In this paper, according to the practical condition of local fixed telecom network, based on the method of the realistic total element long-run incremental cost, the practical methods of dividing the network elements,...In this paper, according to the practical condition of local fixed telecom network, based on the method of the realistic total element long-run incremental cost, the practical methods of dividing the network elements, calculating the cost of network elements and services are given, to provide reference for the cost calculation in telecom industry.展开更多
Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatme...Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatment parameters and materials properties,a 11×12×12×4 back-propagation（BP）artificial neural network（ANN）was set up.Alloying element contents,quenching and tempering temperatures were selected as input;hardness,tensile and yield strength were set as output parameters.The ANN shows a high fitting precision.The effects of alloying elements and heat treatment parameters on the properties of hot die steel were studied using this model.The results indicate that high temperature hardness increases with increasing alloying element content of C,Si,Mo,W,Ni,V and Cr to a maximum value and decreases with further increase in alloying element content.The ANN also predicts that the high temperature hardness will decrease with increasing quenching temperature,and possess an optimal value with increasing tempering temperature.This model provides a new tool for novel hot die steel design.展开更多
1. IntroductionA large number of networks for realizing first and second order transfer functions using a currentconveyor have been reported in the literature. Especially, the networks that can offer highinput impedan...1. IntroductionA large number of networks for realizing first and second order transfer functions using a currentconveyor have been reported in the literature. Especially, the networks that can offer highinput impedance attract attention, for high input impedance has the advantage that the networksmay be used in cascade without requiring impedance matching device. In the Higashimura and展开更多
Heterogeneity is an inherent component of rock and may be present in different forms including mineralheterogeneity, geometrical heterogeneity, weak grain boundaries and micro-defects. Microcracks areusually observed ...Heterogeneity is an inherent component of rock and may be present in different forms including mineralheterogeneity, geometrical heterogeneity, weak grain boundaries and micro-defects. Microcracks areusually observed in crystalline rocks in two forms： natural and stress-induced; the amount of stressinducedmicrocracking increases with depth and in-situ stress. Laboratory results indicate that thephysical properties of rocks such as strength, deformability, P-wave velocity and permeability areinfluenced by increase in microcrack intensity. In this study, the finite-discrete element method （FDEM）is used to model microcrack heterogeneity by introducing into a model sample sets of microcracks usingthe proposed micro discrete fracture network （mDFN） approach. The characteristics of the microcracksrequired to create mDFN models are obtained through image analyses of thin sections of Lac du Bonnetgranite adopted from published literature. A suite of two-dimensional laboratory tests including uniaxial,triaxial compression and Brazilian tests is simulated and the results are compared with laboratory data.The FDEM-mDFN models indicate that micro-heterogeneity has a profound influence on both the mechanicalbehavior and resultant fracture pattern. An increase in the microcrack intensity leads to areduction in the strength of the sample and changes the character of the rock strength envelope. Spallingand axial splitting dominate the failure mode at low confinement while shear failure is the dominantfailure mode at high confinement. Numerical results from simulated compression tests show thatmicrocracking reduces the cohesive component of strength alone, and the frictional strength componentremains unaffected. Results from simulated Brazilian tests show that the tensile strength is influenced bythe presence of microcracks, with a reduction in tensile strength as microcrack intensity increases. Theimportance of microcrack heterogeneity in reproducing a bi-linear or S-shape failure envelope and itseffects on the mechanisms leading to spalling damage near an underground opening are also discussed.展开更多
A new approach based on resonance technique and modified boundary ele-ment method is presented to calculate the impedance parameter matrix of a microwaveN-port network of waveguide structure.A two port network is take...A new approach based on resonance technique and modified boundary ele-ment method is presented to calculate the impedance parameter matrix of a microwaveN-port network of waveguide structure.A two port network is taken as a numerical ex-ample and the results show that the approach occupys the advantages of high accuracyand less computation effort.展开更多
Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studi...Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN) analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP) three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.展开更多
Exploiting mobile elements （MEs） to accomplish data collection in wireless sensor networks （WSNs） can improve the energy efficiency of sensor nodes, and prolong network lifetime. However, it will lead to large dat...Exploiting mobile elements （MEs） to accomplish data collection in wireless sensor networks （WSNs） can improve the energy efficiency of sensor nodes, and prolong network lifetime. However, it will lead to large data collection latency for the network, which is unacceptable for data-critical applications. In this paper, we address this problem by minimizing the traveling length of MEs. Our methods mainly consist of two steps： we first construct a virtual grid network and select the minimal stop point set （SPS） from it; then, we make optimal scheduling for the MEs based on the SPS in order to minimize their traveling length. Different implementations of genetic algorithm （GA） are used to solve the problem. Our methods are evaluated by extensive simulations. The results show that these methods can greatly reduce the traveling length of MEs, and decrease the data collection latency.展开更多
Fatigue life and reliability of aero-engine blade are always of important significance to flight safety.The establishment of damage model is one of the key factors in blade fatigue research.Conventional linear Miner＇...Fatigue life and reliability of aero-engine blade are always of important significance to flight safety.The establishment of damage model is one of the key factors in blade fatigue research.Conventional linear Miner＇s sum method is not suitable for aero-engine because of its low accuracy.A back propagation neutral network（BPNN） based on the combination of Levenberg-Marquardt（LM） and finite element method（FEM） is used to describe process of nonlinear damage accumulation behavior in material and predict fatigue life of the blade.Fatigue tests of standard specimen made from TC4 are carried out to obtain material fatigue parameters and S-N curve.A nonlinear continuum damage model（CDM）,based on the BPNN with one hidden layer and ten neurons,is built to investigate the nonlinear damage accumulation behavior,in which the results from the tests are used as training set.Comparing with linear models and previous nonlinear models,BPNN has the lowest calculation error in full load range.It has significant accuracy when the load is below 500 MPa.Especially,when the load is 350 MPa,the calculation error of the BPNN is only 0.4%.The accurate model of the blade is built by using 3D coordinate measurement technology.The loading cycle in fatigue analysis is defined from takeoff to cruise in 10 min,and the load history is obtained from finite element analysis（FEA）.Then the fatigue life of the compressor blade is predicted by using the BPNN model.The final fatigue life of the aero-engine blade is 6.55 104 cycles（10 916 h） based on the BPNN model,which is effective for the virtual design of aero-engine blade.展开更多
This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis （FEA） method is adopted to derive the d...This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis （FEA） method is adopted to derive the dynamics model of the system. A back propagation neural network （BPNN） based proportional-derivative （PD） algorithm is applied to suppress the vibration. Simulation and experiments are conducted using the FEA model and BPNN-PD control law. Experimental results show good agreement with the simulation results using finite element modeling and the neural network control algorithm.展开更多
The assay and recovery of rare earth elements （REEs） in the leaching process is being determined using expensive analytical methods： inductively coupled plasma atomic emission spectroscopy （ICP-AES） and inductive...The assay and recovery of rare earth elements （REEs） in the leaching process is being determined using expensive analytical methods： inductively coupled plasma atomic emission spectroscopy （ICP-AES） and inductively coupled plasma mass spectroscopy （ICP-MS）. A neural network model to predict the effects of operational variables on the lanthanum, cerium, yttrium, and neodymium recovery in the leaching of apatite concentrate is presented in this article. The effects of leaching time （10 to 40 min）, pulp densities （30% to 50%）, acid concentrations （20% to 60%）, and agitation rates （100 to 200 r/min）, were investigated and optimized on the recovery of REEs in the laboratory at a leaching temperature of 60℃. The obtained data in the laboratory optimization process were used for training and testing the neural network. The feed-forward artificial neural network with a 4-5-5-1 arrangement was capable of estimating the leaching recovery of REEs. The neural network predicted values were in good agreement with the experimental results. The correlations of R=l in training stages, and R=0.971, 0.952, 0.985, and 0.98 in testing stages were a result of Ce, Nd, La, and Y recovery prediction respectively, and these values were usually acceptable. It was shown that the proposed neural network model accurately reproduced all the effects of the operation variables, and could be used in the simulation of a leaching plant for REEs.展开更多
