Isothermal compression of Ti-6Al-4V alloy was conducted in the deformation temperature range of 1093-1303 K, the strain rates of 0.001, 0.01, 0.1, 1.0, and 10.0 s-1, and the height reductions of 20%-60% with an interv...Isothermal compression of Ti-6Al-4V alloy was conducted in the deformation temperature range of 1093-1303 K, the strain rates of 0.001, 0.01, 0.1, 1.0, and 10.0 s-1, and the height reductions of 20%-60% with an interval of 10%. After compression, the effect of the processing parameters including deformation temperature, strain rate, and height reduction on the flow stress and the microstructure was investigated. The grain size of primary a phase was measured using an OLYMPUS PMG3 microscope with the quantitative metallography SISC IAS V8.0 image analysis software. A model of grain size in isothermal compression of Ti-6A1-4V alloy was developed using fuzzy neural net- work （FNN） with back-propagation （BP） learning algorithm. The maximum difference and the average difference between the predicted and the experimental grain sizes of primary a phase are 13.31% and 7.62% for the sampled data, and 16.48% and 6.97% for the non-sampled data, respectively. It can be concluded that the present model with high prediction precision can be used to predict the grain size in isothermal compression of Ti-6Al-4V alloy.展开更多
Parameter optimization of nodes communication is the foundation of underwater sensor networks.The packet size is an important indicator of the impact of communication performance.As a result,the optimal packet size se...Parameter optimization of nodes communication is the foundation of underwater sensor networks.The packet size is an important indicator of the impact of communication performance.As a result,the optimal packet size selection is a critical issue in improving the communication performance.This paper aims to make a model reflecting the communication characteristics as the optimization target,because underwater sensor networks have the characteristics of high time delay,high energy consumption and high bit error rate.Finally,simulation experiments and theory have demonstrated the effectiveness and timeliness of simultaneous perturbation stochastic approximation(SPSA) algorithm.展开更多
Ethernet network, standardized by IEEE 802.3, is vastly installed in Local Area Network (LAN) for cheaper cost and reliability. With the emergence of cost effective and enhanced user experience needs, the Quality of S...Ethernet network, standardized by IEEE 802.3, is vastly installed in Local Area Network (LAN) for cheaper cost and reliability. With the emergence of cost effective and enhanced user experience needs, the Quality of Service (QoS) of the underlying Ethernet network has become a major issue. A network must provide predictable, reliable and guaranteed services. The required QoS on the network is achieved through managing the end-to-end delay, throughput, jitter, transmission rate and many other network performance parameters. The paper investigates QoS parameters based on packet size to analyze the network performance. Segmentation in packet size larger than 1500 bytes, Maximum Transmission Unit (MTU) of Ethernet, is used to divide the large data into small packets. A simulation process under Riverbed modeler 17.5 initiates several scenarios of the Ethernet network to depict the QoS metrics in the Ethernet topology. For analyzing the result from the simulation process, varying sized packets are considered. Hence, the network performance results in distinct throughput, end-to-end delay, packet loss ratio, bit error rate etc. for varying packet sizes.展开更多
Growing actin networks provide the driving force for the motility of cells and intracellular pathogens. Based on the molecular-level processes of actin polymerization, branching, capping, and depolymerization, we have...Growing actin networks provide the driving force for the motility of cells and intracellular pathogens. Based on the molecular-level processes of actin polymerization, branching, capping, and depolymerization, we have developed a modeling framework to simulate the stochastic and cooperative behaviors of growing actin networks in propelling obstacles, with an emphasis on the size and shape effects on work capacity and filament orientation in the growing process. Our results show that the characteristic size of obstacles changes the protrusion power per unit length, without influencing the orientation distribution of actin filaments in growing networks. In contrast, the geometry of obstacles has a profound effect on filament patterning, which influences the orientation of filaments differently when the drag coefficient of environment is small, intermediate, or large. We also discuss the role of various parameters, such as the aspect ratio of obstacles, branching rate, and capping rate, in affecting the protrusion power of network growth.展开更多
We investigate the similarities and differences among three queue rules,the first-in-first-out(FIFO)rule,last-in-firstout(LIFO)rule and random-in-random-out(RIRO)rule,on dynamical networks with limited buffer size.In ...We investigate the similarities and differences among three queue rules,the first-in-first-out(FIFO)rule,last-in-firstout(LIFO)rule and random-in-random-out(RIRO)rule,on dynamical networks with limited buffer size.In our network model,nodes move at each time step.Packets are transmitted by an adaptive routing strategy,combining Euclidean distance and node load by a tunable parameter.Because of this routing strategy,at the initial stage of increasing buffer size,the network density will increase,and the packet loss rate will decrease.Packet loss and traffic congestion occur by these three rules,but nodes keep unblocked and lose no packet in a larger buffer size range on the RIRO rule networks.If packets are lost and traffic congestion occurs,different dynamic characteristics are shown by these three queue rules.Moreover,a phenomenon similar to Braess’paradox is also found by the LIFO rule and the RIRO rule.展开更多
