模糊综合评判中几种数学模型的比较

本文对模糊综合评判中几种常见的数学模型进行了比较,分析了这几种数学模型的实质,为模糊综合评判的合理应用提供了基础。     

基于数据挖掘方法的电子邮件过滤

电子邮件在给人们带来很多方便抽时,也产生了一个新的问题,即大量垃圾邮件的出现。邮件过滤就是从大量邮件中过滤出垃圾邮件,帮助用户寻找到所需要的有用邮件。本文介绍了一个基于数据挖掘方法的邮件过滤系统原型,给出了Bayes方法的几个基于概念,并重点讨论了要实现这个系统所需要处理的几个关键问题。     

建筑结构裂缝的图形仿真

本文从建筑结构中构件裂缝的概念和表征出发,面向图形对象建立裂缝仿真模型,并 从计算机图形学的角度,运用粘贴技术来处理裂缝图形。为了使裂缝更逼真,在知识 推理的基础上,本文运用了随机处理技术。     

纳米金-壳聚糖-血红蛋白/普鲁士蓝/金电极检测过氧化氢

在普鲁士蓝修饰的金电极上,通过纳米金/壳聚糖复合膜固定血红蛋白,研制了安培型过氧化氢生物传感器,线性范围和检测限分别为1～5 000 umol/L和0.27umol/L.     

SPI总线的UART扩展方法

借助于微处理器的SPI总线接口,利用NXP公司的一款芯片SC16IS752,提出一种基于SPI转UART的软硬件设计方案;着重介绍SPI-UART转换原理,并结合实际的应用系统,讨论了整个设计的可行性和实用性.实际应用表明,该方案经济、实用.     

A Reservation-Based Extended Transaction Protocol

With the advent of the new generation of Internet-based technology, in particular, web services, the automation of business activities that are distributed across multiple enterprises becomes possible. Business activities are different from traditional transactions in that they are typically asynchronous, loosely coupled, and long running. Therefore, extended transaction protocols are needed to coordinate business activities that span multiple enterprises. Existing extended transaction protocols typically rely on compensating transactions to handle exceptional conditions. In this paper, we identify a number of issues with compensation-based extended transaction protocols and describe a reservation-based extended transaction protocol that addresses those issues. Moreover, we define a set of properties, analogous to the ACID properties of traditional transactions that are more appropriate for business activities that span multiple enterprises. In addition, we compare our reservation protocol with other extended transaction protocols for coordinating business activities and present performance analyses and results.     

Availability Modeling and Cost Optimization for the Grid Resource Management System

Grid computing is a recently developed technique for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Although the development tools and techniques for the grid have been extensively investigated, the availability of the grid resource management system (RMS) has not been comprehensively studied. In order to contribute to this lacking but important field, this paper first models the grid RMS availability by considering both the failures of resource management (RM) servers and the length limitation of request queues. A hierarchical Markov reward model is implemented to evaluate the grid RMS availability. Based on the availability model, an optimization problem for designing the grid RMS is studied in order to minimize the cost by determining the best number of RM servers. Then, the sensitivity analysis is conducted, and a dynamic switching scheduling method is further presented based on the sensitivity analysis.     

An enhanced possibilistic C-Means clustering algorithm EPCM

The possibility based clustering algorithm PCM was first proposed by Krishnapuram and Keller to overcome the noise sensitivity of algorithm FCM (Fuzzy C-Means). However, PCM still suffers from the following weaknesses: (1) the clustering results are strongly dependent on parameter selection and/or initialization; (2) the clustering accuracy is often deteriorated due to its coincident clustering problem; (3) outliers can not be well labeled, which will weaken its clustering performances in real applications. In this study, in order to effectively avoid the above weaknesses, a novel enhanced PCM version (EPCM) is presented. Here, at first a novel strategy of flexible hyperspheric dichotomy is proposed which may partition a dataset into two parts: the main cluster and auxiliary cluster, and is then utilized to construct the objective function of EPCM with some novel constraints. Finally, EPCM is realized by using an alternative optimization approach. The main advantage of EPCM lies in the fact that it can not only avoid the coincident cluster problem by using the novel constraint in its objective function, but also has less noise sensitivity and higher clustering accuracy due to the introduction of the strategy of flexible hyperspheric dichotomy. Our experimental results about simulated and real datasets confirm the above conclusions.     

Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework

In this paper, a subspace-based multimedia data mining framework is proposed for video semantic analysis, specifically video event/concept detection, by addressing two basic issues, i.e., semantic gap and rare event/concept detection. The proposed framework achieves full automation via multimodal content analysis and intelligent integration of distance-based and rule-based data mining techniques. The content analysis process facilitates the comprehensive video analysis by extracting low-level and middle-level features from audio/visual channels. The integrated data mining techniques effectively address these two basic issues by alleviating the class imbalance issue along the process and by reconstructing and refining the feature dimension automatically. The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework. Furthermore, its unique domain-free characteristic indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.     

两种数据融合算法对扩散硅压力传感器的温度补偿

针对扩散硅压力传感器在实际应用中对温度存在交叉灵敏度的问题,文章采用改进的多维回归分析法和BP神经网络法两种实用的智能化数据融合方法对压力传感器输出进行处理,以消除非目标参量(温度)对传感器的影响;研究结果表明这两种方法均能有效地抑制交叉灵敏度,减小灵敏度温漂和零点温漂;由于这两种融合算法具有各自的特点,因此可应用于不同要求的智能传感器系统中。