Extensions of the uncertainty quantification of common cause failure rates

Uncertainties in common cause event observation, documentation and interpretation are taken into account by conditional probabilities and generalized impact vector weights that separate single and double events of a specific multiplicity in a single observation. Distributions and moments of common cause failure (CCF) rates of a system are obtained in terms of the weights by using probability generating functions, combining assessment uncertainties and statistical uncertainties. These results are then used to generate effective plant-specific input data to general empirical Bayes estimation methods to combine data from many plants. The posterior output yields CCF probabilities for standby safety system fault tree analysis or probabilistic safety assessments of a target plant.     

多维参数线性经验 Bayes 估计的一致收敛速度

在多维参数模型下,本文构造了一个线性经验 Bayes 估计,并证明出其一致收敛速度为1。     

核事故应急中的贝叶斯决策模型研究

通过比较分析 ,发现风场变化的不确定性对核事故应急决策有很大的影响。针对这种情况 ,以贝叶斯 (Bayes)风险决策原理为理论基础 ,结合核事故应急的相关知识提出了在计算机上实现的核事故应急中的贝叶斯决策模型 (NBDM) ,以便改进风场的不确定性 ,提高ESY中决策的精度。同时还与Alexandre方法进行了比较 ,最后给出了一个计算实例分析     

基于Bayes准则自适应Gabor系数缩减的信号恢复

信号在联合时频平面内是相对稀疏的,鉴于此,本文通过假设信号Gabor展开系数先验分布和噪声Gabor展开系数的先验分布,然后应用Bayes准则从含噪信号Gabor展开系数的后验分布估计未知信号的Gabor展开系数,实现时变滤波.数值实验表明该方法是合理有效的.     

应用MCEM算法估计一阶双重线性时间序列模型中的参数

本文利用Monte Carlo EM算法实现了对一阶双重线性时间序列模型的参数的估计.推出了估计模型中两个未知参数m与σ2的迭代步骤,并对模型进行了模拟计算,给出了模拟计算的结果.     

基于先验融合的Bayes递归精度评定方法

制导工具误差分离与落点精度分析是武器精度评定中最为关心的问题之一.为充分融合观测信息进行精度评估,在对制导工具误差系数的测试值进行先验融合的基础上,结合遥外弹道观测数据求解制导工具误差系数,并将相应的制导工具误差与弹道差的Bayes实时融合结果进行了比较.建立了递归的落点精度评估方法,将由地面测试先验导出的落点先验与落点观测数据进行加权融合,给出落点评定结果.理论分析和仿真计算表明,信息融合后得出的评定结果更加稳健.     

基于参数降阶模态的分层贝叶斯在线裂纹检测

为了保证检测的稳定性和准确性,并且有效处理参数的关联性与强非线性,提出一种基于参数降阶模态的分层贝叶斯在线裂纹检测方法。提出一种用于递归输入、状态和参数估计的分层贝叶斯滤波器,它通过使用空间不完整和噪声可以仅输出测量振动。将所寻求的参数视为具有有限个更新状态的随机变量,参数状态的动力学由改进估计策略控制,该策略能够探索参数空间并识别目标值。进一步利用参数降阶模型对所提出的方法进行裂纹识别。最后通过航空航天应用中的真实组件对提出方法进行仿真,结果显示提出方法检测准确性和稳定性较高,证明了提出方法的有效性。     

Review: Heart Diseases Detection by Machine Learning Classification Algorithms

Most human deaths are caused by heart diseases. Such diseases cannot be efficiently detected for the lack of specialized knowledge and experience. Data science is important in healthcare sector for the role it plays in bulk data processing. Machine learning (ML) also plays a significant part in disease prediction and decision-making in medical care industry. This study reviews and evaluates the ML approaches applied in heart disease detection. The primary goal is to find mathematically effective ML algorithm to predict heart diseases more accurately. Various ML approaches including Logistic Regression, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), t-Distributed Stochastic Neighbor Embedding (t-SNE), Nave Bayes, and Random Forest were utilized to process heart disease dataset and extract the unknown patterns of heart disease detection. An analysis was conducted on their performance to examine the effecacy and efficiency. The results show that Random Forest out-performed other ML algorithms with an accuracy of 97%.     

全极化SAR图像中相干矩阵特征值及其应用

推导了全极化合成孔径雷达相干矩阵的特征值和特征值指数与散射振幅函数的直接关系,并用全极化散射的植被模型讨论了特征值指数与植被稀疏程度的关系,结合Bayes极大似然分类法讨论了特征值指数在全极化多视AirSAR图像分类中的应用,最后用Monte Carlo模拟法给出了理论分类误差。     

多智能方法及其融合系统在化工过程故障诊断中的应用

简要介绍了决策树、神经网络、K近邻等五种智能方法,提出了一种将这五种智能方法相融合的故障诊断系统,并将其应用在催化裂化装置上.通过对这五种智能方法及其融合系统的性能考察表明,融合策略发挥了各方法的优点,回避了它们的缺点,提升了系统的整体性能,其召回率为100%,精确度为98%.