Because of complexity and non-predictability of the tunnel surrounding rock,the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretica...Because of complexity and non-predictability of the tunnel surrounding rock,the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering.During design,it is a frequent practice,therefore,to give recommended values by analog based on experience.It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying,expressing and coping with such complex non-linear relationships.The parameters can be verified by searching the optimal network structure,using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results.In the current paper,the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua(FLAC3D.The high non-linearity,network reasoning and coupling ability of the neural network are employed.The output vector required of the training of the neural network is obtained with the numerical analysis software.And the overall space search is conducted by employing the Adaptive Immunity Algorithm.As a result,we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum.At the same time,the computing speed and efficiency are increased as well.Further,in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project.The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data.This is of practical significance to the appraisal of stability and informationization design of the surrounding rock.展开更多
In energy dispersive X-ray fiuorescence(EDXRF), quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, ...In energy dispersive X-ray fiuorescence(EDXRF), quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, a hybrid approach of genetic algorithm(GA) and back propagation(BP) neural network is proposed without considering the complex relationship between the elemental content and peak intensity. The aim of GA-optimized BP is to get better network initial weights and thresholds. The starting point of this approach is that the reciprocal of the mean square error of the initialization BP neural network is set as the fitness value of the individuals in GA; and the initial weights and thresholds are replaced by individuals, then the optimal individual is searched by selecting, crossover and mutation operations, finally a new BP neural network model is established with the optimal initial weights and thresholds. The quantitative analysis results of titanium and iron contents in five types of mineral samples show that the relative errors of 76.7% samples are below 2%, compared to chemical analysis data, which demonstrates the effectiveness of the proposed method.展开更多
This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity ...This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity in software risks, the method of principal components analysis is adopted in the model to enhance network stability.To solve uncertainty of the neural networks structure and the uncertainty of the initial weights, genetic algorithms is employed.The experimental result reveals that the precision of software risk analysis can be improved by using the erhanced neural networks model.展开更多
. This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet c.... This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet coefficients of the noisy signal to estimate the discontinuity of hard threshold function and soft threshold function, limiting its further application in order to overcome this shortcoming, this paper proposes a new threshold function, compared with the original threshold function, a new threshold function is simple and easy to calculate, not only with the soft threshold function is continuous. To deal with this drawback, we integrate the NN to enhance the model. Neural network belongs to the basic unsupervised learning of neural networks, the principle of competition based on the mechanism of learning and biological and the memory capacity can be increased as the number of learning patterns increases, not only offi ine learning can also be carried out on-line ＂learning while learning＂ type. The integrated algorithm can host better performance.展开更多
This study involves the development of an analytical model for understanding the behavior of the extended, stiffened end-plate moment connections with eight high strength bolts. Modeling of the connection as an assemb...This study involves the development of an analytical model for understanding the behavior of the extended, stiffened end-plate moment connections with eight high strength bolts. Modeling of the connection as an assemblage of finite elements (FE) used for load deformation analysis, with material, and contact nonlinearities are developed. Results from the FE mathematical model are verified with results from the ANSYS computer program as well as with the test results. Sensitivity and feasibility studies are carried out. Significant geometry and force related variables are introduced;and by varying the geometric variables of the connections within a practical range, a matrix of test cases is obtained. Maximum end-plate separation, maximum bending stresses in the end-plate, and the forces from the connection bolts for these test cases are obtained. From the FE analysis, a database is produced to collect results for the artificial neural network analysis. Finally, salient features of the optimized Artificial Neural Network (ANN) via Genetic Algorithm (GA) analysis are introduced and implemented with the aim of predicting the overall behavior of the connection.展开更多
