基于遗传算法的多方法协作优化方法

采用遗传算法、模式搜索法和Powell法进行多方法协作构成多方法协作优化方法,进行多方法协作优化方法的结构设计,分析多方法协作优化方法的优化性能。实例证明了多方法协作优化方法相对于遗传算法的优越性。     

分布式对象模型实践

随着组件、中间件等分布式技术的深入应用,分布式对象模型的设计受到了越来越广泛的关注。该文针对当前分布式对象模型设计中存在的问题,首先提出了远程接口和本地接口的概念,然后分析了一种基于RemoteFacade和DataTransferObject设计模式的解决方案,并详细介绍了基于DataTransferObject的应用解决方案,在文章的最后,提出了一种分布式对象模型的设计架构。     

基于设计模式的报表生成组件的设计与实现

文章对实际中应用的报表格式进行了分析、归类,探讨了各类格式报表的生成算法,同时根据对报表格式的分类,提出了一种基于策略模式的报表生成组件模型,并使用Delphi,采用COM技术,利用MicrosoftWord组件模型对这种报表生成组件模型对进行了实现。     

关联度与关系模式分解满足PBC且无α环的关系

一个数据库模式分解为满足保持函数依赖、无损联接性、BCNF且无α环是数据库理论和应用研究的重要问题之一。通过分析无α环的分解特性使我们得知,要达到无α环的分解,必须研究这种分解与关联度之间的关系。论文给出了这种关系。     

计算机病毒的保守模式分析

计算机病毒对计算机世界造成的威胁与日俱增。尽管各种防毒措施,如防病毒软件和入侵检测系统为人们提供了某些帮助,但它们对付计算机病毒的能力有限,存在着检测效率低、病毒的特征检测标识库经常发生变化等问题。该文针对这些问题,通过对计算机病毒的结构和运行机制的研究,提出了计算机病毒的保守模式的概念,并对这一概念进行了深入的分析,如病毒的搜索模式和自我复制模式等。这些模式的描述为病毒的检测和识别,尤其是新型病毒的有效检测提供了一条新的途径。     

分析服务组件模型在数据挖掘中的研究与应用

分析服务组件是SQLServer2000中专门用于进行数据挖掘的工具组件,它基于可视化的Windows开发平台。文中讨论了用分析服务组件进行数据挖掘的方法,介绍了用类似SQL的DDL构建了数据挖掘应用的一个实例。     

基于FP-tree的最大频繁模式挖掘算法

在FP-tree结构的基础上提出了最大频繁模式挖掘算法FP-Max。算法FP-Max只需要两次数据库扫描,挖掘过程不会产生候选项集。实验表明．算法FP-Max在挖掘密集型数据集方面是高效的。     

适于Internet新闻文本实时分类的动态向量空间模型DVSM

传统向量空间模型(VSM)特征间无关联,且不能动态增量训练,不适合主题和焦点实时变化的Internet新闻信息,为此提出了一种改进的文本实时分类模型——动态向量空间模型(DVSM)。通过对VSM的特征提取策略进行改进,提出了特征聚合和增量训练算法。通过将对分类有相同贡献的文本特征词聚合,使用它们共同的分类贡献向量特征模式作为文本特征向量的基本维;采用增量动态训练改变对分类贡献已改变的特征词在文本向量的特征模式中的位置,适应Internet新闻信息的实时特性。使用静态训练集和动态训练集进行的DVSM与传统VSM的对比实验表明,采用特征聚合和动态训练的DVSM在Internet新闻实时分类中优势效果明显优越。     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

How do the internet technological developments shift the consumption pattern of paper products? Evidence from China

Using a monthly data between 2008 and 2019, this study investigated how the development of the Internet technologies has shaped the consumer consumption pattern of different types of paper products in China during this period. In detail, this study covers four major types of paper products in the Chinese market: newsprint, printing paper, household paper, and packaging paper. The developments of Internet technologies were decomposed into two aspects: the communication technology (CT) and the software platform technology (PT). Granger causality analysis was employed to determine whether the consumption pattern of paper products was affected by the development of the Internet. Furthermore, impulse-response analyses were utilized to measure how the consumption pattern of each paper product was shaped by the development of each aspect of Internet technologies, i.e., CT or PT. The empirical results suggested that the Internet developments are causing the consumption patterns shifts of all four paper products. Specifically, the consumptions of newsprint and printing paper were driven by both CT and PT. However, the consumptions of packaging paper and household paper were driven by the PT only. Compared to the development of CT, we found that the quick evolution of PT plays a more significant role in shifting people's consumption of paper products in China. The empirical results revealed by this study could provide valuable economics and policy implications to both academia and relevant industries.