Sizes of nuggets are often used to evaluate spot weld quality in production. This paper presents a neural estimator used to carry out non-destructive on-line analysis of spot weld quality in which trained ANN function...Sizes of nuggets are often used to evaluate spot weld quality in production. This paper presents a neural estimator used to carry out non-destructive on-line analysis of spot weld quality in which trained ANN functions to map dynamic resistance characteristics into sizes of spot weld nuggets and results confirm the validity of neural network for this type of application.展开更多
For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection o...For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection of hot-rolling control parameters was studied for microalloy steel by following the neural network principle. An experimental scheme was first worked out for acquisition of sample data, in which a gleeble-1500 thermal simolator was used to obtain rolling temperature, strain, stain rate, and stress-strain curves. And consequently the aust enite grain sizes was obtained through microscopic observation. The experimental data was then processed through regression. By using the training network of BP algorithm, the mapping relationship between the hotrooling control parameters (rolling temperature, stain, and strain rate) and the microstructural paramete rs (austenite grain in size and flow stress) of microalloy steel was function appro ached for the establishment of a neural network-based model of the austeuite grain size and flow stress of microalloy steel. From the results of estimation made with the neural network based model, the hot-rolling control parameters can be effectively predicted.展开更多
提出一种利用神经网络(ANN)模型获取不同特征尺寸静态随机存储器(static random access memory,SRAM)单粒子翻转截面数据的方法。利用SRAM单粒子翻转历史数据建立了SRAM单粒子翻转机器学习预测模型,并基于该模型研究了器件的电压相关性...提出一种利用神经网络(ANN)模型获取不同特征尺寸静态随机存储器(static random access memory,SRAM)单粒子翻转截面数据的方法。利用SRAM单粒子翻转历史数据建立了SRAM单粒子翻转机器学习预测模型,并基于该模型研究了器件的电压相关性。研究中系统梳理了来自不同文献重离子实验的历史数据,并开展重离子实验进行数据的补充。结合输出参数的特点对相关数据进行处理,在饱和截面与形状参数的基础上引入特征因子以表征不同器件之间的系统偏差,优化并训练了双隐层前馈神经网络,该网络能够准确预测SRAM单粒子翻转截面并探究单粒子翻转电压相关性的规律。利用神经网络丰富了不同特征尺寸SRAM在低电压下单粒子翻转截面数据,通过机理分析发现了灵敏体积减小是造成小尺寸器件单粒子翻转电压相关性增加的主要原因,器件的电压相关性与工作电压和LET值之间均存在指数关系等规律。展开更多
We propose a model of weighted networks in which the structural evolution is coupled with weight dynamics. Based on a simple merging and regeneration process, the model gives powel-law distributions of degree, strengt...We propose a model of weighted networks in which the structural evolution is coupled with weight dynamics. Based on a simple merging and regeneration process, the model gives powel-law distributions of degree, strength and weight, as observed in many real networks. It should be emphasized that, in our model, the nontrivial degree-strength correlation can be reproduced and in agreement with empirical data. Moreover, the size-growing evolution model is also presented to meet the properties of real-world systems.展开更多
An artificial neural network （ANN） model for predicting transformed mierostrueture in conventional rolling process and therrnomechanical controlled process （TMCP） is proposed. The model uses austenite grain size a...An artificial neural network （ANN） model for predicting transformed mierostrueture in conventional rolling process and therrnomechanical controlled process （TMCP） is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstructure evolution models, together with a measured cooling rate and chemical compositions as inputs and the ferrite grain size and ferrite fraction as outputs. The predicted re- suits show that the model can predict the transformed microstructure which is in good agreement with the measured one, and it is better than the empirical equations. Also, the effect of the alloying elements on transformed products has been analyzed by using the model. The tendency is the same as that in the reported articles. The model can be used further for the optimization of processing parameters, mierostructure and properties in TMCP.展开更多
The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation sys...The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation system becomes an important topic in the process of city construction. In this work, we study the structural and robustness properties of transportation networks and their sub-networks. We introduce a complementary network model to study the relevance and complementarity between bus network and subway network. Our numerical results show that the mutual supplement of networks can improve the network robustness. This conclusion provides a theoretical basis for the construction of public traffic networks, and it also supports reasonable operation of managing smart cities.展开更多
It is very important to estimate the basic parameters in helicopter preliminary design. Neural Network （NN） has the advantages in estimating accuracy and generalization over traditional methods. However, there are s...It is very important to estimate the basic parameters in helicopter preliminary design. Neural Network （NN） has the advantages in estimating accuracy and generalization over traditional methods. However, there are some difficulties in using NN, e.g., how to select a proper network structure and the number of hidden layers. In this paper, structure and connection weight of a three-layer NN are optimized by genetic algorithm, and the optimized network is applied to helicopter sizing. The proposed method can not only give an optimal NN structure and connection weight, but also reduce the prediction error and has the capability of self-learning when the latest data are available. Furthermore, this method can be easily applied to helicopter design systems.展开更多
