Wiki技术在山东中医药信息平台建设中的应用

wiki作为一种当前广泛应用于建立网络知识库的社会性软件,因其对结构化知识的积累作用,也越来越受到医疗工作者的关注.本文首先概述了wiki的概念及基本特点,对其在中医药信息平台中的可行性进行了分析,并指出在中医药信息平台中的应用形式.     

基于3种主流网络仿真软件的网络仿真探讨

网络仿真是一种新的网络规划和设计技术,它以极低的成本实现对复杂环境的再现与分析,能够提供虚拟的未来网络平台以进行研究。网络仿真软件在网络仿真中起着重要作用。对网络仿真技术的概念、特点、困难和应用流程等作了全面的论述;介绍了3种主流网络仿真软件:OPNET、NS和GloMoSim,比较了这3种仿真软件的特点及具体使用情况;最后,以学术界广泛使用的NS为例,说明软件仿真的流程。     

基于NS2的新协议的扩展与实现

通信网络的日新月异,各种网络方案和协议日趋复杂、网络规模日趋庞大,网络研究人员掌握网络仿真是很重要的。文章主要介绍网络仿真软件NS2软件包的构成、工作原理及NS仿真环境的搭建,并详细介绍了该软件使用中的难点问题。在此基础上以NS2为平台开发了一个新的协议EpApp,介绍了仿真过程中修改代码、拓扑构建,及其新协议的实现过程,为以后协议的实现奠定了基础。     

基于软件定义的以用户为中心的5G无线网络架构

5G无线接入网络是一种复杂的异构网络,各个协议之间的封闭性限制了网络性能.为了统一管理异构网络,向用户提供个性化的服务,提出了一种基于软件定义的以用户为中心的新型网络架构.该架构通过软件定义的形式统一管理所有无线网络,打破了传统蜂窝小区的概念,针对每个用户生成以用户为中心的小区,小区内的所有基站都能为该用户提供服务.这种新的网络架构能够为用户提供个性化的网络服务,提升用户的峰值速率和边缘用户性能.     

面向5G移动通信系统中SDP关键技术要点

5G移动通信具有多层次化网络结构与复杂网络环境,为合理规划网络逻辑拓扑结构,实现安全、高效的定制化组网和服务,本文提出了面向5G的软件定义边界(software defined perimeter,SDP)基础技术方案,介绍了SDP技术的相关概念,对SDP技术中基于零信任的网络隐身设计以及部署方式的关键技术进行了阐述,...     

软件定义FiWi网络中网络编码控制方法

针对传统FiWi(光纤无线混合接入)网络中网络编码控制过程中大量Gate、Report信息交换导致的网络信息复杂、编码时延增大等问题,设计了一种支持编码功能的软件定义FiWi网络架构和基于Openflow协议的网络编码控制方法。仿真结果表明,所提出的软件定义FiWi网络架构中的集中编码控制方法及其实现方案能够减少网络中控制信息数量,提高带宽利用率,有效降低数据传输时延并提高网络吞吐量。     

基于开源软件的开发者和项目网络分析

文章讨论了复杂的网络概念以及利用开源软件(OSS)数据构建的社会网络,对Berlios,GNU和SourceForge这3种不同开放源码软件的社会网络进行了初探。很多研究者已经在此类网络快照或合并视图上进行了大量研究,尤其是SourceForge,归结于其社区规模的庞大。SourceForge网络因具有在不同时间点的度分布、连通性、中心性和无标度等特性,被进行过大量的研究。然而,很少有人研究网络生长,即如何建立网络,尤其是指标是如何随时间演变的。     

基于复杂网络的软件复杂性度量研究

软件开发者对于日趋复杂的软件系统的理解和控制越来越困难，传统软件工程正接近其复杂性和可扩展性的极限．复杂性使软件开发困难，质量难以保证．复杂网络理论的最新研究成果，为软件复杂性度量提供了新的数学基础．讨论了软件复杂性的形成原因和度量方法，介绍了目前复杂网络与软件复杂性结合的研究工作．探讨了基于复杂网络的软件结构复杂性度量方法，提出一种结合复杂网络和演化算法的软件演化复杂性度量模型．     

SNS发展浅析

1 SNS简介 SNS(Social Networking Services/ software,社会性网络服务/软件)是指帮助人们建立社会性网络的瓦联网应用和服务.SNS是在六度分割理论基础上发展起来的,它是人们社会关系和web 2.0技术的强力结合,可以看作是非正式但无所不包的特性管理工具,它规定了通过社会关系可以访问个人信息.     