This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube s...This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.展开更多
Artificial neural network with the back-propagation (BP-ANN) approach was applied to the classification of normal persons and various cancer patients based on the elemental contents in serum samples. This method was ...Artificial neural network with the back-propagation (BP-ANN) approach was applied to the classification of normal persons and various cancer patients based on the elemental contents in serum samples. This method was verified by the cross-validation method. The effects of the net- work parameters were investigated and the related problems were discussed. The samples of 72, 42, and 52 for lung, liver, and stomach cancer patients and normal persons, respectively, were used for the classification study. About 95% of the samples can be classified correctly. There- fore, the method can be used as an auxiliary means of the diagnosis of cancer.展开更多
Wastewater treatment plants(WWTPs) are deemed reservoirs of antibiotic resistance genes(ARGs). Bacterial phylogeny can shape the resistome in activated sludge. However, the co-occurrence and interaction of ARGs abunda...Wastewater treatment plants(WWTPs) are deemed reservoirs of antibiotic resistance genes(ARGs). Bacterial phylogeny can shape the resistome in activated sludge. However, the co-occurrence and interaction of ARGs abundance and bacterial communities in different WWTPs located at continental scales are still not comprehensively understood. Here, we applied quantitative PCR and Miseq sequence approaches to unveil the changing profiles of ARGs(sul1, sul2, tet W, tet Q, tet X), int I1 gene, and bacterial communities in 18 geographically distributed WWTPs. The results showed that the average relative abundance of sul1 and sul2 genes were 2.08 × 10^(-1) and 1.32 × 10^(-1) copies/16 S rRNA copies, respectively. The abundance of tet W gene was positively correlated with the Shannon diversity index(H′), while both studied sul genes had significant positive relationship with the int I1 gene. The highest average relative abundances of sul1, sul2, tet X, and int I1 genes were found in south region and oxidation ditch system. Network analysis found that 16 bacterial genera co-occurred with tet W gene. Co-occurrence patterns were revealed distinct community interactions between aerobic/anoxic/aerobic and oxidation ditch systems. The redundancy analysis model plot of the bacterial community composition clearly demonstrated that the sludge samples were significant differences among those from the different geographical areas,and the shifts in bacterial community composition were correlated with ARGs. Together,these findings from the present study will highlight the potential risks of ARGs and bacterial populations carrying these ARGs, and enable the development of suitable technique to control the dissemination of ARGs from WWTPs into aquatic environments.展开更多
文摘In this paper, according to the practical condition of local fixed telecom network, based on the method of the realistic total element long-run incremental cost, the practical methods of dividing the network elements, calculating the cost of network elements and services are given, to provide reference for the cost calculation in telecom industry.
文摘Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatment parameters and materials properties,a 11×12×12×4 back-propagation（BP）artificial neural network（ANN）was set up.Alloying element contents,quenching and tempering temperatures were selected as input;hardness,tensile and yield strength were set as output parameters.The ANN shows a high fitting precision.The effects of alloying elements and heat treatment parameters on the properties of hot die steel were studied using this model.The results indicate that high temperature hardness increases with increasing alloying element content of C,Si,Mo,W,Ni,V and Cr to a maximum value and decreases with further increase in alloying element content.The ANN also predicts that the high temperature hardness will decrease with increasing quenching temperature,and possess an optimal value with increasing tempering temperature.This model provides a new tool for novel hot die steel design.
