Exploiting global information in complex network repair processes

Robustness of complex networks has been studied for decades,with a particular focus on network attack.Research on network repair,on the other hand,has been conducted only very lately,given the even higher complexity and absence of an effective evaluation metric.A recently proposed network repair strategy is self-healing,which aims to repair networks for larger compo nents at a low cost only with local information.In this paper,we discuss the effectiveness and effi ciency of self-healing,which limits network repair to be a multi-objective optimization problem and makes it difficult to measure its optimality.This leads us to a new network repair evaluation metric.Since the time complexity of the computation is very high,we devise a greedy ranking strategy.Evaluations on both real-world and random networks show the effectiveness of our new metric and repair strategy.Our study contributes to optimal network repair algorithms and provides a gold standard for future studies on network repair.     

基于人工神经网络的停机坪安全预警研究

停机坪是机场的重要组成部分,停机坪安全是机场安全关注的重点。影响停机坪安全作业的要素有很多,通过对这些要素进行监控和预警,可以减少停机坪的安全隐患,保证机场正常运行。根据济南遥墙机场的主要数据,运用因子分析法对影响停机坪安全的主要因素进行分类,提取公共因子。并运用人工神经网络（ANN）方法,对这些公共因子进行神经网络训练和预测分析,最终得到遥墙机场停机坪的安全情况。     

基于民航团队旅客销售的组合预测方法分析

利用Matlab分别用回归分析算法、BP神经网络算法、最小二乘支持向量机算法和组合预测算法对民航团队销售数据进行预测和比较分析,期望为民航销售人员提供更加精准的预测信息,以获得更高的航线收益.结果显示神经网络、支持向量机和组合预测3种算法比航空公司常用的回归分析预测精准度有了明显的提高.支持向量机预测精度相对神经网络稍低,却拥有更强的泛化能力.组合预测能避免单一预测方法的误差,更加适合航线销售人员的实际操作.     

地基深空网发射机现状及未来发展

我国未来深空探测工程的发展对地基深空站发射机提出了更高的要求,不仅仅需要具有更高的输出功率,同时需要解决发射机高频谱纯度、高效率以及上行功率合成等。针对这些要求,对国内外地基深空网的组成和深空站高功率发射机现状以及未来发展趋势进行了研究,分析了速调管发射机与固态发射机在高功率输出条件下的优势,在此基础上,提出了我国深空站速调管发射机需要开展高功率连续波速调管、高功率低损耗滤波器、低纹波高功率开关电源、上行功率合成等关键技术,并为相应关键技术提供了理论建议及相应的解决方法,这可以为我国未来深空探测工程高功率发射机研制提供技术支撑,为后续地基深空网建设和发展提供参考。     

低轨卫星通信系统地面核心网设计与仿真

文章针对无星际链路的低轨卫星通信系统，利用OPNET软件设计了低轨卫星通信系统在透明转发模式下的核心网构成，并进行了性能仿真。首先介绍核心网的构成、功能和拓扑，在此基础上实现了呼叫处理和切换的仿真，统计了呼叫时延、切换时延等性能指标，分析了仿真结果，为低轨道卫星通信系统的设计奠定了一定的参考价值。     

浅谈企业园区网的构建

以太网是当今应用最为普遍的局域网技术,本文描述了企业园区网建设的主要技术,进行了关于布线系统和网络组网的探讨。     

基于Hamming神经网络聚类分析的进化策略

本文提出了一种基于Ｈａｍｍｉｎｇ神经网络聚类分析的进化策略，模糊自适应Ｈａｍｍｉｎｇ神经网络各类族的权重矢量纪录被进化搜索过的区域，并相应妄下该区域内最优个体和它的适应度，因此通过Ｈａｍｍｉｎｇ神经网络对进化个体的聚类分析，进化策略具有搜索记忆性，可以充分保证下一代遗传群体中个体遗传基因的丰富性，从而避免早熟现象的发生，这种进化策略还可以避免在被搜索过的区域内的无用搜索，进而加快进化策略的收敛速度     

Training effectiveness evaluation of helicopter emergency relief based on virtual simulation

Helicopters are playing an increasingly important part in emergency relief, such as earthquake rescue, firefighting and medical transport. With the development of virtual simulation technology, virtual simulation-based training is widely used in the training of the helicopter crew especially for the dangerous and costly missions mentioned above. A complete training effectiveness evaluation method is proposed to evaluate the trainees’ training effect based on virtual simulation in this paper. A key to this method is regarding the complicated process as a discrete event-activity flow system and establishing the evaluation indicator system. Then expert group and Analytical Network Process (ANP) are applied to determine the weight of indicators. When the training data are processed, there is a novel attempt to apply Fuzzy Comprehensive Evaluation (FCE) model to calculate the two categories of indicators. Eventually, an experiment and the analysis were carried out to validate the evaluation method.     

Large-scale Coronal Heating, Clustering of Coronal Bright Points, and Concentration of Magnetic Flux

By combining quiet-region Fe XII coronal images from SOHO/EIT with magnetograms from NSO/Kitt Peak and from SOHO/MDI, we show that the population of network coronal bright points and the magnetic flux content of the network are both markedly greater under the bright half of the large-scale quiet corona than under the dim half. These results (1) support the view that the heating of the entire corona in quiet regions and coronal holes is driven by fine-scale magnetic activity (microflares, explosive events, spicules) seated low in the magnetic network, and (2) suggest that this large-scale modulation of the magnetic flux and coronal heating is a signature of giant convection cells. This revised version was published online in June 2006 with corrections to the Cover Date.     

非线性系统的动态神经网络自适应辨识

提出了用双层动态神经网络在线辨识非线性动态系统的方法。神经网络的权重在线学习，不需要离线训练。在无逼近误差和扰动的理想情况下，所提出的在线算法能保证辨识误差趋于零，基函数持续激励条件能保证权重趋于零。在非理想情况下，权重调整律采用ｅ修正权重算法，它是ＢＰ算法的推广，不需要基函数的持续激励条件。基于李雅普诺夫稳定性理论保证了自适应辨识系统的稳定性。仿真算例说明了所提出的动态神经网络自适应辨识的有效性