基于特征加权的故障征兆处理算法

为了防范网络中存在的多种多样的安全威胁,由大量异构型安全设备所组成的统一管理平台得到了广泛的应用。然而如何有效地解决由这些安全设备集成所引发的各种冲突和故障,已成为网络管理的重点和难点。本文提出以距离度量和特征加权算法为基础,对网络中采集到的各种原始样本参数进行数据预处理,以便为故障诊断提供更加可靠、准确地输入参量。由于安全设备的故障特征具有很强的异构特性,所以首先对特征变量进行分类描述。将变量分为连续型、有序型、标称型、二分型四类23种。之后,对不同类型特征变量,提出Euclid、VDM算法、DVDM及IVDM算法来标称不同样本的距离。最后,综合运用知识规则和模糊理论对四种算法分别赋予不同的权重。通过实验及结果分析,显示加权后DVDM和IVDM 算法的故障分类准确度增长10%以上,说明该理论算法具有较高的可靠性和准确度。     

软件的易测试性分析方法述评

软件测试是软件工程领域中重要组成部分。随着软件规模的不断扩大,测试工作的复杂性也不断升高,而改善、提高软件易测试性则是降低测试复杂性的有效手段。为达到这一目标,首先要能对软件的易测试性进行准确的度量。这种度量结果除了作为软件度量的一个量化指标外,还应能为改善易测试性提供指导、为测试的设计提供有效帮助。本文对现有软件易测试性分析方法进行归类,将已有技术大致归并为基于复杂度分析、基于信息论分析、基于PIE技术分析和基于UML类图分析四种基本类型。简要介绍了每一类方法,对这些方法的特点与不足进行分析比较,并探讨今后的研究方向。     

非线性参数平差模型的非线性度量

该文首先在分析模型非线性三种简单的度量方法及几何性质的基础上,给出了模型的两种非线性──模型固有的非线性与参数效应的非线性及其度量方法,接着讨论了参数向量及残差向量的非线性,最后给出诊断模型非线性的一种方法。     

梯度法在欧氏距离选址模型中的应用

介绍了物流设施选址目前的方法和模型,重点讨论欧氏距离选址模型的应用,提出了梯度法的求解方法.将梯度法应用于欧氏距离选址模型,并归纳了模型的求解公式.通过实例分析证明梯度法解决欧氏距离选址模型是有效的.     

Wasserstein autoregressive models for density time series

Data consisting of time-indexed distributions of cross-sectional or intraday returns have been extensively studied in finance, and provide one example in which the data atoms consist of serially dependent probability distributions. Motivated by such data, we propose an autoregressive model for density time series by exploiting the tangent space structure on the space of distributions that is induced by the Wasserstein metric. The densities themselves are not assumed to have any specific parametric form, leading to flexible forecasting of future unobserved densities. The main estimation targets in the order-p Wasserstein autoregressive model are Wasserstein autocorrelations and the vector-valued autoregressive parameter. We propose suitable estimators and establish their asymptotic normality, which is verified in a simulation study. The new order-p Wasserstein autoregressive model leads to a prediction algorithm, which includes a data driven order selection procedure. Its performance is compared to existing prediction procedures via application to four financial return data sets, where a variety of metrics are used to quantify forecasting accuracy. For most metrics, the proposed model outperforms existing methods in two of the data sets, while the best empirical performance in the other two data sets is attained by existing methods based on functional transformations of the densities.     

稀疏偏差补偿最小平均对数算法

针对最小平均对数（LMLS）算法在输入信号受噪声干扰的环境下进行稀疏系统辨识时存在精度低的问题，提出了一种稀疏偏差补偿LMLS算法.利用无偏准则推导偏差补偿项来修正输入噪声带来的偏差，构建偏差补偿LMLS.借助系统稀疏特性的先验知识，采用互相关熵诱导维度作为稀疏惩罚约束条件，优化偏差补偿LMLS算法.仿真结果表明，所提算法对于含噪输入信号下的稀疏系统参数辨识具有高稳态精度.     

Discriminative tensor decomposition with large margin

Tensor decompositions have many application areas in several domains where one key application is revealing relational structure between multiple dimensions simultaneously and thus enabling the compression of relational data. In this paper, we propose the Discriminative Tensor Decomposition with Large Margin (shortly, Large Margin Tensor Decomposition, LMTD), which can be viewed as a tensor-to-tensor projection operation. It is a novel method for calculating the mutual projection matrices that map the tensors into a lower dimensional space such that the nearest neighbor classification accuracy is improved. The LMTD aims finding the mutual discriminative projection matrices which minimize the misclassification rate by minimizing the Frobenius distance between the same class instances (in-class neighbors) and maximizing the distance between different class instances (impostor neighbors). Two versions of LMTD are proposed, where the nearest neighbor classification error is computed in the feature (latent) or input (observations) space. We evaluate the proposed models on real data sets and provide a comparison study with alternative decomposition methods in the literature in terms of their classification accuracy and mean average precision.     

Nonlinear iterative methods for solving the split common null point problem in Banach spaces

In this work, we study the split common null point problem in the framework of Banach spaces. We propose an iterative scheme for solving the problem and then prove strong convergence theorem of the sequences generated by our iterative scheme under suitable conditions. We finally provide some numerical examples to support the main theorem.     

Some results on the entropy of non-autonomous dynamical systems

In this paper, we advance the entropy theory of discrete non-autonomous dynamical systems that was initiated by Kolyada and Snoha in 1996. The first part of the paper is devoted to the measure-theoretic entropy theory of general topological systems. We derive several conditions guaranteeing that an initial probability measure, when pushed forward by the system, produces an invariant measure sequence whose entropy captures the dynamics on arbitrarily fine scales. In the second part of the paper, we apply the general theory to the non-stationary subshifts of finite type, introduced by Fisher and Arnoux. In particular, we give sufficient conditions for the variational principle, relating the topological and measure-theoretic entropy, to hold.     

基于HVS的结构相似性的视频质量评价

近年来,基于结构相似性的视频质量的客观评价方法得到了广泛的关注,它建立在人眼对结构失真较为敏感这一假设之上,实验表明其性能优于峰值信噪比。该方法并没有考虑人眼视觉系统对不同的频率分量和对不同视频区域的敏感程度不同。基于人眼视觉系统的特点和结构相似性,提出了一种改进的视频质量评价方法,实验表明它与主观视频质量的相关性明显好于峰值信噪比和结构相似性。