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.     

A Study on Turbo—rotor Multi—fault Diagnosis Based on a Neural Network

The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine.As it has many excellent qualities,the neural network might be used to solve the problems of multi-fault diagnasis of a turbo-rotor system.First,the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing.Then.the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propaga-tion)neural network.According to the BP neural network problems,the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated.The results indicated that the parallel Bp neural network method could solve the turbo-rotor multi-fault diagnosis problems.     

江苏复杂小断块油藏老井侧钻实践与认识

利用停关、低效井实施老井侧钻,其条件包括油藏、井身及技术经济等3个方面。侧钻效益的好坏与油藏类型、侧钻目标有着很大关系,侧钻断块油层、挖掘构造高部位剩余油成功率高,边底水、高渗透油藏侧钻效果好。实践表明,老井侧钻是提高江苏复杂小断块油藏开发效果,降低投资、提高效益的一项重要手段。     

多传感器目标的模糊识别

根据模糊理论，采用信息融合技术和模糊识别技术，讨论了在多类传感器网内进行多目标模糊识别的一般原理和方法。     

Signal Integrity: Fault Modeling and Testing in High-Speed SoCs

As we approach 100 nm technology the interconnect issues are becoming one of the main concerns in the testing of gigahertz system-on-chips. Voltage distortion (noise) and delay violations (skew) contribute to the signal integrity loss and ultimately functional error, performance degradation and reliability problems. In this paper, we first define a model for integrity faults on the high-speed interconnects. Then, we present a BIST-based test methodology that includes two special cells to detect and measure noise and skew occurring on the interconnects of the gigahertz system-on-chips. Using an inexpensive test architecture the integrity information accumulated by these special cells can be scanned out for final test and reliability analysis.     

液力联轴器喷油着火事故的原因分析及预防

以矿用液力联轴器为研究对象 ,阐明其喷油着火的机理 ,分析其喷油着火的原因并作出事故树分析 ,针对现场使用的情况 ,提出预防措施及发展趋势 ,对矿井安全生产具有指导性作用。     

大规模数据库中的知识获取

一、前言数据是知识的潭泉,拥有大量的数据与拥有许多有用的知识完全是两回事.为了有效地利用大量的公共数据,必须更好地理解这些数据,并从其中快速、准确地发现知识.这里所说的知识是指大量数据中存在的规律性(r egularity)或不同属性值之间所存在的[I F THEN〕规则.将所获取的知识附加于仅由事实数据(fact data)构成的传统数据库上,既可强化数据库的查询能力,又可给数据库提供推理能力,     

故县大坝坝基泥化性F_5断层的化学灌浆防渗加固处理

泥化软化性F_5断层是故县大坝坝基范围内最大的一条断层,位于左岸,对左坝肩的稳定极为不利,采用水泥灌浆处理无效,故改用化学灌浆处理。选用EAA化学浆材,采用“优选组合配方综合施灌”的创新施工工艺,效果良好,为今后低渗透性复杂的泥化软化性断层的防渗加固处理,提供了新的依据和途径。     

综采工作面过断层采煤实践

介绍了大同煤矿集团公司挖金湾矿断层构造情况。总结了技术经验，针对具体条件，该矿综采过断层采煤采取了直接割煤、割顶（底）和预掘巷与割底相结合等过断层措施，取得了较好的效果。