In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration....In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration.The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound(UB)limit analyses,facilitating an in-depth examination of various material and geometric conditions.A hybrid deep neural network,specifically the Whale Optimization Algorithm-Deep Neural Network(WOA-DNN),is then employed to utilize these 10,000 outputs for precise bearing capacity predictions.Notably,the WOA-DNN model outperforms conventional ML techniques,offering a robust and accurate prediction tool.This innovative approach explores a broad range of design parameters,including sand layer depth,load-to-soil unit weight ratio,internal friction angle,cohesion,and footing roughness.A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity,providing valuable insights for practical foundation design.This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles,marking a significant stride in geotechnical engineering advancements.展开更多
Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway s...Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.展开更多
Neural network has the abilities of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain nu...Neural network has the abilities of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. This paper presents a solution for overcoming these shortages from two aspects. One is to adopt principle component analysis to select study samples and make some of them contain sample characteristics as many as possible, the other is to train the network using Levenberg-Marquardt backward propagation algorithm. This new method was proved to be valid and practicable in site selection of practical garbage power generation plants.展开更多
To reasonably implement the reliability analysis and describe the significance of influencing parameters for the multi-failure modes of turbine blisk, advanced multiple response surface method (AMRSM) was proposed for...To reasonably implement the reliability analysis and describe the significance of influencing parameters for the multi-failure modes of turbine blisk, advanced multiple response surface method (AMRSM) was proposed for multi-failure mode sensitivity analysis for reliability. The mathematical model of AMRSM was established and the basic principle of multi-failure mode sensitivity analysis for reliability with AMRSM was given. The important parameters of turbine blisk failures are obtained by the multi-failure mode sensitivity analysis of turbine blisk. Through the reliability sensitivity analyses of multiple failure modes (deformation, stress and strain) with the proposed method considering fluid-thermal-solid interaction, it is shown that the comprehensive reliability of turbine blisk is 0.9931 when the allowable deformation, stress and strain are 3.7 x 10(-3) m, 1.0023 x 10(9) Pa and 1.05 x 10(-2) m/m, respectively; the main impact factors of turbine blisk failure are gas velocity, gas temperature and rotational speed. As demonstrated in the comparison of methods (Monte Carlo (MC) method, traditional response surface method (RSM), multiple response surface method (MRSM) and AMRSM), the proposed AMRSM improves computational efficiency with acceptable computational accuracy. The efforts of this study provide the AMRSM with high precision and efficiency for multi-failure mode reliability analysis, and offer a useful insight for the reliability optimization design of multi-failure mode structure. (C) 2016 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.展开更多
Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures...Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R^(2)in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy.展开更多
The driver’s cognitive and physiological states affect his/her ability to control the vehicle.Thus,these driver states are essential to the safety of automobiles.The design of advanced driver assistance systems(ADAS)...The driver’s cognitive and physiological states affect his/her ability to control the vehicle.Thus,these driver states are essential to the safety of automobiles.The design of advanced driver assistance systems(ADAS)or autonomous vehicles will depend on their ability to interact effectively with the driver.A deeper understanding of the driver state is,therefore,paramount.Electroencephalography(EEG)is proven to be one of the most effective methods for driver state monitoring and human error detection.This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades.First,the commonly used EEG system setup for driver state studies is introduced.Then,the EEG signal preprocessing,feature extraction,and classification algorithms for driver state detection are reviewed.Finally,EEG-based driver state monitoring research is reviewed in-depth,and its future development is discussed.It is concluded that the current EEGbased driver state monitoring algorithms are promising for safety applications.However,many improvements are still required in EEG artifact reduction,real-time processing,and between-subject classification accuracy.展开更多
There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill l...There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill load cell mass,SAG mill solid percentage,inlet and outlet water to the SAG mill and work index are studied.A total number of185full-scale SAG mill works are utilized to develop the artificial neural network(ANN)and the hybrid of ANN and genetic algorithm(GANN)models with relations of input and output data in the full-scale.The results show that the GANN model is more efficient than the ANN model in predicting SAG mill power.The sensitivity analysis was also performed to determine the most effective input parameters on SAG mill power.The sensitivity analysis of the GANN model shows that the work index,inlet water to the SAG mill,mill load cell weight,SAG mill solid percentage,mass flowrate and feed moisture have a direct relationship with mill power,while outlet water to the SAG mill has an inverse relationship with mill power.The results show that the GANN model could be useful to evaluate a good output to changes in input operation parameters.展开更多
