基于贝叶斯原理的小子样试验导航误差评定方法

针对鉴定试验花费大、周期长的问题,提出一种水下无人航行器导航误差小子样贝叶斯试验CEP评定方法。该方法综合运用大量验前子样,对其进行相容性检验,以确定有效的验前子样,并运用统计推断理论获取验前信息。根据在验收试验中获取的验后子样获取验后信息,利用贝叶斯理论推出无人航行器满足CEP指标的概率。实例结果表明了方法的有效性和实用性。     

测量导致设备性能退化条件下的剩余寿命建模分析

测量本身导致设备性能退化的情况是存在的,然而这个问题往往被研究者们所忽视。文中针对这种情况下的剩余寿命建模进行了研究。首先,在自然退化模型的基础上建立有损测量退化模型;其次给出了相应模型的参数估计方法和基于贝叶斯理论的设备剩余寿命的实时更新方法。最后通过一个仿真示例证明了研究这个问题的意义和文中建模的有效性。     

一种基于轴承剩余寿命预测的状态维修优化决策方法

针对以往研究中状态维修的关键环节,剩余寿命预测不能更新的问题,提出一种融合贝叶斯方法的神经网络退化预测模型,实现利用实时传感信号动态预测轴承的剩余寿命分布。检验结果表明,该模型对轴承的剩余寿命预测比较精确。基于更新的剩余寿命分布,建立了以费用率最小为目标的轴承状态维修优化决策模型,求解得到最优的轴承预防性更换时间。     

基于仿真的某型号车辆底盘可信性评估

对某型号车辆底盘,在子系统服从任意失效和修复分布、失效数据不充分和可靠性试验小样本的情况下,采用Bayes与Monte- Carlo方法评估复杂可修系统的可信性（可靠性和维修性）。根据“先来先修”的队列原则,利用优先级和事件表建立了复杂可修系统的可信性仿真的逻辑关系,根据对运行特征的识别,得到复杂系统可信性的统计值,为可靠性设计和维修性管理提供依据。     

基于多源信息的发动机无故障数据可靠性评估

针对弹用发动机无故障数据可靠性评估问题,提出基于多源信息的可靠性评估方法;综合应用修正极大似然估计和Bayes估计方法,给出信息源权值的计算公式和可靠性评估步骤,最后给出应用实例.计算结果表明,多源信息融合方法计算简单,评估结果合理,具有一定工程应用价值.     

An analysis of the transition proportion for binarization in handwritten historical documents

In this paper, we will present a mathematical analysis of the transition proportion for the normal threshold (NorT) based on the transition method. The transition proportion is a parameter of NorT which plays an important role in the theoretical development of NorT. We will study the mathematical forms of the quadratic equation from which NorT is computed. Through this analysis, we will describe how the transition proportion affects NorT. Then, we will prove that NorT is robust to inaccurate estimations of the transition proportion. Furthermore, our analysis extends to thresholding methods that rely on Bayes rule, and it also gives the mathematical bases for potential applications of the transition proportion as a feature to estimate stroke width and detect regions of interest. In the majority of our experiments, we used a database composed of small images that were extracted from DIBCO 2009 and H-DIBCO 2010 benchmarks. However, we also report evaluations using the original (H-)DIBCO?s benchmarks.     

Classification of ground glass opacity lesion characteristic based on texture feature using lung CT image

Lung cancer causes a high mortality rate in the world than any other cancers. That can be minimised if the symptoms and cancer cells have been detected early. One of the techniques used to detect lung cancer is by computed tomography (CT) scan. CT scan images have been used in this study to identify one of the lesion characteristics named ground glass opacity (GGO). It has been used to determine the level of malignancy of the lesion. There were three phases in identifying GGO: image cropping, feature extraction using grey level co-occurrence matrices (GLCM) and classification using Naïve Bayes Classifier. In order to improve the classification results, the most significant feature was sought by feature selection using gain ratio evaluation. Based on the results obtained, the most significant features could be identified by using feature selection method used in this research. The accuracy rate increased from 83.33% to 91.67%, the sensitivity from 82.35% to 94.11% and the specificity from 84.21% to 89.47%.     

Probabilistic enhancement of approximate indexing in metric spaces

Some approximate indexing schemes have been recently proposed in metric spaces which sort the objects in the database according to pseudo-scores. It is known that (1) some of them provide a very good trade-off between response time and accuracy, and (2) probability-based pseudo-scores can provide an optimal trade-off in range queries if the probabilities are correctly estimated. Based on these facts, we propose a probabilistic enhancement scheme which can be applied to any pseudo-score based scheme. Our scheme computes probability-based pseudo-scores using pseudo-scores obtained from a pseudo-score based scheme. In order to estimate the probability-based pseudo-scores, we use the object-specific parameters in logistic regression and learn the parameters using MAP (Maximum a Posteriori) estimation and the empirical Bayes method. We also propose a technique which speeds up learning the parameters using pseudo-scores. We applied our scheme to the two state-of-the-art schemes: the standard pivot-based scheme and the permutation-based scheme, and evaluated them using various kinds of datasets from the Metric Space Library. The results showed that our scheme outperformed the conventional schemes, with regard to both the number of distance computations and the CPU time, in all the datasets.     

Multivariate spatial models of excess crash frequency at area level: Case of Costa Rica

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data.     

Angewandte Multivariate Statistik

Multivariate quality characteristics are often monitored using a single statistic or a few statistics. However, it is difficult to determine the causes of an out-of-control signal based on a few summary statistics. Therefore, if a control chart for the mean detects a change in the mean, the quality engineer needs to determine which means shifted and the directions of the shifts to facilitate identification of root causes. We propose a Bayesian approach that gives a direct answer to this question. For each mean, an indicator variable that indicates whether the mean shifted upward, shifted downward, or remained unchanged is introduced. Prior distributions for the means and indicators capture prior knowledge about mean shifts and allow for asymmetry in upward and downward shifts. The mode of the posterior distribution of the vector of indicators or the mode of the marginal posterior distribution of each indicator gives the most likely scenario for each mean. Evaluation of the posterior probabilities of all possible values of the indicators is avoided by employing Gibbs sampling. This renders the computational cost more affordable for high-dimensional problems. This article has supplementary materials online.