An Estimation Method for Relationship Strength in Weighted Social Network Graphs

Previous works mainly focused on estimating direct relationship strength in social networks. If two users are not directly connected in a social network, there is no direct relationship. In order to estimate the relationship strength between two indirectly connected users as well as directly connected users, this paper proposes an estimation method for relationship strength in weighted social network graphs, which is based on the trust propagation strategy and the estimation of direct relationship strength. Our method considers the length of a relationship path, the number of relationship paths and the edge weights (direct relationship strength) along with a relationship path to estimate the strength of indirect relationship. Then it synthesizes the direct and indirect relationship strength to represent the strength of relationship between two users in social net- works. Thus our method can fully estimate the relationship strength between any two users in a social network no matter whether they are directly connected or not.

Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression

An artificial neural network(ANN) constitutive model is developed for high strength armor steel tempered at 500 C, 600 C and 650 C based on high strain rate data generated from split Hopkinson pressure bar(SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnsone Cook(Je C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stressestrain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures(500e650 C), strains(0.05e0.2) and strain rates(1000e5500/s) are employed to formulate Je C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient(R) and average absolute relative error(AARE). R and AARE for the Je C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.

Fuzzy neural network analysis on gray cast iron with high tensile strength and thermal conductivity

To develop a high performance gray cast iron with high tensile strength and thermal conductivity, multivariable analysis of microstructural effects on properties of gray cast iron was performed. The concerned parameters consisted of graphite content, maximum graphite length, primary dendrite percentage and microhardness of the matrix. Under the superposed influence of various parameters, the relationships between thermal conductivity and structural characteristics become irregular, as well as the effects of graphite length on the strength. An adaptive neuro-fuzzy inference system was built to link the parameters and properties. A sensitivity test was then performed to rank the relative impact of parameters. It was found that the dominant parameter for tensile strength is graphite content, while the most relative parameter for thermal conductivity is maximum graphite length. The most effective method to simultaneously improve the tensile and thermal conductivity of gray cast iron is to reduce the carbon equivalent and increase the length of graphite flakes.

Strength dynamics of weighted evolving networks

In this paper, a simple model for the strength dynamics of weighted evolving networks is proposed to characterize the weighted networks. By considering the congestion effects, this approach can yield power law strength distribution appeared on the many real weighted networks, such as traffic networks, internet networks. Besides, the relationship between strength and degree is given. Numerical simulations indicate that the strength distribution is strongly related to the strength dynamics decline. The model also provides us with a better description of the real weighted networks.

Prediction of Sintering Strength for Selective Laser Sintering of Polystyrene Using Artificial Neural Network

In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser sintering( SLS) of polystyrene( PS). Artificial neural network( ANN) methodology is employed to develop mathematical relationships between the process parameters and the output variable of the sintering strength. Experimental data are used to train and test the network. The present neural network model is applied to predicting the experimental outcome as a function of input parameters within a specified range. Predicted sintering strength using the trained back propagation( BP) network model showed quite a good agreement with measured ones. The results showed that the networks had high processing speed,the abilities of error-correcting and self-organizing. ANN models had favorable performance and proved to be an applicable tool for predicting sintering strength SLS of PS.

A Double Network Hydrogel with High Mechanical Strength and Shape Memory Properties

Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into the conventional tough DN hydro-gel system.We synthesize the PEG-PDAC/P(AAm-co-AAc)DN hydrogels,of which the first network is a well-defined PEG(polyethylene glycol)network loaded with PDAC(poly(acryloyloxyethyltrimethyl ammonium chloride))strands,while the second network is formed by copolymerizing AAm(acrylamide)with AAc(acrylic acid)and cross-linker MBAA(N;N′-methylenebisacrylamide).The PEG-PDAC/P(AAm-co-AAc)DN gels exhibits high mechanical strength.The fracture stress and toughness of the DN gels reach up to 0.9 MPa and 3.8 MJ/m^3,respectively.Compared with the conventional double network hydrogels with neutral polymers as the soft and ductile second network,the PEG-PDAC/P(AAm-co-AAc)DN hydrogels use P(AAm-co-AAc),a weak polyelectrolyte,as the second network.The AAc units serve as the coordination points with Fe^3+ions and physically crosslink the second network,which realizes the shape memory property activated by the reducing ability of ascorbic acid.Our results indicate that the high mechanical strength and shape memory properties,probably the two most important characters related to the potential application of the hydrogels,can be introduced simultaneously into the DN hydrogels if the functional monomer has been integrated into the network of DN hydrogels smartly.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

Prediction of the residual strength of clay using functional networks

Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks（FN） using data available in the literature. The performance of FN was compared with support vector machine（SVM） and artificial neural network（ANN） based on statistical parameters like correlation coefficient（R）, Nash–Sutcliff coefficient of efficiency（E）, absolute average error（AAE）, maximum average error（MAE） and root mean square error（RMSE）. Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output.