文摘1. IntroductionA large number of networks for realizing first and second order transfer functions using a currentconveyor have been reported in the literature. Especially, the networks that can offer highinput impedance attract attention, for high input impedance has the advantage that the networksmay be used in cascade without requiring impedance matching device. In the Higashimura and
文摘Heterogeneity is an inherent component of rock and may be present in different forms including mineralheterogeneity, geometrical heterogeneity, weak grain boundaries and micro-defects. Microcracks areusually observed in crystalline rocks in two forms： natural and stress-induced; the amount of stressinducedmicrocracking increases with depth and in-situ stress. Laboratory results indicate that thephysical properties of rocks such as strength, deformability, P-wave velocity and permeability areinfluenced by increase in microcrack intensity. In this study, the finite-discrete element method （FDEM）is used to model microcrack heterogeneity by introducing into a model sample sets of microcracks usingthe proposed micro discrete fracture network （mDFN） approach. The characteristics of the microcracksrequired to create mDFN models are obtained through image analyses of thin sections of Lac du Bonnetgranite adopted from published literature. A suite of two-dimensional laboratory tests including uniaxial,triaxial compression and Brazilian tests is simulated and the results are compared with laboratory data.The FDEM-mDFN models indicate that micro-heterogeneity has a profound influence on both the mechanicalbehavior and resultant fracture pattern. An increase in the microcrack intensity leads to areduction in the strength of the sample and changes the character of the rock strength envelope. Spallingand axial splitting dominate the failure mode at low confinement while shear failure is the dominantfailure mode at high confinement. Numerical results from simulated compression tests show thatmicrocracking reduces the cohesive component of strength alone, and the frictional strength componentremains unaffected. Results from simulated Brazilian tests show that the tensile strength is influenced bythe presence of microcracks, with a reduction in tensile strength as microcrack intensity increases. Theimportance of microcrack heterogeneity in reproducing a bi-linear or S-shape failure envelope and itseffects on the mechanisms leading to spalling damage near an underground opening are also discussed.
文摘A new approach based on resonance technique and modified boundary ele-ment method is presented to calculate the impedance parameter matrix of a microwaveN-port network of waveguide structure.A two port network is taken as a numerical ex-ample and the results show that the approach occupys the advantages of high accuracyand less computation effort.
基金Otokar Otomotiv ve Savunma Sanayi A.S. for the financial support
文摘Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN) analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP) three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.
基金supported by Tianjin Municipal Information Industry Office (No. 082044012)
文摘Exploiting mobile elements （MEs） to accomplish data collection in wireless sensor networks （WSNs） can improve the energy efficiency of sensor nodes, and prolong network lifetime. However, it will lead to large data collection latency for the network, which is unacceptable for data-critical applications. In this paper, we address this problem by minimizing the traveling length of MEs. Our methods mainly consist of two steps： we first construct a virtual grid network and select the minimal stop point set （SPS） from it; then, we make optimal scheduling for the MEs based on the SPS in order to minimize their traveling length. Different implementations of genetic algorithm （GA） are used to solve the problem. Our methods are evaluated by extensive simulations. The results show that these methods can greatly reduce the traveling length of MEs, and decrease the data collection latency.
基金supported by National Natural Science Foundation of China (Grant No. 60879002)Tianjin Municipal Science and Technology Support Plan of China (Grant No. 10ZCKFGX03800)
文摘Fatigue life and reliability of aero-engine blade are always of important significance to flight safety.The establishment of damage model is one of the key factors in blade fatigue research.Conventional linear Miner＇s sum method is not suitable for aero-engine because of its low accuracy.A back propagation neutral network（BPNN） based on the combination of Levenberg-Marquardt（LM） and finite element method（FEM） is used to describe process of nonlinear damage accumulation behavior in material and predict fatigue life of the blade.Fatigue tests of standard specimen made from TC4 are carried out to obtain material fatigue parameters and S-N curve.A nonlinear continuum damage model（CDM）,based on the BPNN with one hidden layer and ten neurons,is built to investigate the nonlinear damage accumulation behavior,in which the results from the tests are used as training set.Comparing with linear models and previous nonlinear models,BPNN has the lowest calculation error in full load range.It has significant accuracy when the load is below 500 MPa.Especially,when the load is 350 MPa,the calculation error of the BPNN is only 0.4%.The accurate model of the blade is built by using 3D coordinate measurement technology.The loading cycle in fatigue analysis is defined from takeoff to cruise in 10 min,and the load history is obtained from finite element analysis（FEA）.Then the fatigue life of the compressor blade is predicted by using the BPNN model.The final fatigue life of the aero-engine blade is 6.55 104 cycles（10 916 h） based on the BPNN model,which is effective for the virtual design of aero-engine blade.
