基于内容的三维模型检索被广泛运用于许多研究领域。为了弥补特征提取算法描述模型特征的不足，提高三维模型的检索效果，该文提出了一种基于先验知识的三维模型特征向量动态选择算法。该算法利用查询模型计算各种特征向量的先验知识，然后动态地选择描述能力较强的特征向量计算模型之间的相似度距离。实验采用标准的模型库Princeton shape benchmark （PSB）和多种公认的评价方法，结果显示该算法提高了三维模型的检索效果，优于现有的2种流行的三维模型特征选择算法。

As the need for personalized services sharply increases caused by the booming of Internet, Web-based data-mining is becoming a valuable sources of thoughts and theory to satisfy the personalized system function. The characters of personalized data-mining is reviewed and discussed in the beginning, and then an innovative algorithm (FP-Tree time-validity algorithm) of Web pageviews, based on personalization, is raised. More authentic information can be efficiently got by adding time-validity coefficient to FTT-Tree storage structure to implement increment mining.

This paper proposes Particle Swarm Optimization (PSO) algorithm to discover classification rules. The potential IF-THEN rules are encoded into real-valued particles that contain all types of attributes in data sets. Rule discovery task is formulized into an optimization problem with the objective to get the high accuracy, generalization performance, and comprehensibility, and then PSO algorithm is employed to resolveit. The advantage of the proposed approach is that it can be applied on both categorical data and continuous data. The experiments are conducted on two benchmark data sets: Zoo data set, in which all attributes are categorical, and Wine data set, in which all attributes except for the classification attribute are continuous. The results show that there is on average the small number of conditions per rule and a few rules per rule set, and also show that the rules have good performance of predictive accuracy and generalization ability.

针对存在配准偏差的双平台无源融合跟踪系统，提出了基于扩维Unscented 卡尔曼滤波的配准跟踪一体化方法，在跟踪算法中，采用模糊调度方法调节“当前”统计模型参数，引入渐消因子，能够在状态发生突变时，迅速调整系统参数，提高了系统的抗机动目标自适应能力。仿真结果表明，这种跟踪算法能够较好地解决双平台无源融合跟踪系统中的配准偏差问题。

Since 3D models have been widely applied in many research areas, the techniques for content-based 3D model retrieval become necessary. In this paper, a novel visual based 3D shape descriptor called MATE is proposed. A modi-ed Principal component analysis (PCA) method for model normalization is presented at-rst. Sec-ondly, a new Adjacent angle distance Fourier (AADF) al-gorithm is proposed. Then the original two-viewed Dbucrer method is presented to extract characteristics of projected images. Finally, based on the modi-ed PCA method, the shape descriptor MATE is proposed by combining AADF, Tchebichef and two-viewed Dbu®er. Experimental results show that the descriptor MATE provides better retrieval performance than the best current descriptors.