产生式与判别式线性混合分类器

产生式方法和判别式方法是解决分类问题的两种不同框架,具有各自的优势。为利用两种方法各自的优势,文中提出一种产生式与判别式线性混合分类模型,并设计一种基于遗传算法的产生式与判别式线性混合分类模型的学习算法。该算法将线性混合分类器混合参数的学习看作一个最优化问题,以两个基分类器对每个训练数据的后验概率值为数据依据,用遗传算法找出线性混合分类器混合参数的最优值。实验结果表明,在大多数数据集上,产生式与判别式线性混合分类器的分类准确率优于或近似于它的两个基分类器中的优者。     

高效液相色谱法同时测定食品中12种防腐剂和甜味剂

目的建立高效液相色谱法同时测定食品中12种防腐剂和甜味剂的含量。方法样品经甲醇-水(30:70,V:V)提取,经Agilent TC C_(18)色谱柱分离,以甲醇/20 mmol/L乙酸铵水溶液为流动相进行梯度洗脱,流速为1.0mL/min,采用二极管矩阵检测器进行检测,检测波长为230 nm。结果 12种防腐剂和甜味剂在0.2~20μg/mL的浓度范围内呈现良好的线性关系,r~2≥0.9992,回收率在70.9%~115.6%之间,方法的检出限为0.001~0.005 g/kg。结论该方法具有高通量、操作简便等优点,可适用于食品中12种防腐剂和甜味剂的检测。     

基于新颖性检测的跌倒风险预测

跌倒是65岁及以上人群因伤害致死的第一位原因.结合受试者个体信息的个性化特征,提出一种基于Kinect三维骨架数据的步态特征提取方法,对老年人的跌倒风险进行评估和预测.将跌倒风险分为高跌倒风险和低跌倒风险两类,考虑数据采集的成本问题,采用新颖性检测模型在不平衡数据集下对特征数据进行训练和评估.实验结果表明,OC-SVM...     

An Overview of Virtual Private Network (VPN): IP VPN and Optical VPN

Recently, there has been rapid development and deployment of virtual private network (VPN) services. There are wide varieties of IP-based VPNs and optical VPNs (OVPNs) proposed in the literature and readers could easily get confused with so many different types of VPNs. The purpose of this paper is to present a comprehensive overview of the VPN and discuss the main issues associated with the design of IP VPN and OVPN. We first present a classification of the VPNs including CE-based, network based, customers provisioned, provider provisioned, connection oriented, connectionless oriented, port based, connection based, layer 1 VPN, layer 2 VPN, and layer 3 VPN, and describe different VPN protocols such as IPSec, GRE and MPLS. We then review the recent work on OVPN by different standard bodies, and outline the key requirements for OVPN service providers and customers. Finally, we describe several OVPN architectures appeared in the literature, highlight the future work in OVPN.     

Vector-based signal processing and quantization for image and videocompression

Image and video compression has become an increasingly important and active area. Many techniques have been developed in this area. Any compression technique can be modeled as a three-stage process. The first stage can be generally called a signal processing stage where an image or video signal is converted into a different domain. Usually, there is no or little loss of information in this stage. The second stage is quantization where loss of information occurs. The third stage is lossless coding that generates the compressed bit stream. The purpose of the signal processing stage is to convert an image or video signal into such a form that quantization can achieve better performance than without the signal processing stage. Because the quantization stage is the place where most of compression is achieved and loss of information occurs, it is naturally the central stage of any compression technique. Since scalar quantization or vector quantization may be used in the second stage, the operation in the first stage should be scalar-based or vector-based respectively in order to match the second stage so that the compression performance can be optimized. In this paper, we summarize the most recent research results on vector-based signal processing and quantization techniques that have shown high compression performance     

Polarimetric scattering from a layer of random clusters of smallspheroids

Employing the volume integral equation formulation with self-interaction term for internal fields, the Jones scattering matrix and phase matrix for random clusters of small spheroids are derived. The mutual coherent wave interactions among clustered spheroids are included. Clustering enhances the internal fields and the scattering amplitude function. Substituting the newly derived phase matrix into the Mueller matrix in the approach of vector radiative transfer for a layer of random clusters of small spheroids, fully polarimetric copolarized and cross-polarized backscattering are numerically calculated. Clustering effects of a layer of random spheroids are quantitatively illustrated, and the backscattering enhancement of several dB order and its functional dependence on parameters are discussed     

基于GA-CFS属性选择的个人信用评估模型

属性选择可以有效减少数据的冗余度和降低数据的维度,将GA-CFS属性选择方法引入个人信用评估中,利用CFS评价得到的启发式"价值"作为GA的适应度函数来对个人信用指标体系优化,建立了基于GA-CFS属性选择的个人信用评估模型.在Australian数据集上比较了ID3、NB、Logistic、SMO与GA-CFS属性选...     

模2n 加和模2加混合运算的异或分支数

研究了扩散结构为二元域上非线性变换的异或分支数.给出了扩散结构为二元域上非线性变换的异或分支数的定义及其与分组密码抗差分攻击和线性分析能力的关系,证明了以模2n加和模2加的混合运算为扩散结构的异或分支数等于将模2n加换成模2加且将各变元系数模2后所得的二元域上线性变换的异或分支数,从而简化了此类非线性扩散结构异或分支数的计算问题.     

一种有效的高属性维稀疏数据聚类算法

聚类分析是数据挖掘最常见的技术之一,数据的规模、维数和稀疏性都是制约聚类分析的不同方面.本文提出一种有效的高属性维稀疏数据聚类方法.给出稀疏相似度、等价关系的相似度、广义的等价关系的定义.基于对象间的稀疏相似度和等价关系原理形成初始等价类,通过等价关系的相似度修正初始等价关系,使得最终聚类结果更合理.该算法聚类过程不依赖于输入样本的排列顺序,高维稀疏数据的有效压缩提高算法在维数较高时的执行效率,适合于高维稀疏数据的聚类分析.