There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites.Today,customers throughout the world share their points of view on all kinds of topics...There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites.Today,customers throughout the world share their points of view on all kinds of topics through these sources.The massive volume of data created by these customers makes it impossible to analyze such data manually.Therefore,an efficient and intelligent method for evaluating social media data and their divergence needs to be developed.Today,various types of equipment and techniques are available for automatically estimating the classification of sentiments.Sentiment analysis involves determining people’s emotions using facial expressions.Sentiment analysis can be performed for any individual based on specific incidents.The present study describes the analysis of an image dataset using CNNswithPCA intended to detect people’s sentiments(specifically,whether a person is happy or sad).This process is optimized using a genetic algorithm to get better results.Further,a comparative analysis has been conducted between the different models generated by changing the mutation factor,performing batch normalization,and applying feature reduction using PCA.These steps are carried out across five experiments using theKaggledataset.The maximum accuracy obtained is 96.984%,which is associated with the Happy and Sad sentiments.展开更多
Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known bef...Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.展开更多
Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by us...Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.展开更多
An image consists of large data and requires more space in the memory. The large data results in more transmission time from transmitter to receiver. The time consumption can be reduced by using data compression techn...An image consists of large data and requires more space in the memory. The large data results in more transmission time from transmitter to receiver. The time consumption can be reduced by using data compression techniques. In this technique, it is possible to eliminate the redundant data contained in an image. The compressed image requires less memory space and less time to transmit in the form of information from transmitter to receiver. Artificial neural net- work with feed forward back propagation technique can be used for image compression. In this paper, the Bipolar Coding Technique is proposed and implemented for image compression and obtained the better results as compared to Principal Component Analysis (PCA) technique. However, the LM algorithm is also proposed and implemented which can acts as a powerful technique for image compression. It is observed that the Bipolar Coding and LM algorithm suits the best for image compression and processing applications.展开更多
In this work, austenitic stainless steel screws employed in a locking compression plate for veterinarian use were investigated. These types of implants are widely utilized in bone fractures healing. Two surgical screw...In this work, austenitic stainless steel screws employed in a locking compression plate for veterinarian use were investigated. These types of implants are widely utilized in bone fractures healing. Two surgical screws were extracted due to the observation of slight superficial red rust colorizing on one of the screw implants, visual evidence of probable screw rusting. From the same implant, another screw was extracted simultaneously without visual evidence of rusting. In order to characterize and analyze the different behavior of both screws, the chemical composition was characterized by atomic absorption and energy dispersive X-ray spectroscopy (EDS) coupled to a scanning electron microscope (SEM). Also, the screws were studied by metallography, optical microscopy (OM), Vickers microhardness tests, and SEM analysis. On the other hand, a prospection for alloy chemical composition limits of these types of implants was performed based on the Schaeffler-Delong diagram and the ASTM F-138 standard. To analyze the effect of the chemical composition, heat treatment, microstructure, pitting resistance equivalent number (PRE) and stacking fault energy (SFE), a genetic algorithm (GA) and an artificial neural network (ANN) were used. In accordance with the elemental analysis, the surgical screws do not fulfill the ranges of the chemical composition established by the ASTM F-138 standard. Furthermore, there were found differences between the microstructures of the screws. In regard to the prospection, the results of GA and ANN support the proposed chemical composition region on the Schaeffler-Delong diagram. The corrosion failure was associated with severe plastic deformation and the presence of precipitates. The proposal can minimize the cause of failures in these types of austenitic stainless steel implants.展开更多
