一种Vague集的模糊支持向量数据描述

针对支持向量数据描述中噪声和孤立点带来的过拟合问题,提出了一种Vague集的支持向量数据描述（VFSVDD）,利用模糊k-均值聚类方法生成每个训练样本的真、假隶属度,可以精细地控制训练样本对超球面边界的影响。用UCI机器学习数据集的数据实验验证了VFSVDD的有效性。     

Bayesian density estimation from grouped continuous data

Grouped data occur frequently in practice, either because of limited resolution of instruments, or because data have been summarized in relatively wide bins. A combination of the composite link model with roughness penalties is proposed to estimate smooth densities from such data in a Bayesian framework. A simulation study is used to evaluate the performances of the strategy in the estimation of a density, of its quantiles and first moments. Two illustrations are presented: the first one involves grouped data of lead concentration in the blood and the second one the number of deaths due to tuberculosis in The Netherlands in wide age classes.     

贝叶斯框架下的非参数估计Graph Cuts分割算法

假设图像中各像素灰度值是具有一定概率分布的随机变量,由贝叶斯定理,正确分割观测图像等价于求出具有最大后验概率的实际图像估计。在此框架下,提出了一种改进型Graph Cuts图像分割算法。与传统Graph Cuts分割算法相比,该算法在模型建立上有两个方面的改进：1)将模糊C均值聚类引入数据约束能量函数来得到各像素在某个标记下的概率,改善了收敛性能;2)使用非参数方法估计图像的统计分布,然后用此统计量构成图像分割的先验概率,并保证分割结果的局部平滑。由于非参数估计是由样本直接估计得到的结果,特别适用于小样本和分布函数不恒定的情况,因此拓展了算法的适用范围。实验结果表明,改进算法在遥感图像分割和医学图像分割中均提高了分割精度,证明了该算法的有效性。     

On vague logics and approximate reasoning based on vague linear transformation

As a generalisation of the Fuzzy Sets theory, vague set has been proven to be a new tool in dealing with vague information. In this article, we attempt to generalise the techniques of fuzzy inference in a vague environment. In the rule-based inference system, an ‘if?…?then?…’ rule can be considered a transformer that implements information conversion between input–output ends. Thus, according to the logical operations of vague linguistic variables, we introduce an approach to approximation inference based on linear transformation, and then discuss the representations for several inference structures regarding single rule, multi-rules and compound rules. By defining the inclusion function of vague sets, we provide vague rough approximation based on measure of inclusion, and then present a method on rule creation from a decision system. A case study on the prediction for welding deformation is used to illustrate the effectiveness of the proposed approaches.     

Remote sensing image fusion based on Bayesian linear estimation

A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spa- tial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.     

基于有监督Bayesian网络的垃圾邮件过滤

对影响邮件特性的邮件报文格式作了仔细的分析并对垃圾邮件的特征进行了分类归纳,在此基础上构建了一个有监督的Bayesian邮件分类网络。通过对该网络作Bayesian参数估计,实现了判定邮件类别的不确定推理。对不同邮件测试集的在线学习试验结果表明,有监督Bayesian邮件分类网络能够有效地实现垃圾邮件的相对完备特征学习,改善邮件过滤的准确率。     

Bayesian estimation based on trimmed samples from Pareto populations

Trimmed samples are widely employed in several areas of statistical practice, especially when some sample values at either or both extremes might have been contaminated. The problem of estimating the inequality and precision parameters of a Pareto distribution based on a trimmed sample and prior information is considered. From an inferential viewpoint, the problem of finding the highest posterior density (HPD) estimates of the Pareto parameters is discussed. The existence and uniqueness of the HPD estimates are established under mild conditions; explicit and accurate lower and upper bounds are also provided. Adopting a decision-theoretic perspective, several Bayesian estimators for standard loss functions are presented. In addition, two-sided and HPD credibility intervals for each Pareto parameter and joint HPD credibility regions for both parameters are derived, which have the corresponding frequentist confidence level in the noninformative case. Finally, an illustrative example concerning annual wage data is included.     

