邮区中心局生产成本事前控制

侧重介绍了邮区中心局生产成本事前控制的意义及相关方法,并着重对设备配置临界业务量的计算方法进行了详细论述。     

开放分布系统安全中的Bayes信任模型

为了解决开放分布系统的安全问题,本文提出了一个Bayes信任模型.借鉴人类的信任概念,该模型根据直接经验和推荐信息可以计算出一个实体对其他实体的信任值.在该模型中,把实体间交互成功的概率作为信任的重要指标,选择Beta分布作为其先验分布,通过对实体间4种不同关系的分析,获得相应的成功交互概率的估计.最终可形成实体的相对稳定的交互系统,为是否和别的实体交互提供依据.该模型考虑了影响信任的主观因素和客观因素,且具有统计基础,为解决开放分布系统的安全问题提供了新方法.     

分组样本下先验BN模型及条件概率的学习算法

文章扩展经典的先验BN模型，采用两层学习结构讨论分组样本下BN模型的条件概率及学习算法：一层是对各组私有条件概率分布的学习；另一层是对各组公有条件概率分布的学习。算法在综合公有后验条件概率分布和本组学习实例数据分布特征的基础上，实现对各组私有条件概率分布的学习，并可通过经验或学习来改变综合值中共性和个性的比例。     

A learning-based method for compressive image recovery

Compressive sensing (CS) theory dictates that a sparse signal can be reconstructed from a few random measurements. An important issue of compressive image recovery (CIR) is that the optimal sparse space is usually unknown and/or it often varies spatially for non-stationary signals (e.g., natural images). In this paper, apart from fixed sparse spaces, prior models, specifically a set of piecewise autoregressive (AR) models that encode the common statistics of image micro-structures, are learned from example image patches, and they are then used to construct adaptive sparsity regularizers for CIR. Furthermore, a complementary non-local structural sparsity regularizer is also incorporated into the CIR process to improve the robustness. The regularization by local AR model and non-local redundancy makes the proposed CIR very effective. Experimental results on benchmark images validate that the proposed algorithm can outperform significantly previous CIR methods in terms of both PSNR and visual quality.     

Multiple Empirical Kernel Learning with dynamic pairwise constraints

Unlike the traditional Multiple Kernel Learning (MKL) with the implicit kernels, Multiple Empirical Kernel Learning (MEKL) explicitly maps the original data space into multiple feature spaces via different empirical kernels. MEKL has been demonstrated to bring good classification performance and to be much easier in processing and analyzing the adaptability of kernels for the input space. In this paper, we incorporate the dynamic pairwise constraints into MEKL to propose a novel Multiple Empirical Kernel Learning with dynamic Pairwise Constraints method (MEKLPC). It is known that the pairwise constraint provides the relationship between two samples, which tells whether these samples belong to the same class or not. In the present work, we boost the original pairwise constraints and design the dynamic pairwise constraints which can pay more attention onto the boundary samples and thus to make the decision hyperplane more reasonable and accurate. Thus, the proposed MEKLPC not only inherits the advantages of the MEKL, but also owns multiple folds of prior information. Firstly, MEKLPC gets the side-information and boosts the classification performance significantly in each feature space. Here, the side-information is the dynamic pairwise constraints which are constructed by the samples near the decision boundary, i.e. the boundary samples. Secondly, in each mapped feature space, MEKLPC still measures the empirical risk and generalization risk. Lastly, different feature spaces mapped by multiple empirical kernels can agree to their outputs for the same input sample as much as possible. To the best of our knowledge, it is the first time to introduce the dynamic pairwise constraints into the MEKL framework in the present work. The experiments on a number of real-world data sets demonstrate the feasibility and effectiveness of MEKLPC.     

改进学习贝叶斯网络结构的MDL准则

文章针对用于BN结构学习的MDL准则在继承性方面的不足,通过扩充DL测度的组成要素,在其中增加一项旨在反映目标网络结构与当前网络结构拓扑差异度的描述长度指标,改进MDL准则,使其具备处理先验知识的能力。     

A novel etchant for revealing the prior austenite grain boundaries and matrix information in high alloy steels

To observe the prior austenite grain (PAG) boundaries in high-alloy steels, a novel etchant composed of oxalic acid, hydrogen peroxide, sodium bisulfite, and water has been developed. Etching with this system successfully revealed both the grain boundaries and the deformation bands within grains in high Co–Ni martensitic alloy steels. The carbon content of the alloys used in the test program varied from 0.23% to 0.35%, the cobalt content was in the range 9–13%, and the nickel content was in the range 8–11%. The specimens were austenitized at temperatures in the range 900–1200 °C. The rolling temperatures ranged from 700 °C to 1000 °C. The grain sizes in these alloys and those containing titanium were clearly revealed by optical microscopy following application of the new etchant.     

Bayesian Inference and Life Testing Plan for the Weibull Distribution in Presence of Progressive Censoring

This article deals with the Bayesian inference of unknown parameters of the progressively censored Weibull distribution. It is well known that for a Weibull distribution, while computing the Bayes estimates, the continuous conjugate joint prior distribution of the shape and scale parameters does not exist. In this article it is assumed that the shape parameter has a log-concave prior density function, and for the given shape parameter, the scale parameter has a conjugate prior distribution. As expected, when the shape parameter is unknown, the closed-form expressions of the Bayes estimators cannot be obtained. We use Lindley's approximation to compute the Bayes estimates and the Gibbs sampling procedure to calculate the credible intervals. For given priors, we also provide a methodology to compare two different censoring schemes and thus find the optimal Bayesian censoring scheme. Monte Carlo simulations are performed to observe the behavior of the proposed methods, and a data analysis is onducted for illustrative purposes.