To accurately depict the strong nonlinear relationship between the viscosity of propellant slurry and shear rate,premix time,and temperature,and to improve the prediction accuracy,based on the sample preparation and e...To accurately depict the strong nonlinear relationship between the viscosity of propellant slurry and shear rate,premix time,and temperature,and to improve the prediction accuracy,based on the sample preparation and experimental measurement of a certain type of propellant,viscosity data under multiple working conditions were obtained as the basic data for the research.By comparing typicalmodels such as support vector regression and random forest,it was found that although the traditional BP neural network was superior to the both,its accuracy was still insufficient.Based on this,a BPmodel co-optimized by the SparrowSearch Algorithm(SSA)and theGenetic Algorithm(GA)is proposed.The global search of SSA and the local convergence of GA are utilized to conduct dual optimization of the initial weights and thresholds of the BP network,and the training is completed based on themeasured shear rate,temperature and time data.Further,the Box-Behnken response surface design is adopted to transformthe output of the neural network into a quadratic explicit function of viscosity and multiple factors.The results show that the SSA-GABP model achieves a determination coefficient of R^(2)=0.948,compared with R^(2)=0.628 for the traditional BP neural network,while the root mean square error(RMSE)is reduced from 1093.99 to 154.75.Within the key pouring viscosity range of 200–600 Pa⋅s,the prediction deviation remains within±5%,and the overall prediction variance is improved by more than 100%.The polynomial quantification obtained from the response surface reveals the dominant role of shear rate and its interaction with temperature and time.The fitting curve is more in line with the experimental trend than the traditional constitutive model.The constructed explicit function can be directly embedded in Computational Fluid Dynamics(CFD)for pouring process simulation and has good engineering application value.展开更多
In order to find out the optimal press bend forming path in fabricating aircraft integral panels, this article proposes a new method on the basis of the authors＇ previous work. It is composed of the finite element me...In order to find out the optimal press bend forming path in fabricating aircraft integral panels, this article proposes a new method on the basis of the authors＇ previous work. It is composed of the finite element method （FEM） equivalent model, the surface curvature analysis, the artificial neural network response surface and the genetic algorithm. The method begins with analyzing the objective＇s shape curvature to determine the bending position. Then it optimizes the punch travel at each bending position by the following steps： （1） Establish a multi-step press bend forming FEM equivalent model, with which the FEM ex- periments designed with the Taguchi method are performed. （2） Construct a back-propagation （BP） neural network response surface with the data from the FEM experiments. （3） Use the genetic algorithm to optimize the neural network response surface as the objective function. Finally, this method is verified by press bending a complicated double-curvature grid-type stiffened panel and bears out its effectiveness and intrinsic worth in designing the press bend forming path.展开更多
基金supported by the National Natural Science Foundation of China(No.50609028)
文摘Because of complexity and non-predictability of the tunnel surrounding rock,the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering.During design,it is a frequent practice,therefore,to give recommended values by analog based on experience.It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying,expressing and coping with such complex non-linear relationships.The parameters can be verified by searching the optimal network structure,using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results.In the current paper,the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua(FLAC3D.The high non-linearity,network reasoning and coupling ability of the neural network are employed.The output vector required of the training of the neural network is obtained with the numerical analysis software.And the overall space search is conducted by employing the Adaptive Immunity Algorithm.As a result,we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum.At the same time,the computing speed and efficiency are increased as well.Further,in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project.The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data.This is of practical significance to the appraisal of stability and informationization design of the surrounding rock.
基金Supported by National Outstanding Youth Science Foundation of China(No.41025015)the National Natural Science Foundation of China(No.41274109)Sichuan Youth Science and Technology Innovation Research Team(No.2011JTD0013)
文摘In energy dispersive X-ray fiuorescence(EDXRF), quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, a hybrid approach of genetic algorithm(GA) and back propagation(BP) neural network is proposed without considering the complex relationship between the elemental content and peak intensity. The aim of GA-optimized BP is to get better network initial weights and thresholds. The starting point of this approach is that the reciprocal of the mean square error of the initialization BP neural network is set as the fitness value of the individuals in GA; and the initial weights and thresholds are replaced by individuals, then the optimal individual is searched by selecting, crossover and mutation operations, finally a new BP neural network model is established with the optimal initial weights and thresholds. The quantitative analysis results of titanium and iron contents in five types of mineral samples show that the relative errors of 76.7% samples are below 2%, compared to chemical analysis data, which demonstrates the effectiveness of the proposed method.