基于概念格的复杂网络重要节点评估

传统复杂网络重要节点评估存在评估准确率较低的问题,因此对基于概念格的复杂网络重要节点评估进行研究。以重要节点评估网络示意图为基础,对网络直接与平均距离进行计算,从而复杂网络节点中心度,实现对复杂网络重要节点的评估。根据模拟实验证明,基于概念格的复杂网络重要节点评估与传统方法相比,评估准确率提高14%。     

Small Data: Effective Data Based on Big Communication Research in Social Networks

Big data research is difficult because of its complex structure, vast data storage, and unpredictable change. Social network communication involves a significant amount of incomputable data created by wireless devices across the world. Such data can be used to analyze human activities, seek certain patterns using communication data, and predict emergencies. However, most data are effect of human to research our activities. So, recording effective node distribution and investigating the topological structure in communication are particularly important in big data communication. This study establishes a big data communication simulation environment by searching small data and calculating the influence of small data nodes. The experiment shows that 1% of small data can connect 75% of communication nodes and 20% of small data can transmit 80% of data packets.     

复杂网络特征量度及典型网络模型分析

复杂系统科学的发展目前仍处于萌芽阶段,但专家学者们广泛看好它的发展前景。近年来随着对复杂系统研究的不断深入,人们开始在不同领域进行从整体出发的复杂网络研究,从而形成了自成体系的复杂网络理论。首先介绍了复杂网络的概念,然后介绍了几种常用的复杂网络特征量度,之后总结了从复杂网络科学始创到今天,几种典型的网络模型,如随机网络模型、小世界网络模型、无标度网络模型等,并对这些模型进行了简单的分析。     

一种基于信息流的社交网络安全本体模型

社交网站作为互联网的新兴应用,近年来发展迅速。以Facebook和人人网为代表的社交网站现在已经拥有数以百万计的活动用户,创造了一种新的生活方式。但是社交网络快速发展的同时也带来了新的安全问题,如垃圾信息、隐私泄露、身份盗用等。文中尝试用本体来描述社交网络中的信息流动,并提供一个能够识别垃圾信息发送者的自动化解决方案。     

车载网运动模型的复杂网络解析方法

车载网快速移动的特点导致其结构具有强烈的复杂性,传统的网络研究方法无法对这种复杂性进行分析.针对此问题将复杂网络解析方法运用于车载网的研究之中,依据车载网组网过程建立车载网运动模型,通过复杂网络研究手段与运动模型相结合的方式对车载网的度分布、鲁棒性以及网络连接变化率进行分析与仿真实验.结果表明,所构建的运动模型能够反应车载网构网过程,复杂网络解析方法在车载网性质分析方面十分有效.     

Chaos Synchronization of Complex Network Based on Signal Superposition of Single Variable

Chaos synchronization of complex network with uncertain topological structure and coupling coefficient is used to study. By designing appropriate kinetic equation of network node, the chaos synchronization of the complex network is achieved. The unknown parameters and transported values of all the kinetic equations are identified simultaneously in the process of synchronization. When sets the parameter C^T for a specific value, the transported values of complex network node is the superposition of specific parameter of passed node. Lorenz system is taken for example to demonstrate the effectiveness of the presented method for a complex network of arbitrary topological type, and the dynamics analysis of the Lorenz chaotic system is given, the results we get including the Lyapunov exponents spectrum and its corresponding bifurcation diagram, and its corresponding analysis of SE complexity algorithm and C0 complexity algorithm are analysis briefly. In this paper, 0–1 test is given respectively. Discusses the influence of parameters on the synchronization performance. It is found that the synchronization performance of the complex network is very stable.     

