基于词性的文本挖掘算法在IDS日志中的应用

提出一种以词性为参考值的文本挖掘算法,能有效挖掘与种子词有关的关联规则。基于Bootstrapping算法思想,既减少了预处理阶段对于词根还原的依赖,能处理日志中出现的中文词汇。增加了对于日志文本上下的理解,提高了关联规则的有效性,并应用与IDS日志挖掘之中,有效改善挖掘效率,为规则库提供关联规则。     

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

Stochastic approximation learning for mixtures of multivariate elliptical distributions

Most of the current approaches to mixture modeling consider mixture components from a few families of probability distributions, in particular from the Gaussian family. The reasons of these preferences can be traced to their training algorithms, typically versions of the Expectation-Maximization (EM) method. The re-estimation equations needed by this method become very complex as the mixture components depart from the simplest cases. Here we propose to use a stochastic approximation method for probabilistic mixture learning. Under this method it is straightforward to train mixtures composed by a wide range of mixture components from different families. Hence, it is a flexible alternative for mixture learning. Experimental results are presented to show the probability density and missing value estimation capabilities of our proposal.     

政务信息资源中心框架设计及关键技术研究

目前国内有关政务信息资源共享的研究及应用还停留在网络互联互通、信息资源整合、点对点数据交换阶段,并没有达到"协同工作"的要求.鉴于此,提出了政务信息资源中心的概念,实现政务信息资源的统一获取、统一管理、规则化和规模化服务.结合工作实践,对其组成、总体框架、技术架构进行了设计,并就实现时需要解决的关键技术进行了研究.     

无线光电定位网络的设计

为满足非接触化、小型化、集成化、数字化、智能化等现代检测技术的发展要求,从成本、工作原理、灵敏度等方面综合考虑,现采用以光电定位模块为核心的光电定位技术,以无线传感器网络作为数据传输与管理的技术手段,构建了无线光电定位网络,并进行了实验分析.可实现多点实时监测、异地显示测量数据、数据网络化管理等特殊需求;涵盖了多学科领...     

An improved lossless data hiding scheme based on image VQ-index residual value coding

Copyright protection and information security have become serious problems due to the ever growing amount of digital data over the Internet. Reversible data hiding is a special type of data hiding technique that guarantees not only the secret data but also the cover media can be reconstructed without any distortion. Traditional schemes are based on spatial, discrete cosine transformation (DCT) and discrete wavelet transformation (DWT) domains. Recently, some vector quantization (VQ) based reversible data hiding schemes have been proposed. This paper proposes an improved reversible data hiding scheme based on VQ-index residual value coding. Experimental results show that our scheme outperforms two recently proposed schemes, namely side-match vector quantization (SMVQ)-based data hiding and modified fast correlation vector quantization (MFCVQ)-based data hiding.     

A hybrid multi-criteria decision-making model for firms competence evaluation

In this paper, we present a hybrid multi-criteria decision-making (MCDM) model to evaluate the competence of the firms. According to the competence-based theory reveals that firm competencies are recognized from exclusive and unique capabilities that each firm enjoy in marketplace and are tightly intertwined within different business functions throughout the company. Therefore, competence in the firm is a composite of various attributes. Among them many intangible and tangible attributes are difficult to measure. In order to overcome the issue, we invite fuzzy set theory into the measurement of performance. In this paper first we calculate the weight of each criterion through adaptive analytic hierarchy process (AHP) approach (A³) method, and then we appraise the performance of firms via linguistic variables which are expressed as trapezoidal fuzzy numbers. In the next step we transform these fuzzy numbers into interval data by means of α-cut. Then considering different values for α we rank the firms through TOPSIS method with interval data. Since there are different ranks for different α values, we apply linear assignment method to obtain final rank for alternatives.     

一种基于W7100A的实验室仪器信息采集系统

针对实验室仪器信息统计中的准确性、完整性欠缺及低效率等问题,设计了一种全新的实验室仪器信息采集系统,以低功耗网络微处理器W7100A为系统的主控制器,通过SHT11温湿度传感模块和ZLG500AT射频IC模块直接采集仪器的环境参数等信息,通过用户与上位机客户端软件交互的方式采集仪器的工作内容等信息,相较于传统的手工记录方式,信息采集的及时性、完整性、可靠性方面都有了极大的提高.     

基于跳频和同步捕获技术的水下无线通信技术研究

本文所阐述的水下无线技术即基于跳频技术和同步捕获技术的水声通信,由于声波在海水中的传播特性显著优越于电磁波和可见光,使得水声技术成为海洋高技术的主要研究领域之一 ,在国防领域也具有十分重要的研究意义.本文主要论述了水声通信中跳频技术和同步捕获技术的原理、具体算法的实现方案以及部分硬件设施的使用简述,并通过展示实验结果验证算法的可行性.