基于语义的AdHoc网络服务发现

在分析现有的Ad Hoc网络服务发现方法的基础上,提出一种基于语义的Ad Hoc网络服务发现方法。该方法基于语义推理机制,能对服务进行准确地刻画,大大提高了查准率;又由于该方法在考虑服务本身的同时,还考虑了服务的节点属性和节点所处的环境属性,可以有效地改善服务质量。仿真结果表明,与同类的Ad Hoc网络服务发现方法相比,该方法在查准率方面具有较大的优越性。     

灰色组合模型在股指预测中的新发现

针对四项灰色组合模型,提出了较优的最佳等维新息两项组合灰色模型预测。通过证券市场股指预测的实证分析,发现灰色组合模型均不及两次拟合灰色模型适用。     

Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

Text event mining, as an indispensable method of text mining processing, has attracted the extensive attention of researchers. A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper, i.e. UKGE-MS. Specifically, UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information, and solves the problems of traditional unsupervised feature selection methods, which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples. Firstly, considering the influence of local information of samples in feature correlation evaluation, a feature clustering algorithm based on average neighborhood mutual information is proposed, and the feature clusters with certain event correlation are obtained; Secondly, an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation, so as to enhance the generalization ability of the selected feature items. Finally, the events knowledge graph is constructed by means of sparse representation and l₁ norm. Extensive experiments are carried out on five real datasets and synthetic datasets, and the UKGE-MS are compared with five corresponding algorithms. The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection, and has some advantages over other methods in text event recognition and discovery.     

A misclassification cost‐minimizing evolutionary–neural classification approach

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)‐based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real‐world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back‐propagation artificial neural network, integrated cost preference‐based linear mathematical programming‐based minimize squared deviations, linear integrated cost preference‐based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost‐minimizing approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.     

基于SNF的一种检测和跟踪性能优化方法

对于一个监视系统,跟踪性能的优化要求检测系统和跟踪系统通过引入反馈进行联合设计,针对最大量测幅值邻域滤波器(SNF),提出了先验和后验检测跟踪优化准则,通过分析得出先验优化准则不可行这一结论,而利用后验优化准则同时对检测阈值和门值的联合优化,仿真结果表明,用后验优化准则进行检测和跟踪性能优化,不仅具有比恒虚警率更好的跟踪精度,而且其计算代价也大为降低.     

噪声干扰条件下雷达检测概率的评估

通过对雷达检测概率特性的分析,采用拟合法,得出自卫式噪声干扰条件下雷达检测概率与距离的关系,为定量评估雷达干扰效果提供了一种新的方法。     

一种基于DCT和SVM的人脸识别新方法

文中提出了一种新的人脸识别方法,该方法采用DCT提取人脸特征,并采用SVM对该特征进行分类识别。基于该方法,对ORL人脸库进行分类识别,仅用28个特征平均识别率达到97.4%。仿真结果表明,该方法显著地降低了特征维数和计算复杂度,明显提高了特征的可辨别能力,而且,SVM可以有效地提高分类器的分类和推广能力。     

“发现教学法”的探索与廊用

立足于加强学员的科学思维方法训练,培养具有创新能力的高素质人才,从新的角度探索“发现教学法”在电子技术实验课教学中的应用,提出了应用发现教学法一般实施的四个阶段和应着重把握的几个问题。     

一种以太网拓扑发现算法

局域网内部的拓扑对于现代网络管理非常重要,而网络层拓扑发现对于局域网内部的拓扑是不可见的。文章提出了一种基于生成树协议(STP)的拓扑发现算法,利用简单网络管理协议(SNMP)读取交换机MIB中的生成树信息,可以得到局域网内设备间的连接。该算法不需要交换机地址转发表(AFT)完整,也能发现交换机的备份链路和集线器以及不支持SNMP的交换机。经分析,该算法是一种简单的,准确的以太网拓扑发现算法。     

多源文本数据真值发现方法

针对传统真值发现算法无法直接应用于文本数据的问题,提出基于深度神经网络面向多源文本数据的真值发现算法(NN_Truth)。根据文本答案多因素性、词语使用多样性以及文本数据稀疏性等特点,将“数据源-答案”向量作为网络输入,识别答案真值向量作为网络输出,依据真值发现的一般假设,无监督学习各数据源答案向量间关联关系,并最终获得答案真值。实验结果表明,该算法适用于文本数据真值发现场景,较基于检索的方法及传统真值发现算法效果更优。