文摘This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity in software risks, the method of principal components analysis is adopted in the model to enhance network stability.To solve uncertainty of the neural networks structure and the uncertainty of the initial weights, genetic algorithms is employed.The experimental result reveals that the precision of software risk analysis can be improved by using the erhanced neural networks model.
文摘. This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet coefficients of the noisy signal to estimate the discontinuity of hard threshold function and soft threshold function, limiting its further application in order to overcome this shortcoming, this paper proposes a new threshold function, compared with the original threshold function, a new threshold function is simple and easy to calculate, not only with the soft threshold function is continuous. To deal with this drawback, we integrate the NN to enhance the model. Neural network belongs to the basic unsupervised learning of neural networks, the principle of competition based on the mechanism of learning and biological and the memory capacity can be increased as the number of learning patterns increases, not only offi ine learning can also be carried out on-line ＂learning while learning＂ type. The integrated algorithm can host better performance.
文摘This study involves the development of an analytical model for understanding the behavior of the extended, stiffened end-plate moment connections with eight high strength bolts. Modeling of the connection as an assemblage of finite elements (FE) used for load deformation analysis, with material, and contact nonlinearities are developed. Results from the FE mathematical model are verified with results from the ANSYS computer program as well as with the test results. Sensitivity and feasibility studies are carried out. Significant geometry and force related variables are introduced;and by varying the geometric variables of the connections within a practical range, a matrix of test cases is obtained. Maximum end-plate separation, maximum bending stresses in the end-plate, and the forces from the connection bolts for these test cases are obtained. From the FE analysis, a database is produced to collect results for the artificial neural network analysis. Finally, salient features of the optimized Artificial Neural Network (ANN) via Genetic Algorithm (GA) analysis are introduced and implemented with the aim of predicting the overall behavior of the connection.
文摘In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration.The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound(UB)limit analyses,facilitating an in-depth examination of various material and geometric conditions.A hybrid deep neural network,specifically the Whale Optimization Algorithm-Deep Neural Network(WOA-DNN),is then employed to utilize these 10,000 outputs for precise bearing capacity predictions.Notably,the WOA-DNN model outperforms conventional ML techniques,offering a robust and accurate prediction tool.This innovative approach explores a broad range of design parameters,including sand layer depth,load-to-soil unit weight ratio,internal friction angle,cohesion,and footing roughness.A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity,providing valuable insights for practical foundation design.This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles,marking a significant stride in geotechnical engineering advancements.
文摘Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.
基金This paper is about a project financed by the Research Fund for Doctoral Program of Higher Education (No. 20040079008).
文摘Neural network has the abilities of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. This paper presents a solution for overcoming these shortages from two aspects. One is to adopt principle component analysis to select study samples and make some of them contain sample characteristics as many as possible, the other is to train the network using Levenberg-Marquardt backward propagation algorithm. This new method was proved to be valid and practicable in site selection of practical garbage power generation plants.
基金co-supported by the National Natural Science Foundation of China (No. 51275138)the Science Foundation of Heilongjiang Provincial Department of Education (No. 12531109)+1 种基金the funding of Hong Kong Scholars Programs (Nos. XJ2015002 and G-YZ90)China’s Postdoctoral Science Funding (No. 2015M580037)
文摘To reasonably implement the reliability analysis and describe the significance of influencing parameters for the multi-failure modes of turbine blisk, advanced multiple response surface method (AMRSM) was proposed for multi-failure mode sensitivity analysis for reliability. The mathematical model of AMRSM was established and the basic principle of multi-failure mode sensitivity analysis for reliability with AMRSM was given. The important parameters of turbine blisk failures are obtained by the multi-failure mode sensitivity analysis of turbine blisk. Through the reliability sensitivity analyses of multiple failure modes (deformation, stress and strain) with the proposed method considering fluid-thermal-solid interaction, it is shown that the comprehensive reliability of turbine blisk is 0.9931 when the allowable deformation, stress and strain are 3.7 x 10(-3) m, 1.0023 x 10(9) Pa and 1.05 x 10(-2) m/m, respectively; the main impact factors of turbine blisk failure are gas velocity, gas temperature and rotational speed. As demonstrated in the comparison of methods (Monte Carlo (MC) method, traditional response surface method (RSM), multiple response surface method (MRSM) and AMRSM), the proposed AMRSM improves computational efficiency with acceptable computational accuracy. The efforts of this study provide the AMRSM with high precision and efficiency for multi-failure mode reliability analysis, and offer a useful insight for the reliability optimization design of multi-failure mode structure. (C) 2016 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
基金financially supported by the Natural Science Foundation of Hunan Province(2021JJ30679)。
文摘Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R^(2)in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy.
