用马尔可夫─贝叶斯方法预测孔隙度的分布

用地震资料预测储层参数可以为勘探和开发提供有价值的信息，可以提高钻井的成功率和布井的准确性。但地震所提供的信息存在着局限性，一般来说其精度较低。因此，要有效地预测储层参数，必须把地震、测井及地质学家的经验和推测结合起来。马尔可夫－贝叶斯方法就是这样一种方法。此方法不仅可以预测控制井点较少的区域，而且在没有井的区域，通过地质学家对点散图的解释，仍然可以得到区域储层参数的分布。本文用此方法仅做了孔隙度的估算，其它储层参数的估算道理相同。     

Effect of welding process parameters on embrittlement of Grade P92 steel using Granjon implant testing of welded joints

Precipitation of Cr-rich carbides, diffusible hydrogen content and heterogeneous microstructure formation across the weldments makes heat-affected zone (HAZ) susceptible to intergranular cracking and makes weldability of creep strength enhanced ferritic (CSEF) Grade P92 steel a critical issue. In the present research work, the Granjon implant test and mercury method (for diffusible hydrogen measurement) have been performed on Grade P92 steel welded specimens to study the effect of welding parameters on diffusible hydrogen levels and their subsequent effect on hydrogen-assisted cracking (HAC). The weld metal was deposited by a shielded metal arc welding process on Grade P92 steel samples using P92 matching filler. The three different welding conditions are used to measure the diffusible hydrogen level in the deposited metal. Granjon implant test was performed to evaluate HAZ HAC susceptibility with similar welding conditions which were used in the mercury method. Lower critical stress (LCS) was also evaluated using the Granjon implant test. The higher susceptibility of CSEF Grade P92 steel welded plate towards HAZ HAC was noticed in case of lower heat input or higher diffusible hydrogen content. However, by considering LCS, fracture mode and diffusible hydrogen content, the weld deposited using the highest heat input (condition III) offers great resistance to HAZ HAC.     

Bayesian prediction for business mortality analysis

Summary This paper is concerned with business mortality analysis in a Bayesian setting. We assume that a businessman startsN businesses at different points of time and at a certain epoch referred to as the ‘present’, the failure times of the failed businesses and the survival times of the still surviving businesses are recorded. Bayesian prediction for business mortality or survival up to a future time pointt ₀ is made under the assumptions of a Weibull business survival distribution (WBSD) and the squared error loss function (SELF). The results are extended to the situation where one of the observed times of business failure may possibly be an outlier.     

A predictivistic interpretation of the multivariatet distribution

Summary De Finetti type theorems characterize models in terms of invariance. The idea is to take observables, postulate symmetry and then represent the model as a mixture of standard parametric models. If additional conditions are specified, then the mixing measure can be determined. Invariance under the action of special groups of orthogonal transformations may give results on mixtures of parametric normal distributions (Diaconis, Eaton and Lauritzen, 1992). The additional conditions required to determine the mixing measure in this case can be obtained using results in Diaconis and Ylvisaker (1979, 1985). From these results, we obtain a predictivistic characterization of the multivariatet distribution. Furthermore, we state conditions under which then-dimensional law of sequences of random variables is a location mixture of multivariatet distributions. The results are extended to the case of sequences of orthogonally invariant random vectors.     

基于自适应融合的手掌静脉增强方法

针对掌脉轮廓不清晰，图像对比度低、亮度低，进而导致识别性能降低的现象，提出一种自适应融合的手掌静脉增强方法。首先，基于暗原色先验（DCP）去雾算法，根据掌脉图像变异系数自适应选择去雾系数，得到DCP增强图像，并且基于部分子块重叠直方图均衡（POSHE）算法得到POSHE增强图像；然后，将图像分为16个子块，依据图像灰度均值与标准差确定各子块权重；最后，根据各子块权重对DCP和POSHE增强图像进行自适应融合，得到最终增强图像。该方法既保留了DCP算法在增强图像对比度和亮度的同时不引入明显噪声的优点，又保留了POSHE算法在增强图像对比度和亮度的同时不损失局部细节的特点；同时，两者的自适应融合既解决了DCP图像阴影部分掌脉缺失现象，又削弱了POSHE产生的块效应。在对两个公开库和自建库分别进行的实验中，三个数据库的等错误率分别为0.0004、0.0472、0.0579，识别率分别为99.98%、94.27%、92.05%。实验结果表明，与现有的图像增强方法相比，该方法降低了等错误率，提高了识别精度。     

综合单元验前信息的系统可靠度Bayes评估

针对目前可靠性评估方法的不足,提出了综合单元验前信息的系统可靠度Bayes评估方法。该方法根据验前信息确定各单元的验前分布,将寿命型单元等效为成败型单元,将混联系统等效为串联系统,从而将复杂系统转化为成败型单元串联系统,在此基础上利用各单元的验后前两阶矩进行系统可靠度综合,能够较好地解决传统方法对数据依赖性过强、不能有效综合验前信息等缺陷。最后通过仿真示例验证了该方法的合理性与有效性。     

Exploiting object features in deep gaze prediction models

The human visual system analyzes the complex scenes rapidly. It devotes the limited perceptual resources to the most salient subsets and/or objects of scenes while ignoring their less salient parts. Gaze prediction models try to predict the human eye fixations (human gaze) under free-viewing conditions while imitating the attentive mechanism. Previous studies on saliency benchmark datasets have shown that visual attention is affected by the salient objects of the scenes and their features. These features include the identity, the location, and the visual features of objects in the scenes, beside to the context of the input image. Moreover, the human eye fixations often converge to the specific parts of salient objects in the scenes. In this paper, we propose a deep gaze prediction model using object detection via image segmentation. It uses some deep neural modules to find the identity, location, and visual features of the salient objects in the scenes. In addition, we introduce a deep module to capture the prior bias of human eye fixations. To evaluate our model, several challenging saliency benchmark datasets are used in the experiments. We also conduct an ablation study to show the effectiveness of our proposed modules and its architecture. Despite its fewer parameters, our model has comparable, or even better performance on some datasets, to the state-of-the-art saliency models.     

Equipment failure rate updating-Bayesian estimation

The paper presents a Bayes' method for augmenting generic equipment failure data with a prior distribution - predicated on the evidence, e.g., plant data - resulting in a posterior distribution. The depth of the evidence is significant in shaping the characteristics of the posterior distribution. In conditions of insufficient data about the prior distribution or great uncertainty in the generic data sources, we may use "constrained non-informative priors". This representation of the prior preserves the mean value of the failure rate estimate and maintains a broad uncertainty range to accommodate the site-specific event data. Although the methodology and the case study presented in this paper focus on the calculation of a time-based (i.e., failures per unit time) failure rate, based on a Poisson likelihood function and the conjugate gamma distribution, a similar method applies to the calculation of demand failure rates utilizing the binomial likelihood function and its conjugate beta distribution.     

正态分布在投资决策中的应用

在投资决策中,经常会遇到需要在不确定状态下决策的问题,在此情况下,单凭主观经验或客观资料做资料做决策,其盲目性和风险性均较大。如果在做决策之前进行某种抽样实验,得根据抽样结果所提供的信息对影响的各种自然状态增加了解后再做决策,可以提高决策的正确性,本文对如何应用正态分布,求出最优化决策行动的问题进行了探讨。