Application of neural network merging model in dam deformation analysis

In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the merging model is built based on the neural network BP algorithm and the traditional statistical model. The three models mentioned above are calculated and analyzed according to the long-term deformation observation data in Chencun Dam. The analytical results show that the average prediction accuracies of the statistical model and the BP neural network model are ~ 0.477 and ＋- 0.390 mm, respectively, while the prediction accuracy of the merging model is ~0. 318 mm, which is improved by 33% and 18% compared to the other two models, respectively. And the merging model has a better generalization ability and broad applicability.

The Fatherhood Premium： Mirage or Reality？ How Do Occupational Characteristics Matter

A differential fatherhood premium exists among selected groups of fathers of various socio-economic backgrounds. Besides marital status, residential arrangement, biological paternity, and other demographic dimensions, the mechanisms associated with fathers＇ structural characteristics of work that produce divergent labor market outcomes are less well understood. The authors leveraged the cases of productivity and specialization, family and responsibility, and compensating differentials and examined the impacts of work context and occupational characteristics on the pay gap between fathers and non-fathers Based on joint data from the 1997-2015 National Longitudinal Survey of Youth （NLSY97） and Occupational Information Network （O＇NET） （n -- 37,138）, the authors used fixed-effects models to estimate the associations between job features and the fatherhood premium. The regression results showed that occupations entailing competition and requiring on-the-job training are associated with more wage bonus; occupations offering inflexible schedule are associated with less wage gain These work-related characteristics further emphasize fathers＇ work structure and context, human capital accumulation, and work-family conflict. Overall, these findings are consistent with the theoretical perspectives of productivity and specialization and compensating differentials, adding new evidences to the structural explanations of the fatherhood premium.

Probabilistic Top-k Query:Model and Application on Web Traffic Analysis

Top-k ranking of websites according to traffic volume is important for Internet Service Providers(ISPs) to understand network status and optimize network resources. However, the ranking result always has a big deviation with actual rank for the existence of unknown web traffic, which cannot be identified accurately under current techniques. In this paper, we introduce a novel method to approximate the actual rank. This method associates unknown web traffic with websites according to statistical probabilities. Then, we construct a probabilistic top-k query model to rank websites. We conduct several experiments by using real HTTP traffic traces collected from a commercial ISP covering an entire city in northern China. Experimental results show that the proposed techniques can reduce the deviation existing between the ground truth and the ranking results vastly. In addition, we find that the websites providing video service have higher ratio of unknown IP as well as higher ratio of unknown traffic than the websites providing text web page service. Specifically, we find that the top-3 video websites have more than 90% of unknown web traffic. All these findings are helpful for ISPs understanding network status and deploying Content Distributed Network(CDN).

Trust Type Based Trust Bootstrapping Model of Computer Network Collaborative Defense

In the system of Computer Network Collaborative Defense(CNCD),it is difficult to evaluate the trustworthiness of defense agents which are newly added to the system,since they lack historical interaction for trust evaluation.This will lead that the newly added agents could not get reasonable initial trustworthiness,and affect the whole process of trust evaluation.To solve this problem in CNCD,a trust type based trust bootstrapping model was introduced in this research.First,the division of trust type,trust utility and defense cost were discussed.Then the constraints of defense tasks were analyzed based on game theory.According to the constraints obtained,the trust type of defense agents was identified and the initial trustworthiness was assigned to defense agents.The simulated experiment shows that the methods proposed have lower failure rate of defense tasks and better adaptability in the respect of defense task execution.

Consensus of Multi-Agent Systems with Prestissimo Scale-Free Networks

In this paper, the relations of the network topology and the moving consensus of multi-agent systems are studied. A consensus-prestissimo scale-free network model with the static preferential-consensus attachment is presented on the rewired link of the regular network. The effects of the static preferential-consensus BA network on the algebraic connectivity of the topology graph are compared with the regular network. The robustness gain to delay is analyzed for variable network topology with the same scale. The time to reach the consensus is studied for the dynamic network with and without communication delays. By applying the computer simulations, it is validated that the speed of the convergence of multi-agent systems can be greatly improved in the preferential-consensus BA network model with different configuration.

Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics

Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.

A statistical end-to-end performance model for networks with complex topologies

Network calculus provides new tools for performance analysis of networks, but analyzing networks with complex topologies is a challenging research issue using statistical network calculus. A service model is proposed to characterize a service process of network with complex topologies. To obtain closed-form expression of statistical end-to-end performance bounds for a wide range of traffic source models, the traffic model and service model are expanded according to error function. Based on the proposed models, the explicit end-to-end delay bound of Fractional Brownian Motion（FBM） traffic is derived, the factors that affect the delay bound are analyzed, and a comparison between theoretical and simulation results is performed. The results illustrate that the proposed models not only fit the network behaviors well, but also facilitate the network performance analysis.