基金financially supported by the National Natural Science Foundation of China (No.50975234)
文摘Isothermal compression of Ti-6Al-4V alloy was conducted in the deformation temperature range of 1093-1303 K, the strain rates of 0.001, 0.01, 0.1, 1.0, and 10.0 s-1, and the height reductions of 20%-60% with an interval of 10%. After compression, the effect of the processing parameters including deformation temperature, strain rate, and height reduction on the flow stress and the microstructure was investigated. The grain size of primary a phase was measured using an OLYMPUS PMG3 microscope with the quantitative metallography SISC IAS V8.0 image analysis software. A model of grain size in isothermal compression of Ti-6A1-4V alloy was developed using fuzzy neural net- work （FNN） with back-propagation （BP） learning algorithm. The maximum difference and the average difference between the predicted and the experimental grain sizes of primary a phase are 13.31% and 7.62% for the sampled data, and 16.48% and 6.97% for the non-sampled data, respectively. It can be concluded that the present model with high prediction precision can be used to predict the grain size in isothermal compression of Ti-6Al-4V alloy.
文摘Parameter optimization of nodes communication is the foundation of underwater sensor networks.The packet size is an important indicator of the impact of communication performance.As a result,the optimal packet size selection is a critical issue in improving the communication performance.This paper aims to make a model reflecting the communication characteristics as the optimization target,because underwater sensor networks have the characteristics of high time delay,high energy consumption and high bit error rate.Finally,simulation experiments and theory have demonstrated the effectiveness and timeliness of simultaneous perturbation stochastic approximation(SPSA) algorithm.
文摘Ethernet network, standardized by IEEE 802.3, is vastly installed in Local Area Network (LAN) for cheaper cost and reliability. With the emergence of cost effective and enhanced user experience needs, the Quality of Service (QoS) of the underlying Ethernet network has become a major issue. A network must provide predictable, reliable and guaranteed services. The required QoS on the network is achieved through managing the end-to-end delay, throughput, jitter, transmission rate and many other network performance parameters. The paper investigates QoS parameters based on packet size to analyze the network performance. Segmentation in packet size larger than 1500 bytes, Maximum Transmission Unit (MTU) of Ethernet, is used to divide the large data into small packets. A simulation process under Riverbed modeler 17.5 initiates several scenarios of the Ethernet network to depict the QoS metrics in the Ethernet topology. For analyzing the result from the simulation process, varying sized packets are considered. Hence, the network performance results in distinct throughput, end-to-end delay, packet loss ratio, bit error rate etc. for varying packet sizes.
基金supported by the National Natural Science Foundation of China (Grants 11321202, 11672268)the Zhejiang Provincial Natural Science Foundation of China (Grant LR16A020001)
文摘Growing actin networks provide the driving force for the motility of cells and intracellular pathogens. Based on the molecular-level processes of actin polymerization, branching, capping, and depolymerization, we have developed a modeling framework to simulate the stochastic and cooperative behaviors of growing actin networks in propelling obstacles, with an emphasis on the size and shape effects on work capacity and filament orientation in the growing process. Our results show that the characteristic size of obstacles changes the protrusion power per unit length, without influencing the orientation distribution of actin filaments in growing networks. In contrast, the geometry of obstacles has a profound effect on filament patterning, which influences the orientation of filaments differently when the drag coefficient of environment is small, intermediate, or large. We also discuss the role of various parameters, such as the aspect ratio of obstacles, branching rate, and capping rate, in affecting the protrusion power of network growth.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.71801066 and 71431003)the Fundamental Research Funds for the Central Universities of China(Grant Nos.PA2019GDQT0020 and JZ2017HGTB0186)
文摘We investigate the similarities and differences among three queue rules,the first-in-first-out(FIFO)rule,last-in-firstout(LIFO)rule and random-in-random-out(RIRO)rule,on dynamical networks with limited buffer size.In our network model,nodes move at each time step.Packets are transmitted by an adaptive routing strategy,combining Euclidean distance and node load by a tunable parameter.Because of this routing strategy,at the initial stage of increasing buffer size,the network density will increase,and the packet loss rate will decrease.Packet loss and traffic congestion occur by these three rules,but nodes keep unblocked and lose no packet in a larger buffer size range on the RIRO rule networks.If packets are lost and traffic congestion occurs,different dynamic characteristics are shown by these three queue rules.Moreover,a phenomenon similar to Braess’paradox is also found by the LIFO rule and the RIRO rule.