文摘The driver’s cognitive and physiological states affect his/her ability to control the vehicle.Thus,these driver states are essential to the safety of automobiles.The design of advanced driver assistance systems(ADAS)or autonomous vehicles will depend on their ability to interact effectively with the driver.A deeper understanding of the driver state is,therefore,paramount.Electroencephalography(EEG)is proven to be one of the most effective methods for driver state monitoring and human error detection.This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades.First,the commonly used EEG system setup for driver state studies is introduced.Then,the EEG signal preprocessing,feature extraction,and classification algorithms for driver state detection are reviewed.Finally,EEG-based driver state monitoring research is reviewed in-depth,and its future development is discussed.It is concluded that the current EEGbased driver state monitoring algorithms are promising for safety applications.However,many improvements are still required in EEG artifact reduction,real-time processing,and between-subject classification accuracy.
文摘There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill load cell mass,SAG mill solid percentage,inlet and outlet water to the SAG mill and work index are studied.A total number of185full-scale SAG mill works are utilized to develop the artificial neural network(ANN)and the hybrid of ANN and genetic algorithm(GANN)models with relations of input and output data in the full-scale.The results show that the GANN model is more efficient than the ANN model in predicting SAG mill power.The sensitivity analysis was also performed to determine the most effective input parameters on SAG mill power.The sensitivity analysis of the GANN model shows that the work index,inlet water to the SAG mill,mill load cell weight,SAG mill solid percentage,mass flowrate and feed moisture have a direct relationship with mill power,while outlet water to the SAG mill has an inverse relationship with mill power.The results show that the GANN model could be useful to evaluate a good output to changes in input operation parameters.
文摘There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites.Today,customers throughout the world share their points of view on all kinds of topics through these sources.The massive volume of data created by these customers makes it impossible to analyze such data manually.Therefore,an efficient and intelligent method for evaluating social media data and their divergence needs to be developed.Today,various types of equipment and techniques are available for automatically estimating the classification of sentiments.Sentiment analysis involves determining people’s emotions using facial expressions.Sentiment analysis can be performed for any individual based on specific incidents.The present study describes the analysis of an image dataset using CNNswithPCA intended to detect people’s sentiments(specifically,whether a person is happy or sad).This process is optimized using a genetic algorithm to get better results.Further,a comparative analysis has been conducted between the different models generated by changing the mutation factor,performing batch normalization,and applying feature reduction using PCA.These steps are carried out across five experiments using theKaggledataset.The maximum accuracy obtained is 96.984%,which is associated with the Happy and Sad sentiments.
基金provided through research grant No.0035/2019/A1 from the Science and Technology Development Fund,Macao SARthe assistantship from the Faculty of Science and Technology,University of Macao。
文摘Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.
基金The authors received the sources of funding of a project,The Name:Special Project for Innovation and Entrepreneurship Education Reform in Hubei Province Colleges and Universities(2020),Item Number:136/5013602701.
文摘Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.
文摘An image consists of large data and requires more space in the memory. The large data results in more transmission time from transmitter to receiver. The time consumption can be reduced by using data compression techniques. In this technique, it is possible to eliminate the redundant data contained in an image. The compressed image requires less memory space and less time to transmit in the form of information from transmitter to receiver. Artificial neural net- work with feed forward back propagation technique can be used for image compression. In this paper, the Bipolar Coding Technique is proposed and implemented for image compression and obtained the better results as compared to Principal Component Analysis (PCA) technique. However, the LM algorithm is also proposed and implemented which can acts as a powerful technique for image compression. It is observed that the Bipolar Coding and LM algorithm suits the best for image compression and processing applications.