Approximation operators based on vague relations and roughness measures of vague sets

Rough set theory and vague set theory are powerful tools for managing uncertain, incomplete and imprecise information. This paper extends the rough vague set model based on equivalence relations and the rough fuzzy set model based on fuzzy relations to vague sets. We mainly focus on the lower and upper approximation operators of vague sets based on vague relations, and investigate the basic properties of approximation operators on vague sets. Specially, we give some essential characterizations of the lower and upper approximation operators generated by reflexive, symmetric, and transitive vague relations. Finally, we structure a parameterized roughness measure of vague sets and similarity measure methods between two rough vague sets, and obtain some properties of the roughness measure and similarity measures. We also give some valuable counterexamples and point out some false properties of the roughness measure in the paper of Wang et al.     

基于Bayes与多层Bayes估计的WSN链路选择算法

无线传感器网络（WSN）节点能量有限,采用传统的链路选择的方法（经验法）进行链路选择,需要发送大量的数据包作为测试样本,这在WSN中是不合适的。设计了两种基于Bayes估计与一种基于多层Bayes估计的WSN链路选择算法,分别记为BLSP-B1、BLSP-B2、BLSP-HE。仿真实验发现,在小样本的条件下,BLSP-B1、BLSP-B2、BLSP-HE选择高质量的链路的概率比经验法要高出10%～20%,其中BLSP-HE算法最稳健,性能较好。     

基于贝叶斯网的离港航班滑行时间动态估计

为提升离港航班运行效率，根据机场协同决策规范（A-CDM）中关于离港航班可变滑行时间（EXOT）的有关规定，分析了相关影响因素。根据数据分析处理和民航专家知识建立了一种基于贝叶斯网的离港航班滑行时间动态估计模型。贝叶斯网是一种将概率统计应用于复杂领域、进行不确定性推理和数据分析的工具。应用其增量学习特点对模型进行动态调整，实现了对场面实时变化的把控。以国内某大型枢纽机场为例，使用期望优化（EM）算法实现了对随机缺失数据的处理，并验证了模型的有效性。对实验结果与该机场实际运行数据对比表明，所建模型能有效地估计离港航班滑行时间且具有较高的置信度。     

基于改进块稀疏贝叶斯学习算法的波达方向估计

针对传统的基于稀疏表示的DOA估计算法单纯利用信号的空域稀疏性,导致在低信噪比时稀疏性能变差,影响信号稀疏重构效果的问题,使用分块稀疏理论对信号进行稀疏分解。随着目标增多及作战任务改变,DOA估计往往呈现目标群测向的特点,为了能够更好地利用信号的结构特征和统计特征,提出了基于空时联合的块稀疏DOA估计算法,使用块稀疏理论挖掘信号的内部结构,充分利用了信号的块内稀疏性和块间相关性,提高稀疏重构性能,进而对DOA估计效果有很大的提升。仿真实验表明,相比于经典的DOA方法,本方法有更好的估计效果。     

Bayesian estimation of directional wave spectra based on ship motions

New techniques are introduced into the Bayesian modeling procedure to estimate directional wave spectra based on ship motion data. In the proposed method, the triple-valued function problem in following seas is strictly taken into account and the optimum solution is obtained from the stochastic viewpoint. In order to examine the reliability of the proposed method, towing tank experiments and onboard experiments were carried out. Using the proposed method, it is shown that the directional wave spectra can be estimated from the vector-valued time series recorded on a running ship even in following seas.     

Software effort estimation based on the optimal Bayesian belief network

In this paper, we present a model for software effort (person-month) estimation based on three levels Bayesian network and 15 components of COCOMO and software size. The Bayesian network works with discrete intervals for nodes. However, we consider the intervals of all nodes of network as fuzzy numbers. Also, we obtain the optimal updating coefficient of effort estimation based on the concept of optimal control using Genetic algorithm and Particle swarm optimization for the COCOMO NASA database. In the other words, estimated value of effort is modified by determining the optimal coefficient. Also, we estimate the software effort with considering software quality in terms of the number of defects which is detected and removed in three steps of requirements specification, design and coding. If the number of defects is more than the specified threshold then the model is returned to the current step and an additional effort is added to the estimated effort. The results of model indicate that optimal updating coefficient obtained by genetic algorithm increases the accuracy of estimation significantly. Also, results of comparing the proposed model with the other ones indicate that the accuracy of the model is more than the other models.     

