基于MATLAB/VB的网络故障诊断软件设计

将VB调用Matlab的方法应用到基于神经网络的网络故障诊断系统软件的设计中;以VB编写诊断系统的主界面,利用ActiveX技术调用Matlab神经网络工具箱完成对网络故障的诊断,将两者的优势有机结合,从而缩短了程序开发周期,减少了程序员的工作量;该软件具有良好人机交互界面,便于网管人员的学习和掌握,且实例验证表明利用VB调用Matlab方法开发的网络故障诊断系统,其诊断结果可靠,能够满足网络实时故障诊断的要求.     

3D牛仔

今年的纽约时装周期间上,牛仔专家G—Star为观众献上了一场精彩纷呈,以“沙漠”为主题的2011春夏牛仔大秀。本场秀在曼哈顿东区的Pier94展览中心举行,单条长形T台使观众们有足够的时间与全方位的角度来仔细揣摩每一件单品,而无论是大型木质ArCPant背景模型,还是T台中央的图形地毯均不断地强调着此系列的最精髓所在——3D立体!     

基于特征值的智能故障诊断技术研究

文章研究了基于特征值的智能故障诊断技术,具体阐述了特征值的提取方法,以及特征参数的建立方法及定义,并描述了智能故障诊断系统模型结构与开发策略。     

利用地面公网实现战术数据链远程通信研究

利用地面公共网络使地理位置远离的多个数据链系统无缝互连,可以支持和提高相对独立的多个战术数据信息链实现超视距的互操作测试和训练。文章分析了两种地面网络通信模式：虚拟专用局域网业务模式和应用层网关模式。比较两种模式应用特点,发现虚拟专用局域网业务模式可以将多个数据链网络连接成一个虚拟局域网,传输数据方便,但是应用层网关更加适合数据链接口的要求并且可以对传输数据进行处理和报文格式修改。     

关于模糊C-均值（FCM）聚类算法的改进

针对模糊C-均值（FCM）聚类算法的容易收敛于局部极值的不足,提出了一种改进的模糊FCM聚类算法,此新算法在聚类中心选取和优化过程中进行了充分的考虑,是一种用于确定最佳聚类数的聚类算法,并且利用了分阶段思想,结合动态直接聚类算法和标准聚类算法,来尽量避免模糊C-均值（FCM）聚类算法的不足。新算法与传统（FCM）聚类算法方法相比,提高了算法的寻优能力,并且迭代次数更少,在准确度上也有较大的提高,具有很好的实际应用价值。     

Using jump-diffusion modeling for valuing real options in infrastructure projects

In infrastructure financed projects, in order to attract private investors, host governments often provide some guarantees. This paper develops a value model of minimum revenue guarantee with multiple-exercise real options under the impact of the emergency incident. The model is applied to infrastructure financed projects using the minimum revenue guarantee under simulation. The simulation results indicate that, before quantifying the value of the minimum revenue guarantee, it is necessary to forecast the jump degree and intensity of the emergency incident, as well as prevent and control risks arising from such emergencies. Otherwise, underestimation of the guarantee value will occur and the government will have to bear huge debt in this condition. We also analyze the dependence of the guaranteed value on the minimum guaranteed revenue level, initial revenue and number of exercise rights. For various conditions, the diagrams of the guaranteed value are also presented.     

Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining

A data warehouse is an important decision support system with cleaned and integrated data for knowledge discovery and data mining systems. In reality, the data warehouse mining system has provided many applicable solutions in industries, yet there are still many problems causing users extra problems in discovering knowledge or even failing to obtain the real and useful knowledge they need. To improve the overall data warehouse mining process, we present an intelligent data warehouse mining approach incorporated with schema ontology, schema constraint ontology, domain ontology and user preference ontology. The structures of these ontologies are illustrated and how they benefit the mining process is also demonstrated by examples utilizing rule mining. Finally, we present a prototype multidimensional association mining system, which with intelligent assistance through the support of the ontologies, can help users build useful data mining models, prevent ineffective pattern generation, discover concept extended rules, and provide an active knowledge re-discovering mechanism.     

Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer

This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the multi-objective problem in order to find out all the non-dominated optima of this objective function. In order to produce a well distributed Pareto front, the master swarm is developed to cover gaps among non-dominated optima by using a local MOPSO algorithm. Moreover, in order to strengthen the capability locating multiple optima of the PSO, several improved techniques such as the Pareto dominance-based species technique and the escape strategy of mature species are introduced. The simulation results indicate that our algorithm is highly competitive to solving the multi-objective optimization problems.     

A conjugate bayesian approach for calculating process capability indices

Process capability indices measure the ability of a production process to produce items within specification limits. The calculation of process capability indices has been focusing on using traditional frequency approach, which requires a large sample size for an accurate estimation. In order to eliminate this defect of traditional frequency approach on multi-batch and low volume production, Bayesian approach was used. The conjugate Bayesian approach is chosen to estimate the process distribution parameters. The algorithm with these conjugate Bayes estimators is proposed for measuring the process capability for multi-batch and low volume production. A case study is presented to demonstrate how the approach can be applied to actual data collected in practice.     

Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method

Contaminant intrusion in a water distribution network is a complex but a commonly observed phenomenon, which depends on three elements – a pathway, a driving force and a contamination source. However, the data on these elements are generally incomplete, non-specific and uncertain. In an earlier work, Sadiq, Kleiner, and Rajani (2006) have successfully applied traditional Dempster–Shafer theory (DST) to estimate the “risk” of contaminant intrusion in a water distribution network based on limited uncertain information. However, the method used for generating basic probability assignment (BPA) was not very flexible, and did not handle and process uncertain information effectively. In this paper, a more pragmatic method is proposed that utilizes “soft” computing flexibility to generate BPAs from uncertain information. This paper compares these two methods through numerical examples, and demonstrates the efficiency and effectiveness of modified method.