predicted values showed a good agreement with the experimental values.In addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were studied.It can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.展开更多
Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificia...Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificial neural network(ANN) and the support vector machine(SVM) respectively. And the recognition experiments were carried out by using flame image data sampled from an alumina rotary kiln to evaluate their effectiveness. The results show that the two recognition methods can achieve good results, which verify the effectiveness of the shape descriptor. The highest recognition rate is 88.83% for SVM and 87.38% for ANN, which means that the performance of the SVM is better than that of the ANN.展开更多
The anisotropy effect is one of the most prominent phenomena in soil mechanics. Although many experimental programs have investigated anisotropy in sand, a computational procedure for determining anisotropy is lacking...The anisotropy effect is one of the most prominent phenomena in soil mechanics. Although many experimental programs have investigated anisotropy in sand, a computational procedure for determining anisotropy is lacking. Thus, this work aims to develop a procedure for connecting the sand friction angle and the loading orientation. All principal stress rotation tests in the literatures were processed via an artificial neural network. Then, with sensitivity analysis, the effect of intrinsic soil properties,consolidation history, and test sample characteristics on enhancing anisotropy was examined. The results imply that decreasing the grain size of the soil increases the effect of anisotropy on soil shear strength. In addition, increasing the angularity of grains increases the anisotropy effect in the sample. The stability of a sandy slope was also examined by considering the anisotropy in shear strength parameters. If the anisotropy effect is neglected, slope safety is overestimated by 5%-25%. This deviation is more apparent in flatter slopes than in steeper ones. However, the critical slip surface in the most slopes is the same in isotropic and anisotropic conditions.展开更多
文摘Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combinations of controllable parameters(microhardness and clad thickness).The microhardness and clad thickness are recorded at all the experimental runs and studied using Taguchi S/N ratio and the optimum controllable parametric combination is obtained.However,an artificial neural network(ANN)identifies different sets of optimal combinations from Taguchi method but they both got almost the same clad thickness and hardness values.The micro-hardness of cladded layer is found to be6.22 times(HV_(0.5)752)the SS304 hardness(HV_(0.5)121).The presence of nitride ceramics results in a higher micro hardness.The cladded surface is free from cracks and pores.The average clad thickness is found to be around 0.6 mm.
基金Project(202006430012)supported by the China Scholarship Council。
文摘Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated.This work proposes an adaptive Bayesian inversion method solved using artificial neural network(ANN)based Markov Chain Monte Carlo simulation.The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples,whereby the computational workload can be greatly reduced.It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database.The enrichment samples are obtained from the posterior distribution after iteration,which allows a more accurate and rapid manner to the target posterior.The method was then applied to the hydraulic analysis of an earth dam.After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location,it was validated by the pore water pressure monitoring values at the upstream saturated location.In addition,the uncertainty in the permeability coefficient was reduced,from 0.5 to 0.05.It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.
文摘在挤出机单螺杆计量段二维解析建模的基础上,采用交叉验证方法构建人工神经网络(artificial neural network,ANN)模型并对其进行了超参数优化,以有效地映射挤出机工作条件和结构参数与生产率和功耗之间的复杂非线性关系。提出利用ANN代理模型,结合NSGA-Ⅱ(non-dominated sorting genetic algorithmⅡ)算法对螺杆计量段的结构参数进行多目标优化,并通过TOPSIS(technique for order preference by similarity to an ideal solution)法得到最优生产率和功耗组合的结构参数。相关工作对单螺杆计量段结构参数的智能化设计具有理论指导意义。
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
基金Supported by National Key R&D Program of China (018YFA0404400)National Natural Science Foundation of China (12070131001,11875075,11935003,11975031,12141501)。
文摘A novel variational wave function defined as a Jastrow factor multiplying a backflow transformed Slater determinant was developed for A=3 nuclei.The Jastrow factor and backflow transformation were represented by artificial neural networks.With this newly developed wave function,variational Monte Carlo calculations were carried out for3H and3He nuclei starting from a nuclear Hamiltonian based on the leadingorder pionless effective field theory.The obtained ground-state energy and charge radii were successfully benchmarked against the results of the highly-accurate hypersphericalharmonics method.The backflow transformation plays a crucial role in improving the nodal surface of the Slater determinant and,thus,providing accurate ground-state energy.
文摘Chemometric amylose modeling for global calibration,using whole grain near infrared transmittance spectra and sample selection,was used in an artificial neural network(ANN) ,to assess the global and local models generated,based on samples of newly bred Indica,Japonica and rice. Global sample sets had a wide range of sample variation for amylose content(0 to 25.9%) . The local sample set,Japonica sample,had relatively low amylose content and a narrow sample variation(amylose;12.3% to 21.0%) . For sample selection the CENTER algorithm was applied to generate calibration,validation and stop sample sets. Spectral preprocessing was found to reduce the optimum number of partial least squares(PLS) components for amylose content and thus enhance the robustness of the local calibration. The best model was found to be an ANN global calibration with spectral preprocessing;the next was a PLS global calibration using standard spectra. These results pose the question whether an ANN algorithm with spectral preprocessing could be developed for global and local calibration models or whether PLS without spectral preprocessing should be developed for global calibration models. We suggest that global calibration models incorporating an ANN may be used as a universal calibration model.
基金supported by the National Natural Science Foundation of China(51875465)
文摘The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.
文摘While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96).
基金Projects(51875558,51471176)supported by the National Natural Science Foundation of ChinaProject(2017YFB1302802)supported by the National Key R&D Program of China。
文摘In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental design.The experimental data of residual stress and microhardness were measured in the same depth.The residual stress and microhardness laws were investigated and analyzed.Artificial neural network(ANN)with four layers(4-N-(N-1)-2)was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap LSP.The experimental data were divided as training-testing sets in pairs.Laser energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as outputs.The prediction performances with different network configuration of developed ANN models were compared and analyzed.The developed ANN model with network configuration of 4-7-6-2 showed the best predict performance.The predicted values showed a good agreement with the experimental values.In addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were studied.It can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.
基金Project(60634020) supported by the National Natural Science Foundation of China
文摘Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificial neural network(ANN) and the support vector machine(SVM) respectively. And the recognition experiments were carried out by using flame image data sampled from an alumina rotary kiln to evaluate their effectiveness. The results show that the two recognition methods can achieve good results, which verify the effectiveness of the shape descriptor. The highest recognition rate is 88.83% for SVM and 87.38% for ANN, which means that the performance of the SVM is better than that of the ANN.
文摘The anisotropy effect is one of the most prominent phenomena in soil mechanics. Although many experimental programs have investigated anisotropy in sand, a computational procedure for determining anisotropy is lacking. Thus, this work aims to develop a procedure for connecting the sand friction angle and the loading orientation. All principal stress rotation tests in the literatures were processed via an artificial neural network. Then, with sensitivity analysis, the effect of intrinsic soil properties,consolidation history, and test sample characteristics on enhancing anisotropy was examined. The results imply that decreasing the grain size of the soil increases the effect of anisotropy on soil shear strength. In addition, increasing the angularity of grains increases the anisotropy effect in the sample. The stability of a sandy slope was also examined by considering the anisotropy in shear strength parameters. If the anisotropy effect is neglected, slope safety is overestimated by 5%-25%. This deviation is more apparent in flatter slopes than in steeper ones. However, the critical slip surface in the most slopes is the same in isotropic and anisotropic conditions.
