Effect of Temperature on Rheological Properties of Different Jams

The rheological behavior of selected jams was analyzed at different temperatures, from 20 to 40°C in a rotational viscosimeter (HAAKE VT550). The rheograms were fitted with Power-Law, Carreau, Herschel-Bulkley, and Cross models. It was observed that the jams presented a pseudoplastic behavior, and the suspended solids influenced the consistency index.     

基于类FP-tree的多层关联分类器

针对传统多层关联分类挖掘产生大量冗余规则而影响分类效率的问题,提出了一种基于类FP-tree的多层关联分类器MACCF(Multi-level Associative Classifier based on Class FP-tree)。该分类器依据事务的类标号划分训练集,采用闭频繁模式(CLOSET+)产生完全候选项目集,通过设计适当的类内规则剪枝策略和类间规则剪枝策略,减少了大量冗余的分类规则,提高了分类的准确率;采用交又关联规则方法,解决了交叉层数据的分类问题,实验结果 表明了算法的高效性。     

GMPLS中基于约束的分段保护算法

在总结、比较当前各种常见的GMPLS恢复保护机制性能的基础上,对多种路由算法思想进行了分析比较,并针对保护机制恢复时间、恢复机制的资源利用率,提出了基于约束的最短路径优先选择的改进算法.利用剪枝算法计算出一条具有约束条件的主路径,再结合LSP分段保护算法建立保护路径.由于保护路径比较短,因此能有效地节省资源,降低保护路径失败的概率,更快地激活保护路径,保证了可靠性.     

Compression of stereoscopic images using pyramid and prune DCT encoding

This paper details a compression system for stereoscopic (3-D) images that takes advantage of the disparity compensation effect by heavily compressing one image and a lightly compressing the other. Two methods were tried for the creation of the heavily compressed image: Lossy Pyramid coding and Pruned Discrete Cosine Transform (PDCT) with Variable Length Coding VLC. Both versions of the program used a lightly quantized PDCT image for the clear image.A detailed explanation of the algorithms, the effect of compression level, and the effect of compression method on stereoscopic perception is presented. A method of creating a pseudo 3-D image from a single image is also discussed.     

结构优化的RBF神经网络学习算法

文章提出了一种自动“删减”隐层神经元的ＲＢＦ神经网络学习算法。模拟结果表明，该算法训练的ＲＢＦ网络不仅结构得以优化，同时性能良好，可能成功地应用于模式分类和时间序列预测问题中。     

一种提高分类精度的支持向量机NDR-SVM

提出基于近邻域比率的支持向量机NDR-SVM.该算法对每个训练样本构造一个近邻域,在此邻域中计算与中心同类的样本占邻域中总样本的比率;根据比率与剔除闲值的大小关系决定邻域中心的取舍,并将所保留的样本带入SVM分类.通过修剪训练集,该算法减弱了噪声对SVM泛化能力的影响.实验结果表明,与已有算法相比,NDR-SVM具有更高的分类精度,大大提高了训练速度.     

GMPLS网络中基于约束的最短路径优先算法

对多种基于约束的最短路径优先算法设计思想进行了分析对比,选择了一种适用于GMPLS网络的路由算法,而且阐述了满足我们需求的设计方案。     

Bowel function of postmenopausal women: Effects of daily consumption of dried plum

Calcium and vitamin D were provided to 48 postmenopausal women for 6 months and randomly assigned into control, 50 g/d dried plum, or 100 g/d dried plum groups to examine dried plum influences on bowel habits. No adverse effects of dried plum were detected, whereas pain and constipation ratings increased (p<0.05) in the control group. Pain was higher (p=0.049) at 6 months for control versus 50 g/d dried plum and lower (p=0.042) at 3 months for 100 g/d dried plum versus 50 g/d. Constipation ratings were higher for control than 100 g/d dried plum at 6 months. Dried plum in the dose of 50 or 100 g per day did not produce adverse effect and may decrease discomfort of bowel movements.     

一种改进的相容关系聚类算法

聚类分析是数据挖掘中的一个重要研究内容。按照数据对象间的关系进行聚类在许多情况具有特殊的意义。提出一种相容关系数据对象的聚类算法。该算法首先对每个数据对象按字典排序,利用相容集的反单调性性质来产生极大相容簇,即通过相容集的连接产生更高层的相容集的候选,再通过剪枝的方法来得到更高层的相容集。该方法可以有效压缩算法的搜索空间,是现有相容关系聚类算法的有益改进和补充。     

PnP: sequential, external memory, and parallel iceberg cube computation

We present “Pipe ’n Prune” (PnP), a new hybrid method for iceberg-cube query computation. The novelty of our method is that it achieves a tight integration of top-down piping for data aggregation with bottom-up a priori data pruning. A particular strength of PnP is that it is efficient for all of the following scenarios: (1) Sequential iceberg-cube queries, (2) External memory iceberg-cube queries, and (3) Parallel iceberg-cube queries on shared-nothing PC clusters with multiple disks. We performed an extensive performance analysis of PnP for the above scenarios with the following main results: In the first scenario PnP performs very well for both dense and sparse data sets, providing an interesting alternative to BUC and Star-Cubing. In the second scenario PnP shows a surprisingly efficient handling of disk I/O, with an external memory running time that is less than twice the running time for full in-memory computation of the same iceberg-cube query. In the third scenario PnP scales very well, providing near linear speedup for a larger number of processors and thereby solving the scalability problem observed for the parallel iceberg-cubes proposed by Ng et al. Research partially supported by the Natural Sciences and Engineering Research Council of Canada. A preliminary version of this work appeared in the International Conference on Data Engineering (ICDE’05).