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!     

电信欺诈行为的聚类研究

针对海量电信数据的聚类问题,利用粗集中的知识简化方法,减少属性的数量,提取主要的特征属性,并结合性能优良的模糊Kohonen聚类网络,提出了一种新的电信欺诈行为的检测模型,采用Microsoft SQL2005和VC＋＋6.0技术,利用电信运营商提供的真实数据对该模型进行验证,实验结果表明,基于粗集神经网络方法提出的模型快速有效且具有较高的准确率。     

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。     

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.     

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.     

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

Cluster partition-based communication of multiagents: The model and analyses

In previous works, the agent communication always takes place randomly through the whole agents distributed area, and all agents are treated equally in the communication process. However, the real communication is always imbalanced: communication in some places of the system may be very busy and the communication resources are insufficient, whereas in other places the communication may be very idle and the communication resources are redundant. To make up the shortage of related works, this paper presents a cluster partition-based communication model for multiagents. In the presented model, agents are divided into some clusters according to their communication frequencies; in each cluster, one agent is set to be hub-agent which carries the information of other agents within the cluster. With the presented model, the communication costs within the same cluster is lower than the one among different clusters because agents have closer distances within the same cluster; moreover, many real communication processes may take place within clusters since the clusters are partitioned according to the communication frequencies among agents. Therefore, the communication time and costs can be reduced by adopting our model. Finally, the advantage of our model is proved by a series of experiments.