基于贝叶斯压缩感知的正交频分复用信道估计改进

为了获得理想的正交频分复用信道估计结果,针对传统算法没有充分利用无线信道时域的固有稀疏性,导致估计精度不高且频谱利用率低等难题,提出了改进贝叶斯算法感知的正交频分复用信道估计算法。首先在详细介绍本文算法的原理和步骤基础上,与传统信道估计算法进行仿真对比实验。仿真结果表明,相对于其它信道估计算法,本算法可获得更高的信道估计精度,在使用较少导频的条件下获得很好的信道估计性能。     

An acceptability estimation and analysis methodology based on Bayesian networks

As companies are forced to conceive innovative products to stay competitive, designers face the challenge of developing products more suited to users' needs and perceptions in order to be accepted, thus reducing project risk failure. Evaluating users' acceptability has become an important research problem. Current approaches leave the acceptance evaluation question to be answered in the last stages of product development process (NPD), when an almost finished prototype is available and when there is no time left for important modifications. Acceptability evaluation methods suitable for use from the early stages of the NPD process are thus needed. This paper proposes a method for acceptability evaluation and analysis that can be used in the early stages of the development cycle. It is based on the evaluation of the solution concept by the users. The relationships among the factors (or criteria) are made explicit, thus helping designers to identify the key factors for acceptance. As the users' tests and the maturity of the concept prototype are limited in this stage, the proposed method exploits the inference properties of Bayesian networks making it possible to make useful estimations and allowing the exploration of actions that could improve the product acceptability level. Two case studies are presented in order to illustrate the method, the first related to a technological product design for a home-health care service provider and the second to a work-related musculoskeletal disorder prevention software design.Relevance to industryThe article describes an acceptability assessment and an analysis approach to be used by industrial engineers, designers and ergonomists in the early phases of design projects. The method can help the design team to identify the levers (key factors) for enhancing product acceptance and to identify different actions (e.g. product modification, deployment strategy, and training).     

一种Vague集上的直接聚类法

Vague集是Fuzzy集的扩展,在给出几种构造Vague集相似矩阵方法的基础上,将Fuzzy集上的编网法和最大树法引入到Vague集上,定义了Vague关系图,并给出了基于Vague集的直接聚类法：编网法和最大树法。最后使用文献[1]中的例子,分别采用Vague传递闭包法和Vague直接聚类法进行计算。实验结果表明,Vague直接聚类法计算简单,不会造成原始信息的失真,比Vague传递闭包法更加有效。     

基于距离的Vague熵

对现有Vague集的模糊熵的缺陷进行了分析,给出了符合人们直觉的Vague集的模糊熵的公理化定义,提出了一种能准确体现未知性和模糊性二者相互关系的新的Vague熵计算公式,证明了其具有良好的数学特性,并通过多个实例与现有方法对比,说明它确实准确地考虑了二者的互相约束关系,所得结果更合理,更符合人的直觉.     

关于区间值数据向Vague值数据转化公式的研究

提出了区间值数据向Vague值数据的转化准则和定义,以及两个区间值数据向Vague值数据的转化公式。例题表明不同公式有不同的特征。     

基于贝叶斯网络的WSNs链路质量评估机制研究

在无线传感器网络(WSNs)应用中,链路质量的有效评估是保障数据可靠传输和上层网络协议性能的基础性问题.针对现有无线链路质量评估研究中,链路质量等级划分仍无统一标准和模型缺乏环境自适应性问题,提出了一种基于贝叶斯网络的链路质量评估机制.从链路质量多属性角度出发,采用贴近度分析法对链路质量等级进行划分,利用贝叶斯网络对链路质量进行不确定性推理与评估建模.通过多应用场景的训练与测试,最后实际测试表明:基于贝叶斯网络的评估模型在不同应用环境具有较高准确率和泛化能力,体现了良好的自适应性.