数据仓库中数据的粒度划分

数据仓库在决策支持系统(DSS)中起着核心作用,而要创建一个好的数据仓库,构造合适的数据粒度模型是要解决的一个重要问题.通过对数据粒度划分方法的分析,结合实例给出了一个划分粒度级别的新思路,提出了解决不同级别间数据查询效率的基本方法.     

Techniques of Image Processing Based on Artificial Neural Networks

This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue, saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram, were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.     

基于SMA阻尼器的大跨桥梁地震位移控制分析

通过超弹性形状记忆合金(shape memory alloy,简称SMA)拉伸试验研究,建立了SMA四折线本构模型.利用SMA超弹滞回耗能特性,提出一种拉压型SMA阻尼器,并给出其最大输出控制力的计算公式.针对某七跨连续刚构桥,分析了桥梁伸缩缝在无限位器、安装钢棒限位器和安装超弹性SMA阻尼器3种情况下的地震位移.结果表明:安装SMA阻尼器后,伸缩缝处相对位移降低80.3%,中跨跨中水平位移和竖向位移分别降低78.8%和65.9%.因此,SMA阻尼器可以有效控制桥梁结构地震位移,对桥段相对位移的控制效果优于钢棒限位器.     

底部三层框架-抗震墙砖房抗震性能及抗震设计的探讨

综合国内底部一层框架—抗震墙砖房和底部两层框架—抗震墙砖房一些试验研究和通过几个算例探讨了底部三层框架—抗震墙结构的抗震性能,并结合工程实践中对底部一层框架—抗震墙砖房和底部两层框架—抗震墙砖房抗震设计中应注意的问题对这种结构的抗震设计给出提示。     

Synthetic seismograms of ground motion near earthquake fault using simulated Green’s function method

Seismograms near source fault were synthesized using the hybrid empirical Green＇s function method where the discretely simulated seismic waveforms are used for Green＇s functions instead of the observed waveforms of small earthquakes. The Green＇s function seismic waveforms for small earthquake were calculated by solving wave equation using the pseudo-spectral method with the staggered grid real FFT strategy under a detailed 2-D velocity structure in Kobe region. Magnitude and seismic moment of simulated Green＇s function waveforms were firstly determined by using the relationship between fault length and corner frequency of source spectrum. The simulated Green＇s function waveforms were employed to synthesize seismograms of strong ground motion near the earthquake fault. The synthetic seismograms of the target earthquake were performed based on the model with multiple source rupture processes. The results suggest that synthesized seismograms coincide well with observed seismic waveforms of the 1995 Hyogo-ken Nanbu earthquake. The simulated Green＇s function method is very useful for prediction of the strong ground motion in region without observed seismic waveforms. The present technique spreads application field of the empirical Green＇s function method.     

一株高产抗菌活性物质链霉菌的初步分类鉴定

对一株土壤中筛选出的可以产抗菌活性物质的链霉菌进行了研究，测定了该菌的抗菌谱，发现该菌株对多种植物病原性真菌及细菌有拮抗作用，但对植物病原性真菌的抑制效果低于植物病原性细菌.通过形态特征、培养特征、生理生化特性及抑菌谱等系列比较，发现该链霉菌菌株与链霉菌属的黑胡桃链霉菌(Streptomyces nogalater)的特征基本相符；通过16S?rDNA序列比对，发现该菌株16S?rDNA与黑胡桃链霉菌的16S?rDNA同源性达99％.根据多项分类原则和系统进化树的构建分析，将该菌株暂归入黑胡桃链霉菌类.     

局部核方法及其应用

核方法是机器学习中一种强有力的学习算法.多个领域的实践表明，通过将领域知识嵌入到核函数中，一般会得到比较好的学习效果. 从微分流形的观点讨论了机器学习中全局信息与局部信息的关系，并提出了一种嵌入局部有意义信息的核方法.文本分类的实验结果表明，与其它几个分类算法相比，它具有较高的性能.     

基于模糊划分的神经网络集成

基于模糊聚类思想,提出了一种神经网络集成方法.由训练数据的模糊聚类结果,把训练数据划分成相交子集,基于各子集生成集成的个体神经网络.由于各子集所包含的数据和数据的类别各不相同,因而个体神经网络性能和结构存在差异.子集个数确定集成中个体神经网络个数.另外,基于隶属度函数计算公式,提出了个体神经网络输出结论结合方法.理论分析和实验结果表明,此方法对模式分类能取得较好的效果.     

《中图法》(第四版)在ChIN资源导航系统中的应用

从分析ChIN资源导航系统入手,阐述了《中国图书馆分类法》学科聚类标准在化学化工资源导航系统中的应用,并就此提出改进网络分类体系的两点建议:一、把学科聚类从网络分类体系中单独列出;二、设置专业主题词表。     

一种基于序列挖掘的分类系统框架

为了有效地对序列数据进行分类，提出了一种集成分类挖掘和序列模式挖掘技术的分类系统框架(SPACS)．先采用一套约束和裁减策略，为每个分类挖掘频繁序列模式，并将其转换为分类序列规则(CSR)；再利用平均CSR匹配置信度和一个规则匹配算法构建有效的序列数据分类器．SPACS不需要在提取序列的特征后采用传统方法进行分类，可以直接利用从序列数据中提取出的频繁序列进行分类．实验结果表明，对于序列类型的数据的分类，SPACS比传统的决策树和关联分类方法具有更高的分类精度．