基金Project supported by the Key Project(No.60934001)the General Projects(Nos.51175181and90505014)of the National Natural Science Foundation of Chinaby the Fundamental Research Funds for the Central Universities,SCUT(No.2012ZZ0060)
文摘This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis （FEA） method is adopted to derive the dynamics model of the system. A back propagation neural network （BPNN） based proportional-derivative （PD） algorithm is applied to suppress the vibration. Simulation and experiments are conducted using the FEA model and BPNN-PD control law. Experimental results show good agreement with the simulation results using finite element modeling and the neural network control algorithm.
文摘The assay and recovery of rare earth elements （REEs） in the leaching process is being determined using expensive analytical methods： inductively coupled plasma atomic emission spectroscopy （ICP-AES） and inductively coupled plasma mass spectroscopy （ICP-MS）. A neural network model to predict the effects of operational variables on the lanthanum, cerium, yttrium, and neodymium recovery in the leaching of apatite concentrate is presented in this article. The effects of leaching time （10 to 40 min）, pulp densities （30% to 50%）, acid concentrations （20% to 60%）, and agitation rates （100 to 200 r/min）, were investigated and optimized on the recovery of REEs in the laboratory at a leaching temperature of 60℃. The obtained data in the laboratory optimization process were used for training and testing the neural network. The feed-forward artificial neural network with a 4-5-5-1 arrangement was capable of estimating the leaching recovery of REEs. The neural network predicted values were in good agreement with the experimental results. The correlations of R=l in training stages, and R=0.971, 0.952, 0.985, and 0.98 in testing stages were a result of Ce, Nd, La, and Y recovery prediction respectively, and these values were usually acceptable. It was shown that the proposed neural network model accurately reproduced all the effects of the operation variables, and could be used in the simulation of a leaching plant for REEs.
基金financially supported by the National Natural Science Foundation of China(Grant No.51278217)
文摘This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.
基金Young Mainstay Teachers Foundation, Ministry of Education.
文摘Artificial neural network with the back-propagation (BP-ANN) approach was applied to the classification of normal persons and various cancer patients based on the elemental contents in serum samples. This method was verified by the cross-validation method. The effects of the net- work parameters were investigated and the related problems were discussed. The samples of 72, 42, and 52 for lung, liver, and stomach cancer patients and normal persons, respectively, were used for the classification study. About 95% of the samples can be classified correctly. There- fore, the method can be used as an auxiliary means of the diagnosis of cancer.
基金supported by the International Science and Technology Cooperation Program(No.2018KW-011)the National Key Research and Development Program of China(No.2016YFC0400706)+1 种基金the grant from "Young Outstanding Talents" in Universities of Shaanxi Provincesupport from the "Yanta Outstanding Youth Scholar" project from Xi'an University of Architecture and Technology(XAUAT)
文摘Wastewater treatment plants(WWTPs) are deemed reservoirs of antibiotic resistance genes(ARGs). Bacterial phylogeny can shape the resistome in activated sludge. However, the co-occurrence and interaction of ARGs abundance and bacterial communities in different WWTPs located at continental scales are still not comprehensively understood. Here, we applied quantitative PCR and Miseq sequence approaches to unveil the changing profiles of ARGs(sul1, sul2, tet W, tet Q, tet X), int I1 gene, and bacterial communities in 18 geographically distributed WWTPs. The results showed that the average relative abundance of sul1 and sul2 genes were 2.08 × 10^(-1) and 1.32 × 10^(-1) copies/16 S rRNA copies, respectively. The abundance of tet W gene was positively correlated with the Shannon diversity index(H′), while both studied sul genes had significant positive relationship with the int I1 gene. The highest average relative abundances of sul1, sul2, tet X, and int I1 genes were found in south region and oxidation ditch system. Network analysis found that 16 bacterial genera co-occurred with tet W gene. Co-occurrence patterns were revealed distinct community interactions between aerobic/anoxic/aerobic and oxidation ditch systems. The redundancy analysis model plot of the bacterial community composition clearly demonstrated that the sludge samples were significant differences among those from the different geographical areas,and the shifts in bacterial community composition were correlated with ARGs. Together,these findings from the present study will highlight the potential risks of ARGs and bacterial populations carrying these ARGs, and enable the development of suitable technique to control the dissemination of ARGs from WWTPs into aquatic environments.