An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash

In this study, an artificial neural network (ANN) model for studying the strength properties of steel fiber reinforced concrete (SFRC) containing fly ash was devised. The mixtures were prepared with 0 wt%, 15 wt%, and 30 wt% of fly ash, at 0 vol.%, 0.5 vol.%, 1.0 vol.% and 1.5 vol.% of fiber, respectively. After being cured under the standard conditions for 7, 28, 90 and 365 d, the specimens of each mixture were tested to determine the corresponding compressive and flexural strengths. The pa- rameters such as the amounts of cement, fly ash replacement, sand, gravel, steel fiber, and the age of samples were selected as input variables, while the compressive and flexural strengths of the concrete were chosen as the output variables. The back propagation learning algorithm with three different variants, namely the Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Fletcher-Powell conjugate gradient (CGF) algorithms were used in the network so that the best approach can be found. The results obtained from the model and the experiments were compared, and it was found that the suitable algorithm is the LM algorithm. Furthermore, the analysis of variance (ANOVA) method was used to determine how importantly the experimental parameters affect the strength of these mixtures.

Predicting model on ultimate compressive strength of Al_2O_3-ZrO_2 ceramic foam filter based on BP neural network

In present study, BP neural network model was proposed for the prediction of ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The inputs of the BP neural network model were the applied load on the epispastic polystyrene template (F), centrifugal acceleration (v) and sintering temperature (T), while the only output was the ultimate compressive strength (σ). According to the registered BP model, the effects of F, v, T on σ were analyzed. The predicted results agree with the actual data within reasonable experimental error, indicating that the BP model is practically a very useful tool in property prediction and process parameter design of the Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting.

Ultimate Compressive Strength Prediction for Stiffened Panels by Counterpropagation Neural Networks(CPN)

Stiffened Panels are important strength members in ship and offshore structures. A new method based on counterpropagation neural networks (CPN) is proposed in this paper to predict the ultimate compressive strength of stiffened panels. Compared with two-parametric polynomial, this method can take more parameters into account and make more use of experimental data. Numerical study is carried out to verify the validation of this method. The new method may find wide application in practical design.

Topological probability and connection strength induced activity in complex neural networks

Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world （SW） connections. Network elements are described by two-dimensional map neurons （2DMNs） with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied.

Modeling and analysis of porosity and compressive strength of gradient Al_2O_3-ZrO_2 ceramic lter using BP neural network

BP neural network was used in this study to model the porosity and the compressive strength of a gradient Al2O3-ZrO2ceramic foam f ilter prepared by centrifugal slip casting.The inf luences of the load applied on the epispastic polystyrene template(F),the centrifugal acceleration(v)and sintering temperature(T)on the porosity(P)and compressive strength(σ)of the sintered products were studied by using the registered three-layer BP model.The accuracy of the model was verif ied by comparing the BP model predicted results with the experimental ones.Results show that the model prediction agrees with the experimental data within a reasonable experimental error,indicating that the three-layer BP network based modeling is effective in predicting both the properties and processing parameters in designing the gradient Al2O3-ZrO2ceramic foam f ilter.The prediction results show that the porosity percentage increases and compressive strength decreases with an increase in the applied load on epispastic polystyrene template.As for the inf luence of sintering temperature,the porosity percentage decreases monotonically with an increase in sintering temperature,yet the compressive strength f irst increases and then decreases slightly in a given temperature range.Furthermore,the porosity percentage changes little but the compressive strength f irst increases and then decreases when the centrifugal acceleration increases.

Periodic synchronization of community networks with non-identical nodes uncertain parameters and adaptive coupling strength

In this paper, we propose a novel approach for simultaneously identifying unknown parameters and synchronizing time-delayed complex community networks with nonidentical nodes. Based on the LaSalle＇s invariance principle, a cri- teflon is established by constructing an effective control identification scheme and adjusting automatically the adaptive coupling strength. The proposed control law is applied to a complex community network which is periodically synchro- nized with different chaotic states. Numerical simulations are conducted to demonstrate the feasibility of the proposed method.

Mass concrete strength assessment method by Sonreb and Core combined method using artificial neural network

The Sonreb and Core (SRC) combined method is proposed to assess the concrete compression strength of mass concrete structures.Artificial neural network is employed together with the SRC combined method to obtain the optimal core number.The artificial neural network is trained based on data from different testing methods.The procedure of using artificial neural network to assess the concrete strength is described.It proves that the SRC combined method is superior in many aspects and artificial the presented neural network has a high efficiency and reliability.The combined method using artificial intelligence is promising in the strength assessment of mass concrete structures such as the dam,the anchor of the suspension bridge,etc.

