基于贝叶斯统计的测量系统误差控制方法仿真

针对传统误差控制方法存在耗时较长、误报率较高以及误差控制精度较低等问题,提出了一种基于贝叶斯统计的测量系统误差控制方法。通过含有区间灰数的模糊子集所处的故障状态,得到测量系统产生误差的条件,利用贝叶斯网络推理算法以及相关的区间运算规则,对规划模型求解,获取测量系统产生误差的概率以及相关部件的重要特征信息。根据求解结果以及相关的空间交汇原理构建待测点坐标、待测距离以及有关于经纬仪观测角的数据模型,通过分析测量系统产生误差的原因,选取满足误差存在条件的数据对网络进行训练,令其逼近非线性函数,完成对测量系统误差的有效控制。实验结果表明,所提方法能够快速、准确地对测量系统产生的误差进行控制。     

基于动态贝叶斯网络的海底管道点蚀疲劳损伤失效模型研究

目的研究海底管道在点腐蚀和腐蚀疲劳双重影响下的整个破坏过程,基于动态贝叶斯网络构建系统失效模型,对海底管道系统不同疲劳寿命下的失效概率进行预测。方法将点蚀疲劳损伤过程分为腐蚀点成核、腐蚀坑增长、短裂纹扩展和长裂纹扩展四个阶段,采用蒙特卡洛模拟方法对腐蚀点形成到短裂纹发生前的管道破坏过程进行分析,结合疲劳裂纹扩展的动态贝叶斯网络结构图,在充分考虑相关影响因素不确定性的基础上,为海底点蚀管道系统提出一种创新性的概率分析方法,对点蚀管道疲劳寿命的失效概率进行科学预测。结果结合实例分析,通过蒙特卡洛模拟方法,求解得出腐蚀坑增长转变为短裂纹扩展状态的临界裂纹尺寸为0.8mm。采用动态贝叶斯网络分析方法,对未经受维修保养的点蚀管道进行疲劳寿命预测,当管道运行到第35年时将会面临失效风险。结论所构建的模型可以对海底点蚀管道腐蚀疲劳寿命失效概率进行合理预测,通过观测相关影响参数的变化,及时更新预测结果,有助于为海底管道系统制定有效的维修策略。     

基于粒子群优化的超声共振谱法测量材料弹性常数

超声共振谱法是一种基于材料样品的自由共振频率,再反推其弹性常数的高精度材料力学属性求取方法,该方法在高Q值(品质因素)固体材料的弹性常数计算方面应用广泛。然而对于具有较高黏弹性阻尼的材料来说,受共振谱线平缓、共振频率提取困难等影响,该方法的应用存在一定困难。建立了关于弹性常数的贝叶斯模型,结合粒子群优化(PSO)的全局搜索能力,寻找弹性常数的最大后验概率密度点,最终实现材料的弹性估计。测量了皮质骨模拟材料的弹性常数,与传统方法相比较,基于PSO的超声共振谱方法对弹性常数初始值不敏感,可用于高阻尼材料弹性常数的测量。     

2019年中国硫磺市场数据统计与分析

介绍了2019年中国硫磺产量、进口量及市场数据。2019年中国硫磺产量约7440 kt,同比增长9.4%;进口硫磺量11730 kt,同比增长8.8%。2019年中国进口日本、韩国液体硫磺量为2039.9 kt,与2018年基本持平。2019年中国硫磺市场一路走低,年度降幅为50%,在此过程中相关数据屡次刷出新的记录。2019年全国港口库存硫磺均值数达到1890 kt,数据最高点达到2750 kt。2020年中国硫磺国产数量继续有增长预期,而进口数量或许要看港口库存消耗及下游的整体表现,综合来看,2020年的硫磺市场背负的压力并不比前几年小。     

Performance analysis of empirical models for predicting rock mass deformation modulus using regression and Bayesian methods

Deformation modulus of rock mass is one of the input parameters to most rock engineering designs and constructions. The field tests for determination of deformation modulus are cumbersome, expensive and time-consuming. This has prompted the development of various regression equations to estimate deformation modulus from results of rock mass classifications, with rock mass rating (RMR) being one of the frequently used classifications. The regression equations are of different types ranging from linear to nonlinear functions like power and exponential. Bayesian method has recently been developed to incorporate regression equations into a Bayesian framework to provide better estimates of geotechnical properties. The question of whether Bayesian method improves the estimation of geotechnical properties in all circumstances remains open. Therefore, a comparative study was conducted to assess the performances of regression and Bayesian methods when they are used to characterize deformation modulus from the same set of RMR data obtained from two project sites. The study also investigated the performance of different types of regression equations in estimation of the deformation modulus. Statistics, probability distributions and prediction indicators were used to assess the performances of regression and Bayesian methods and different types of regression equations. It was found that power and exponential types of regression equations provide a better estimate than linear regression equations. In addition, it was discovered that the ability of the Bayesian method to provide better estimates of deformation modulus than regression method depends on the quality and quantity of input data as well as the type of the regression equation.     

