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。

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。

Markov models have been widely utilized for modeling user web navigation behavior. In this paper, we propose a novel adaptive weighting hybrid-order Markov model-HFTMM for Web pre-fetching based on optimizing HTMM (hybrid-order tree-like Markov model). The model can minimize the number of nodes in HTMM and improve the prediction accuracy, which are two significant sources of overhead for web pre-fetching. The experimental results show that HFTMM excels HTMM in better predicting performance with fewer nodes.

In this paper, Particle Swarm Optimization (PSO) algorithm, a new promising evolutionary algorithm, is proposed to train Radial Basis Function (RBF) network related to automatic configuration of network architecture. Classification tasks on data sets: Iris, Wine, Newthyroid, and Glass are conducted to measure the performance of neural networks. Compared with a standard RBF training algorithm in Matlab neural network toolbox, PSO achieves more rational architecture for RBF networks. The resulting networks hence obtain strong generalization abilities.

In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user’s semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.