An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.     

Factors influencing feature usage in work-related communication

Two studies are presented. The first aimed to identify possible barriers to the uptake and use of commonly available telephony features and to determine whether greater knowledge of features/access codes and availability of user manuals could increase feature usage under certain conditions. Results showed that feature-usage patterns were not affected by any of the manipulations, but that they were determined by specific job demands. Using Constantine and Lockwood's (1999) conceptualization of user roles, a method to support feature bundling decisions for specific target markets was developed and tested in the second study. The method - Strategic User Needs Analysis (SUNA) - was shown to yield a useful balance between high- and low-level information about selected roles. SUNA provided sufficient information to distinguish between PDA feature usage patterns of two similar target user groups as well as to suggest additional features each of the two target groups would find useful. The development of SUNA and observational findings of actual PDA usage are reported.     

MALL在中国的现状及其建筑设计要点

该文介绍了Mall的产生条件和国内发展概况，并结合实例分析，着重论述建筑设计方面的有关问题。     

固定背景下的随机波形提取模型

分析了当前对于从固定背景下的特征符号与背景符号相粘连的图表中提取信息所存在的问题，设计了适用于此类问题的方法模型。提出了一种在固定背景下通过背景符号检测和背景符号擦除来实现随机波形提取的方法。     

中原油田深井大井眼段钻井液工艺

濮深8井是中原油田在西部洼陷南部寻找接替储量的一口重点深探井,也是中原油田第一口井深达6000m的超深井。分析了濮深8井钻井液的配备及维护和处理技术,得出深井大井眼段钻井液工艺技术存在以下问题:井眼大,钻井液量及钻屑量多,处理时间长,处理剂用量多,处理频繁;返速低使钻井液清洗井眼的能力减弱;井下温度高使处理剂的作用明显降低,二氧化碳的侵入使钻井液性能变差。钻井液维护处理的较好方法是定时、定量补充处理剂胶液。固相控制是深井大井眼段钻井液工艺中的关键问题,必须配套并且有效地使用固控设备,以维护好钻井液性能。     

华北油田鄚东地区深井钻井液技术

鄚东地区钻井过程中经常发生垮塌、卡钻、井漏等复杂事故。该地区深井钻井采用了抑制性及封堵性较强的铵基沥青树脂高密度钻井液体系,并根据地层特点,对不同井段制定了相应的钻井液维护处理措施。结果表明,该钻井液体系抑制性好,抗温、抗污染能力强,性能稳定,成功地控制了复杂事故的发生,满足了该地区深层钻井的要求。     

指纹细节特征提取与剪枝

指纹图像经预处理后，由于各种原因，会产生数以百计的特征。为了图像匹配的精确和快速，提出一套在细化后的指纹点线图上提取细节特征并对其剪枝的方法，并根据细节特征的拓扑特性，设计了验证其可靠性的算法。通过实验，证明了该后处理过程在去除指纹图像伪特征方法面具有良好的效果。     

三聚甲醛萃取—精馏新工艺及过程优化

本文对采用Ｓ１和Ｓ２的混合物作为萃取剂的三聚甲醛萃取－精馏新工艺进行了模拟计算，得到了使总费用最低的三聚甲醛精制工艺的联合操作参数。模拟计算结果成功地指导了小试操作，并可为大型工程设计提供可靠的基础数据。