基于拌和生产数据的BP神经网络混凝土抗压强度预测

为解决混凝土生产中抗压强度试验周期长及工程管理存在滞后性的问题,提出了一种基于混凝土拌和生产实时监控数据的BP神经网络混凝土抗压强度预测模型。以混凝土拌和生产中的8项物料生产称重数据和5项生产配比数据作为预测输入变量,建立200组混凝土拌和站生产监控数据和对应的抗压强度试验数据样本集,按照6∶2∶2比例划分为训练集、验证集和测试集;分别以C40配比混凝土拌和生产的8项物料称重数据和全部13项数据作为输入变量,进行混凝土28 d抗压强度预测,将预测结果与实际试验结果进行比较,验证所提出BP神经网络模型的预测效果。结果表明:所提出的BP神经网络混凝土强度预测模型能较好地实时预测混凝土28 d抗压强度,且相对误差优于利用7 d抗压强度试验数据估算值;8项物料称重数据作为输入变量的BP神经网络预测模型预测精度更好,平均绝对百分比误差为0.82%,均方根误差为0.52 MPa;利用不同拌和站C20配比、C30配比混凝土拌和生产监控数据对8项输入变量BP神经网络混凝土抗压强度预测模型进行适应性验证可知,其预测平均绝对误差均在0.5 MPa之内,平均绝对百分比误差均小于2%,与C40配比预测误差一致;该预测模型充分挖掘了混凝土拌和站生产实时监控数据的价值,实现了传统混凝土抗压试验结果提前化,对提高工程建设质量水平具有重要意义。

中高应变速率轧制AZ31镁合金板的抗拉强度预测

本文采集了不同轧制温度、应变速率以及压下量等三个工艺参数下中高应变速率轧制的AZ31镁合金抗拉强度的27组样本,通过惯性权重、学习因子的改进和引入变异操作对PSO-BP神经网络进行改进,并与BP、PSO-BP神经网络对比,进行抗拉强度的预测。结果表明:BP神经网络不能预测AZ31镁合金抗拉强度的非线性变化,PSO-BP神经网络和改进的PSO-BP(IPSO-BP)神经网络均能较好地预测AZ31镁合金抗拉强度的非线性变化;这三个模型中IPSO-BP神经网络预测最为准确,相较于PSO-BP神经网络,其平均绝对误差从15.3764降低至3.4288,平均相对误差从5.94%降低至1.32%,均方误差从251.3662降低至20.7199,相关系数从0.7753提高至0.8937;通过Pearson相关性计算判断出应变速率、压下量对抗拉强度的影响均大于轧制温度,而应变速率与抗拉强度呈负相关关系,压下量与抗拉强度呈正相关关系。

信息网络地位与产业结构升级

数字技术发展促使信息流动打破时空局限,形成信息网络并深刻影响经济主体效率。以往研究通过交通基础设施、企业生产等形成的城市网络,研究信息在城市之间流动对产业结构的影响,但这并非衡量城市信息网络的最直接方式。本文基于2011—2017年中国城市之间的日频百度搜索数据,构建了城市间信息网络,并用信息联系强度、信息多样性刻画信息网络地位特征,并讨论其对产业结构升级的影响。结果发现,信息联系强度和信息多样性促进了城市产业结构升级,且仅在中西部地区显著;省间信息网络作用大于省内;促进知识溢出、带动城市创业是信息网络产生影响的重要渠道。本文为中国构建网络空间命运共同体提供了经验依据,也为数字经济时代城市提升产业结构提供了可行路径。

基于RSSR融合RNGO-Elman神经网络的室内可见光定位

针对动态环境下基于接收信号强度的传统可见光定位方法定位精度低、稳定性差等问题,提出一种基于接收信号强度比的改进北方苍鹰算法(NGO)优化Elman神经网络(RNGOElman)的室内可见光定位系统。提出选择一个辅助参考点,将待测参考点与辅助参考点的接收信号强度比值和接收机的真实位置作为训练集数据,建立不受动态环境影响的指纹数据库。针对NGO算法收敛速度慢、容易陷入局部最优等问题,利用折射反向学习策略初始化种群,增加种群多样性,引入非线性权重因子来加快收敛速度,避免陷入局部最优。使用优化后的NGO算法来优化Elman神经网络的初始权值和阈值,构建RNGO-Elman动态定位预测模型。仿真结果表明,在4m×4m×3m的实验空间下,优化后的RNGO-Elman定位模型平均定位误差为1.34cm,定位精度相较于Elman定位算法、NGO-Elman定位算法分别提高了82%,21%。在LED发射功率波动时,基于RSSR的RNGO-Elman定位误差为1.29cm,1.38cm。所提可见光定位方法具有定位精度高、定位性能稳定等优点。

基于ANN的HVFAC拉伸性能预测评价

基于人工神经网络(ANN)预测混凝土拉伸性能,对研究混凝土开裂机制具有重要意义。基于团队实验数据获得不同粉煤灰掺量、骨胶比、水胶比和养护龄期下大掺量粉煤灰混凝土(HVFAC)的抗压强度、极限拉伸应变、抗拉强度和拉伸弹性模量数据,用均方根误差(RMSE)最小原则建立一种预测HVFAC拉伸性能的ANN模型,并用公开发表的文献数据对该预测模型可靠性进行分析评估。结果表明:模型预测结果与实验结果的相关系数均大于0.94,文献中的实验值与模型预测值的误差均在±20%以内,说明所建立的模型有较高的预测精度。基于ANN影响权重分析发现:骨胶比对HVFAC的抗压强度、极限拉伸应变和拉伸弹性模量的影响最大;对于HVFAC的拉伸性能,在早龄期时水胶比的影响程度较大,但随着龄期的延长,粉煤灰掺量的影响程度逐渐上升并超过水胶比。