Analytical Investigation of Prior Austenite Grain Size Dependence of Low Temperature Toughness in Steel Weld Metal

Prior austenite grain size dependence of the low temperature impact toughness has been addressed in the bainitic weld metals by in situ observations.Usually,decreasing the grain size is the only approach by which both the strength and the toughness of a steel are increased.However,low carbon bainitic steel with small grain size shows a weakening of the low temperature impact toughness in this study.By direct tracking of the morphological evolution during phase transformation,it is found that large austenite grain size dominates the nucleation of intragranular acicular ferrite,whereas small austenite grain size leads to grain boundary nucleation of bainite.This kinetics information will contribute to meet the increasing low temperature toughness requirement of weld metals for the storage tanks and offshore structures.     

基于滑动窗口均值先验的非同构动态贝叶斯网络转换点检测算法*

为了放宽动态贝叶斯网络中的同构假设,提出非同构贝叶斯网络.基于此种情况,文中提出结合先验知识的可逆跳转的马尔可夫链蒙特卡洛算法(APK-RJ-MCMC).算法基本假设为如果一个时间点左右窗口中数据均值间的欧氏距离越大,则这个时间点作为转换点的可能性越高.基于上述假设,可得到关于每个时间点作为转换点可能性的粗略估计,将其作为先验知识调控可逆跳转的马尔可夫蒙特卡洛采样技术(RJ-MCMC)采样转换点时的生成、消除、转换动作的提议概率之比,进而调节状态跳转时的接受概率.在人工数据集和基因数据集上的实验表明,相比其它算法,APK-RJ-MCMC在转换点检测上具有更高的检测后验概率.     

A Bayes Sampling Allocation Scheme for Stratified Finite Populations With Hyperbinomial Prior Distributions

When sampling is carried out independently for the K strata of a finite stratified dichotomous population (defectives vs. standard items), and the number Z_i of defectives per stratum sample is observed, the corresponding probability function for X = (X_i , …, x_K ) is the product of hypergeometric functions which depend on the sample sizes n_i , the stratum sizes N_i , and the number of defectives M_i in the stratum (i = 1, …, K). It is assumed that prior information is available about the M_i 's which can be expressed, by suitable choice of the parameters a_i and b_i , as the product of independent hyperbinomial functions.

In each stratum the cost per observation is a known constant. Using squared error loss function, the prior Bayes risk is found for the linear function of interest,

and the optimum allocation of sample sizes is found, the one for which the prior Bayes risk is minimum when the total sampling budget is fixed.     

批量较小时的贝叶斯抽样方案的算法

首先介绍了贝叶斯抽样方案的原理，给出了批量较小时这类的简单精确算法。     

The referencing of prior art documents in European patents and applications

This paper explains the nature, origin and extent of the various documents and other prior art that may be cited in the course of the procedure from the filing of an EP patent application, through the search report and ‘A’ publication stages, the examination stage, and the eventual grant or refusal. The implications of the much weaker “duty of candour” in the European system of granting a bundle of national patents, compared with the US system, are described.     

基于贝叶斯理论的支持向量机综述

支持向量机(SVM)以其坚实的理论基础,和在机器学习领域表现出的良好推广性能,获得了越来越广泛的关注。为更好地推进其发展,科研工作者们借鉴统计学中经典的贝叶斯理论,做了大量工作,例如:引进贝叶斯理论中先验知识、后验概率等概念,改进支持向量机中的判别准则;或利用贝叶斯理论估计支持向量机中的参数w、正规化参数以及核参数等。目前已取得不错的效果,使支持向量机理论更具有实用价值。     

多先验融合的图像显著性目标检测算法

为了更加准确地检测出图像中的显著性目标，提出了多先验融合的显著性目标检测算法。针对传统中心先验对偏离图像中心的显著性目标会出现检测失效的情况，提出在多颜色空间下求显著性目标的最小凸包交集来确定目标的大致位置，以凸包区域中心计算中心先验。同时通过融合策略将凸包区域中心先验、颜色对比先验和背景先验融合并集成到特征矩阵中。最后通过低秩矩阵恢复模型生成结果显著图。在公开数据集MSRA1000和ESSCD上的仿真实验结果表明，MPLRR能够得到清晰高亮的显著性目标视觉效果图，同时F，AUC，MAE等评价指标也比现有的许多方法有明显提升。     

Effect of Prior Cold Deformation on the Stability of Retained Austenite in GCr15 Bearing Steel

In this work, the effect of prior cold deformation on the stability of retained austenite in GCr15 bearing steel was investigated after quenching and tempering treatment. The thermal stability was evaluated by calculating thermal activation energy for decomposition of retained austenite using differential scanning calorimeter. The mechanical stability was investigated according to the strain-induced martensitic transformation behavior of retained austenite under the standard compression testing. It is found that the prior cold deformation not only accelerates the carbide dissolution during the austenitization process but also contributes to the carbon partitioning in the tempering stage due to the higher density of phase boundaries, which results in the improvement of the thermal stability of retained austenite. Due to the enhanced carbide dissolution, the higher carbon content in the prior austenite will intensify the isotropic strain of martensitic transformation. As a consequence, the film-like retained austenite is likely to form under a higher hydrostatic pressure and thus shows a higher mechanical stability. Additionally, it is noteworthy that the benefits of the prior cold deformation to the stability of retained austenite would be saturated when the cold deformation degree is larger than 40%.     

Training pattern replication and weighted class allocation in artificial neural network classification

In some image classifications the importance of classes varies, and it is desirable to weight allocation to selected classes. Often the desire is to weight allocation in favour of classes that are abundant in the area represented by an image at the expense of the less abundant classes. If there is prior knowledge on the distribution of class occurrence, this weighting can be achieved with widely used statistical classifiers by setting appropriate a prioriprobabilities of class membership. With an artificial neural network, the incorporation of prior knowledge is more problematic. An approach to weight class allocation in an artificial neural network classification by replicating selected training patterns is presented. This investigation focuses on a series of classifications in which some classes were more abundant than others, but the same number of training cases were available for each class. By replicating the training patterns of abundant classes the representation of the abundant classes in the training set is increased, reflecting more closely the relative abundance of the classes in an image. Significant increases in classification accuracy were obtained by replicating the training patterns of abundant classes. Furthermore, in comparison against a discriminant analysis for the classification of synthetic aperture radar imagery, the results showed that training pattern replication could be used to weight class allocation with an effect similar to that of incorporating a prioriprobabilities of class membership into the discriminant analysis, and resulted in a significant 20.88%, increase in classification accuracy. This increase in classification accuracy was obtained without any new information, but was the result of making fuller use of what was available.