基于K-均值聚类与贝叶斯判别的我国煤矿顶板灾害事故安全评价

顶板事故是我国煤矿安全生产中的重大隐患,开展顶板灾害事故安全评价,识别顶板灾害危险性是煤矿安全生产亟待解决的问题。本文在文献调研的基础上,对影响顶板灾害的影响因素进行分析,从自然因素、技术因素和管理因素三个方面构建了顶板灾害安全评价指标体系,结合专家意见对各评价指标进行量化,采用K-均值聚类与贝叶斯判别的方法对我国顶板灾害安全水平进行评价,以我国煤矿实际样本数据为研究对象开展实证分析。研究结果表明:基于K-均值聚类与贝叶斯判别的煤矿顶板安全评价方法具有较高的准确度,能够用于定量化描述煤矿顶板灾害危险,具有一定的应用前景。     

基于后验预测分布的机器人焊接质量监控研究北大核心

针对KUKA机器人焊接质量监测工作量大、抽样样本小的特点,提出一种基于后验预测分布的贝叶斯动态监控方法。从历史数据中选择合适的数据,计算先验分布的超参数;再结合当前样本构建服从负二项分布的后验预测分布,实时计算控制限,实现对焊接质量的动态监测。结果表明:该方法优于传统似然估计法,有更强的异常检出力和稳健性。     

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades, and a plethora of methods have been proposed. But, a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefinedmanually. We propose a flexible nonparametric Bayesian generative model for count-value networks, which can allow K to increase as more and more data are encountered instead of to be fixed in advance. The Indian buffet process was used to model the community assignment matrix Z, and an uncollapsed Gibbs sampler has been derived.However, as the community assignment matrix Z is a structured multi-variable parameter, how to summarize the posterior inference results and estimate the inference quality about Z, is still a considerable challenge in the literature. In this paper, a graph convolutional neural network based graph classifier was utilized to help to summarize the results and to estimate the inference quality about Z. We conduct extensive experiments on synthetic data and real data, and find that empirically, the traditional posterior summarization strategy is reliable.     

Fault detection and diagnostic method of diesel engine by combining rule-based algorithm and BNs/BPNNs

The stable operation of diesel engine is critical to the normal production of the industry, and the prevention, monitoring, and identification of faults are of great significance. At present, the fault research on diesel engines still has some defects, such as only few types of faults diagnosis are identified, the accuracy of fault diagnosis is still low, and fault identification is located at a fixed speed. A novel fault detection and diagnostic method of diesel engine by combining rule-based algorithm and Bayesian networks (BNs) or Back Propagation neural networks (BPNNs) is proposed. The signals are processed by wavelet threshold denoising and ensemble empirical mode decomposition. The signal-sensitive feature values are extracted from the decomposed intrinsic mode function. Seven faults are roughly identified using rule-based algorithm and finely identified using BNs or BPNNs. Results show the proposed fault diagnosis method has a good diagnostic performance for a wide range of rotation speeds when the training data for BNs and BPNNs are from fixed speeds. In addition, the influences of the layers of decomposed signals, sensor noise and external excitation interference on the fault diagnostic performance are also researched.     

Modeling project criticality in IT project portfolios

Today's IT project portfolios (ITPP) contain many projects and varied interdependencies. Depending on a project's criticality to the ITPP, a failure can have massive consequences. However, existing methods usually only assess overall project portfolio risk and do not account for the criticality of single projects and their dependencies. Applying Bayesian network modeling to ITPPs, we bridge this gap and extend the current body of knowledge for the information systems and project management literatures. Our new method analyzes single projects' criticality in a portfolio context by considering both transitive dependencies and different dependency types in an integrated way. Since we demonstrate that single projects' criticality can vary substantially, being aware of which projects are critical is a key success factor for ITPP management. For practitioners, our method provides a straightforward procedure to enhance ITPP risk management.