文摘In this work, austenitic stainless steel screws employed in a locking compression plate for veterinarian use were investigated. These types of implants are widely utilized in bone fractures healing. Two surgical screws were extracted due to the observation of slight superficial red rust colorizing on one of the screw implants, visual evidence of probable screw rusting. From the same implant, another screw was extracted simultaneously without visual evidence of rusting. In order to characterize and analyze the different behavior of both screws, the chemical composition was characterized by atomic absorption and energy dispersive X-ray spectroscopy (EDS) coupled to a scanning electron microscope (SEM). Also, the screws were studied by metallography, optical microscopy (OM), Vickers microhardness tests, and SEM analysis. On the other hand, a prospection for alloy chemical composition limits of these types of implants was performed based on the Schaeffler-Delong diagram and the ASTM F-138 standard. To analyze the effect of the chemical composition, heat treatment, microstructure, pitting resistance equivalent number (PRE) and stacking fault energy (SFE), a genetic algorithm (GA) and an artificial neural network (ANN) were used. In accordance with the elemental analysis, the surgical screws do not fulfill the ranges of the chemical composition established by the ASTM F-138 standard. Furthermore, there were found differences between the microstructures of the screws. In regard to the prospection, the results of GA and ANN support the proposed chemical composition region on the Schaeffler-Delong diagram. The corrosion failure was associated with severe plastic deformation and the presence of precipitates. The proposal can minimize the cause of failures in these types of austenitic stainless steel implants.
文摘To accurately depict the strong nonlinear relationship between the viscosity of propellant slurry and shear rate,premix time,and temperature,and to improve the prediction accuracy,based on the sample preparation and experimental measurement of a certain type of propellant,viscosity data under multiple working conditions were obtained as the basic data for the research.By comparing typicalmodels such as support vector regression and random forest,it was found that although the traditional BP neural network was superior to the both,its accuracy was still insufficient.Based on this,a BPmodel co-optimized by the SparrowSearch Algorithm(SSA)and theGenetic Algorithm(GA)is proposed.The global search of SSA and the local convergence of GA are utilized to conduct dual optimization of the initial weights and thresholds of the BP network,and the training is completed based on themeasured shear rate,temperature and time data.Further,the Box-Behnken response surface design is adopted to transformthe output of the neural network into a quadratic explicit function of viscosity and multiple factors.The results show that the SSA-GABP model achieves a determination coefficient of R^(2)=0.948,compared with R^(2)=0.628 for the traditional BP neural network,while the root mean square error(RMSE)is reduced from 1093.99 to 154.75.Within the key pouring viscosity range of 200–600 Pa⋅s,the prediction deviation remains within±5%,and the overall prediction variance is improved by more than 100%.The polynomial quantification obtained from the response surface reveals the dominant role of shear rate and its interaction with temperature and time.The fitting curve is more in line with the experimental trend than the traditional constitutive model.The constructed explicit function can be directly embedded in Computational Fluid Dynamics(CFD)for pouring process simulation and has good engineering application value.
基金Specialized Research Fund for the Doctoral Program of High Education of China (20091102110021)
文摘In order to find out the optimal press bend forming path in fabricating aircraft integral panels, this article proposes a new method on the basis of the authors＇ previous work. It is composed of the finite element method （FEM） equivalent model, the surface curvature analysis, the artificial neural network response surface and the genetic algorithm. The method begins with analyzing the objective＇s shape curvature to determine the bending position. Then it optimizes the punch travel at each bending position by the following steps： （1） Establish a multi-step press bend forming FEM equivalent model, with which the FEM ex- periments designed with the Taguchi method are performed. （2） Construct a back-propagation （BP） neural network response surface with the data from the FEM experiments. （3） Use the genetic algorithm to optimize the neural network response surface as the objective function. Finally, this method is verified by press bending a complicated double-curvature grid-type stiffened panel and bears out its effectiveness and intrinsic worth in designing the press bend forming path.