文摘Sizes of nuggets are often used to evaluate spot weld quality in production. This paper presents a neural estimator used to carry out non-destructive on-line analysis of spot weld quality in which trained ANN functions to map dynamic resistance characteristics into sizes of spot weld nuggets and results confirm the validity of neural network for this type of application.
文摘For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection of hot-rolling control parameters was studied for microalloy steel by following the neural network principle. An experimental scheme was first worked out for acquisition of sample data, in which a gleeble-1500 thermal simolator was used to obtain rolling temperature, strain, stain rate, and stress-strain curves. And consequently the aust enite grain sizes was obtained through microscopic observation. The experimental data was then processed through regression. By using the training network of BP algorithm, the mapping relationship between the hotrooling control parameters (rolling temperature, stain, and strain rate) and the microstructural paramete rs (austenite grain in size and flow stress) of microalloy steel was function appro ached for the establishment of a neural network-based model of the austeuite grain size and flow stress of microalloy steel. From the results of estimation made with the neural network based model, the hot-rolling control parameters can be effectively predicted.
文摘提出一种利用神经网络(ANN)模型获取不同特征尺寸静态随机存储器(static random access memory,SRAM)单粒子翻转截面数据的方法。利用SRAM单粒子翻转历史数据建立了SRAM单粒子翻转机器学习预测模型,并基于该模型研究了器件的电压相关性。研究中系统梳理了来自不同文献重离子实验的历史数据,并开展重离子实验进行数据的补充。结合输出参数的特点对相关数据进行处理,在饱和截面与形状参数的基础上引入特征因子以表征不同器件之间的系统偏差,优化并训练了双隐层前馈神经网络,该网络能够准确预测SRAM单粒子翻转截面并探究单粒子翻转电压相关性的规律。利用神经网络丰富了不同特征尺寸SRAM在低电压下单粒子翻转截面数据,通过机理分析发现了灵敏体积减小是造成小尺寸器件单粒子翻转电压相关性增加的主要原因,器件的电压相关性与工作电压和LET值之间均存在指数关系等规律。
基金Supported by the National 0utstanding Young Investigator Foundation of China under Grant No 70225005, the National Natural Science Foundation of China under Grant No 70471088.
文摘We propose a model of weighted networks in which the structural evolution is coupled with weight dynamics. Based on a simple merging and regeneration process, the model gives powel-law distributions of degree, strength and weight, as observed in many real networks. It should be emphasized that, in our model, the nontrivial degree-strength correlation can be reproduced and in agreement with empirical data. Moreover, the size-growing evolution model is also presented to meet the properties of real-world systems.
基金Item Sponsored by National Natural Science Foundation of China (50474086)Program for New Century Excellent Talents in University (NECT-04-0278)
文摘An artificial neural network （ANN） model for predicting transformed mierostrueture in conventional rolling process and therrnomechanical controlled process （TMCP） is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstructure evolution models, together with a measured cooling rate and chemical compositions as inputs and the ferrite grain size and ferrite fraction as outputs. The predicted re- suits show that the model can predict the transformed microstructure which is in good agreement with the measured one, and it is better than the empirical equations. Also, the effect of the alloying elements on transformed products has been analyzed by using the model. The tendency is the same as that in the reported articles. The model can be used further for the optimization of processing parameters, mierostructure and properties in TMCP.
基金Project supported by the Major Projects of the China National Social Science Fund(Grant No.11&ZD154)
文摘The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation system becomes an important topic in the process of city construction. In this work, we study the structural and robustness properties of transportation networks and their sub-networks. We introduce a complementary network model to study the relevance and complementarity between bus network and subway network. Our numerical results show that the mutual supplement of networks can improve the network robustness. This conclusion provides a theoretical basis for the construction of public traffic networks, and it also supports reasonable operation of managing smart cities.
文摘It is very important to estimate the basic parameters in helicopter preliminary design. Neural Network （NN） has the advantages in estimating accuracy and generalization over traditional methods. However, there are some difficulties in using NN, e.g., how to select a proper network structure and the number of hidden layers. In this paper, structure and connection weight of a three-layer NN are optimized by genetic algorithm, and the optimized network is applied to helicopter sizing. The proposed method can not only give an optimal NN structure and connection weight, but also reduce the prediction error and has the capability of self-learning when the latest data are available. Furthermore, this method can be easily applied to helicopter design systems.