Community detection and service discovery on Social Internet of Things

Due to the increasing growth of objects and problems such as increased traffic, overload, delay in response, and low search volume in the service discovery process in the complex Social Internet of Things (SIoT) environment, we provide an effective mechanism in the service discovery process by grouping objects based on common criteria that help us improve service search performance. In this article, we present a new method for clustering objects so that we can group objects that have common services and can work together. Hence, we create a set of different associations for the type of service and reciprocal cooperation of objects. With its help, instead of a global network search, we can perform service searches locally more efficiently and ensure the accuracy and correctness of searches and their answers. Then, we have provided a new mechanism for the service discovery process. In addition, we categorized communities based on their size to compare our proposed algorithm with other approaches using factors such as modularity in SIoT. Finally, we achieved sufficient efficiency in service discovery (86.81% and 88.28%) and demonstrated better performance of the proposed approach in identifying communities.     

结合ICA和复数神经网络的二麦阵列盲源分离技术

为了降低语音信号盲源分离算法的延时,提高其准确性和稳定性,本文结合传统盲源分离技术和深度神经网络的优势,提出了一种基于ICA独立分量分析和复数神经网络的二麦阵列盲源分离技术。本文将复数递归神经网络和独立分量分析方法有机融合,提出一种基于时频域的双通道复数神经网络,同时解决了独立分量分析中的排列问题。所提方法利输入混合信号利用复数域神经网络计算初始化分离矩阵,神经网络输出采用复数域形式,利用复数学习标签估计复数矩阵,然后采用独立分量分析方法获得目标分离矩阵。实验数据表明,所提方法相较于其它独立分量分析方法提高了盲源分离的实时性和准确性。      

Evaluating Twitter Data to Discover User’s Perception About Social Internet of Things

Social Internet of Things (SIoT) is a young paradigm that integrates Internet of Things and Social Networks. Social Internet of Things is defined as a social network of intelligent objects. SIoT has led to autonomous decision making and communication between object peers. SIoT has created and opened many research avenues in the recent years and it is vital to understand the impact of SIoT in the real world. In this paper, we have mined twitter to evaluate the user awareness and impact of SIoT among the public. We use R for mining twitter and perform extensive sentiment analysis using supervised and semi supervised algorithms to evaluate the user’s perception about SIoT. Experimental results show that the proposed Fragment Vector model, a semi supervised classification algorithm is better when compared to supervised classification algorithms namely Improved Polarity Classifier (IPC) and SentiWordNet Classifier (SWNC). We also evaluate the combined performance of IPC and SWNC and propose a hybrid classifier (IPC?+?SWNC). Our analysis was challenged by limited number of tweets with respect to our study. Experimental results using R has produced evidences of its social influences.     

Set pair three-way overlapping community discovery algorithm for weighted social internet of things

There are many problems in Social Internet of Things(IoTs), such as complex topology information, different degree of association between nodes and overlapping communities. The idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure. A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things (WSIoT-SPTOCD) is proposed. In the local network structure, which fully considers the topological information between nodes, the set pair connection degree is used to analyze the identity, difference and reverse of neighbor nodes. The similarity degree of different neighbor nodes is defined from network edge weight and node degree, and the similarity measurement method of set pair between nodes based on the local information structure is proposed. According to the number of nodes' neighbors and the connection degree of adjacent edges, the clustering intensity of nodes is defined, and an improved algorithm for initial value selection of k-means is proposed. The nodes are allocated according to the set pair similarity between nodes and different communities. Three-way community structures composed of a positive domain, boundary domain and negative domain are generated iteratively. Next, the overlapping node set is generated according to the calculation results of community node membership. Finally, experiments are carried out on artificial networks and real networks. The results show that WSIoT-SPTOCD performs well in terms of standardized mutual information, overlapping community modularity and F1.     

Information Leaders in Product Development Organizational Networks: Social Network Analysis of the Design Structure Matrix

Many models of Product Development (PD) are concerned with managing the decomposition and integration of tasks, teams and subsystems transforming a conceptual idea into a finished product. Specifically, a PD process is formed of cross-functional teams continuously exchanging information on specified tasks to integrate the product's final structure. Recently, it has been shown that large PD networks (e.g., tasks, teams, or components) follow a Scale Free structure. That is, each PD network included hubs that control information flow. Nevertheless, there is no literature on the implications of these findings on PD management. As a consequence, the objective of this paper is two-folded. First, we examine a set of mathematical measures such as centrality and brokerage used in Social Networks Analysis (SNA) to identify critical players in PD networks. Second, we link these findings to insights and recommendations for the management of complex PD organizational networks; in particular, detection and role designation of information leaders based on the given PD network structure