Customer Retention via Data Mining

``Customer Retention' is an increasingly pressing issue intoday's ever-competitive commercial arena. This is especially relevantand important for sales and services related industries. Motivated by areal-world problem faced by a large company, we proposed a solution thatintegrates various techniques of data mining, such as featureselection via induction, deviation analysis, and mining multipleconcept-level association rules to form an intuitive and novel approachto gauging customer loyalty and predicting their likelihood ofdefection. Immediate action triggered by these ``early-warnings'resulting from data mining is often the key to eventual customerretention.     

激光制导武器的发展现状与趋势

本文简要讨论了激光制导的原理及工作方式,对典型激光制导武器进行了详细介绍,通过分析激光制导武器的特点,重点描述了中外各国激光制导武器的发展现状,对激光武器的攻防战略及其发展趋势也作出了一定阐述。     

一种数据驱动的多故障诊断方法研究

结合统计建模与物理建模的指定元分析(DCA)有效地避免了主元分析的模式复合问题,从而能用来进行多故障诊断。针对非正交指定模式的处理问题,基于模式分组思想给出一种逐步DCA多故障诊断法,把常见变化模式分为几个正交模式组,然后关于各组指定模式,分别对所得观测数据阵或上一步DCA所得残差矩阵做DCA,逐步诊断各组中的故障是否发生。针对包含6种共存故障的观测数据的仿真研究表明,逐步DCA多故障诊断算法可有效地进行多故障诊断,且无需人为解释诊断结果的物理意义。     

Online face recognition system based on multi-view algorithm and Kernel PCA

An online face recognition system is presented in the paper. To online face recognition system, we should consider the recognition rate, the image compression and image size. In the paper we researched the innovation technologies for face recognition system, including Kernel Principal Component Analysis （Kernel PCA）, Delta low-pass wavelet filter, and face recognition algorithm using multiple images. Kernel PCA is derived to classify the characteristics of training images in the database. Delta low-pass wavelet filter is used to reduce the image size. A face recognition algorithm using multiple images is presented to improve the recognition rate. Simulation experiment shows that in the case of packet loss recognition rate is improved highly.     

Robust Classification for Imprecise Environments

In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.     

大型水轮机综合特性曲线处理的研究

水轮机设计的基础理论是相似理论。选型设计决定水轮机性能参数尺寸,其中最关键的技术是水轮机特性曲线的处理,即模型综合特性曲线和真机运动特性曲线的处理。本文应用CAD技术,提出了特性曲线处理算法,极大地提高了水轮机方案设计的效率。该算法已经应用于国家863／CIMS重点应用示范工程中。     

Evaluating increasing block tariff pricing policies when applied to multiple household connections

This paper develops a model for evaluating the consequences of increasing block tariff pricing policies when multiple households share a connection and applies it to a sample from Kumasi, Ghana. Results show that for any given consumption, a household's average share of total monthly water bill for shared connections can either be higher or lower than its single-household equivalent. Significant differences between the average and single-household equivalent bills were found in these cases, although this difference is under-estimated if the sample is not disaggregated by payment group.     

基于SoPC的双频激光回馈位移测量系统设计

介绍了一种基于SoPC的双频激光回馈位移测量系统的实现方法。该系统利用双频激光回馈理论和数字细分电路,对两路正交的光回馈信号采用五细分和四细分电路相结合进行细分处理,解决了单偏振光测量位移分辨率不高的问题。实验证明了本系统的可行性,精度达到了0.791 nm,是一种应用很好的位移测量系统。     

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.