A Design of the Network Security Evaluation System

The security evaluation for an information network system is an important management tool to insure its normal operation. We must realize the significance of the comprehensive network security risks. A network evaluation model and the algorithm are presented and adapt the hierarchical method to characterize the security risk situation. The evaluation method is used to evaluate the key nodes and the mathematics is used to analyze the whole network security situation. Compared with others, the method can automatically create a rule-based security evaluation model to evaluate the security threat from the individual security elements and the combination of security elements, and then evaluation the network situation. It is shown that this system provides a valuable model and algorithms to help to find the security rules, adjust the security

A distributed deadlock detection algorithm for mobile computing system

The mode of mobile computing originated from distributed computing and it has the un-idempotent operation property, therefore the deadlock detection algorithm designed for mobile computing systems will face challenges with regard to correctness and high efficiency. This paper attempts a fundamental study of deadlock detection for the AND model of mobile computing systems. First, the existing deadlock detection algorithms for distributed systems are classified into the resource node dependent (RD) and the resource node independent (RI) categories, and their corresponding weaknesses are discussed. Afterwards a new RI algorithm based on the AND model of mobile computing system is presented. The novelties of our algorithm are that: 1) the blocked nodes inform their predecessors and successors simultaneously; 2) the detection messages (agents) hold the predecessors information of their originator; 3) no agent is stored midway. Additionally, the quit-inform scheme is introduced to treat the excessive victim quitting problem raised by the overlapped cycles. By these methods the proposed algorithm can detect a cycle of size n within n-2 steps and with (n^2-n-2)/2 agents. The performance of our algorithm is compared with the most competitive RD and RI algorithms for distributed systems on a mobile agent simulation platform. Experiment results point out that our algorithm outperforms the two algorithms under the vast majority of resource configurations and concurrent workloads. The correctness of the proposed algorithm is formally proven by the invariant verification technique.

Virtual State Estimation Calculator Model for Three Phase Power System Network

The main objective of this research work is to develop a simple state estimation calculator in LabView for three phase power system network. LabView based state estimation calculator has been chosen as the main platform because it is a user friendly and easy to apply in power systems. This research work is intended to simultaneously acclimate the power system engineers with the utilization of LabView with electrical power systems. This proposed work will discuss about the configuration and the improvement of the intelligent instructional VI （virtual instrument） modules in power systems for state estimation solutions. In the proposed model state estimation has been carried out and model has been developed such that it can accommodate the latest versions of state estimation algorithm.

专利引用关系形成的解释框架:一个指数随机图模型视角

[目的/意义]近年来,围绕着专利引文网络结构特征的研究出现大量的研究成果,这些成果都从某种程度上折射出专利引文关系的形成受到来自属性特征之外关系特征的影响,而现有的以回归方法为基础的统计推断方法难以将这些因素纳入到分析框架中,因此,急需探索新的方法。[方法/过程]从关系形成视角,专利引用关系形成可表示三种广义的关系形成过程:自组织影响过程、自身属性影响过程、网络协变量影响过程,并建立关系形成过程与网络配置间的映射关系,最终,形成一整套可用于理解复杂专利引用关系形成问题的解释框架。[结果/结论]提出一整套可用于理解复杂专利引用关系形成问题的解释框架,该框架是未来进一步构建网络统计模型的理论基础,另外,解释框架包含丰富的网络配置项,预示着未来指数随机图模型在文献计量、科学网络分析上广阔的应用前景。

基于指数随机图模型的专利引用关系形成机制研究——以奈拉滨药物为例

[目的/意义]专利引用关系形成问题是理解创新网络的一个重要问题。传统的回归模型对观察对象设定的独立性假设,无法将网络的结构效应因素整合到模型中来提供综合性的统计推断。指数随机图模型(ERGM,Exponential Random Graph Model)是一种创新性的统计推断方法,它能够将属性特征、自组织特征以及网络协同特征三种特征综合起来观察。[方法/过程]以奈拉滨药物的专利引文网络作为研究对象,利用ERGM系统检验了影响专利引用关系的五种机制:专利属性的主效应;专利引用时间的差值效应;专利引用关系的聚敛效应;专利引用关系的传递效应;专利引用关系的网络协同效应。[结果/结论]五种机制都在奈拉滨药物的专利引用关系的形成过程发挥了作用。但三种效应对于奈拉滨药物的专利引用关系的形成作用最为显著:共享发明人关系协同效应、共享家族关系协同效应、传递效应。一些辅助机制也会对专利引文关系形成产生影响,如引文时滞、权利要求数量和参考文献数量。

基于人工智能的不孕症中医辨证模型的构建与应用

目的:建立基于人工智能的不孕症中医辨证模型,为不孕症中医智能辨证模型的构建与应用提供方法和依据。方法:检索中国期刊全文数据库、万方期刊数据库、中医智库、古今病案云平台,收集关于不孕症的中医名医病案300例,建立不孕症病案中医信息数据库,采用经过超参数调优的随机森林(RF)、支持向量分类(SVC)、K-近邻(KNN)、人工神经网络(ANN)及统计学注意力神经网络模型(SANN)对数据集进行量化分析,建立不孕症辨证模型,采用五折交叉验证对模型进行评价,评价指标包括Accuracy、Precision、Recall、F1。结果:不孕症中医四诊信息为输入变量共86项,输出变量为不孕症中医证型共6项。5种模型的拟合效果较好,Accuracy、Precision、Recall、F1值均在0.77以上;其中SANN模型的准确率、查准率与查全率最高,Accuracy、Precision、Recall、F1分别为0.90、0.90、0.91、0.90,均高于其他算法模型,其参数的中医解释基本符合中医诊断原理。结论:基于SANN算法模型建立的不孕症中医辨证模型具有良好的诊断能力,将人工智能应用于不孕症中医辨证模型的构建与临床应用方法学可行,且具有较高的准确率。

A note on testing conditional independence for social network analysis

In social network analysis, logistic regression models have been widely used to establish the relationship between the response variable and covariates. However, such models often require the network relationships to be mutually independent, after controlling for a set of covariates. To assess the validity of this assumption,we propose test statistics, under the logistic regression setting, for three important social network drivers. They are, respectively, reciprocity, centrality, and transitivity. The asymptotic distributions of those test statistics are obtained. Extensive simulation studies are also presented to demonstrate their finite sample performance and usefulness.