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。     

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。     

Analytical framework for ex-post evaluation of transport projects: Lessons learnt on retrospective CBA

This study deals with the ex-post evaluation of ten major transport projects which were co-financed by the European Regional Development Fund and the Cohesion Fund in the period 2000–2013 and located across nine EU Member States. The original contribution of this study is represented by the combination of the traditional ex-post Cost–Benefit approach with a qualitative analysis. Specifically, a retrospective Cost–Benefit analysis is used to quantify the benefits and costs of the projects, then interviews to relevant stakeholders were carried out. Ex-post CBA, when appropriately implemented and integrated with qualitative evidence, represents a powerful tool for supporting decision-making processes and for policy lessons.     

Employing statistical model emulation as a surrogate for CFD

This work focuses on substituting a computationally expensive simulator by a cheap emulator to enable studying applications where running the simulator is prohibitively expensive. The procedure consists of two steps. In a first step, the emulator is calibrated to closely mimic the simulator response for a number of pre-defined cases. In a second step the calibrated emulator is used as surrogate for the simulator in the otherwise prohibitively expensive application. An appealing feature of the proposed framework contrary to other approaches is that the uncertainty on the emulator prediction can be determined. While the proposed framework is applicable in virtually all areas of natural sciences, we discuss the approach and evaluate its performance based on a typical example in the realm of computational wind engineering, namely the determination of the wind field in an urban area.     

Group decision support system for backbone-network reconfiguration

Modern power system is unprecedentedly vulnerable and the society is exposed to higher blackout risks. Therefore restoration is a key issue to power systems. Reasonable backbone-network reconfiguration is necessary for re-establishing the network and restoring loads quickly. In order to speed up network reconfiguration under security constraints, a group decision support system (GDSS) is developed. A unified data platform based on data warehouse is constructed to resolve inconsistencies of data, information and models. A three-stage restoration strategy and a two-layer restoration path searching method are proposed to establish restoration schemes. Multiple attribute decision-making is implemented to evaluate alternatives and consider various attributes about restoration speed and system security comprehensively. Group decision-making provides an aggregated cardinal ranking of candidate restoration schemes. The GDSS uses expert rules for qualitative analysis and numerical computation programs for quantitative analysis, and it can resolve the semi-structured decision problem of network reconfiguration efficiently. Restoration plan of Shandong power grid is developed with the proposed method and it has been adopted by State Grid Shandong Electric Power Company. Performance results of Shandong power grid of China demonstrate the feasibility and flexibility of the method.     

Bayesian model selection and parameter estimation for fatigue damage progression models in composites

A Bayesian approach is presented for selecting the most probable model class among a set of damage mechanics models for fatigue damage progression in composites. Candidate models, that are first parameterized through a Global Sensitivity Analysis, are ranked based on estimated probabilities that measure the extent of agreement of their predictions with observed data. A case study is presented using multi-scale fatigue damage data from a cross-ply carbon–epoxy laminate. The results show that, for this case, the most probable model class among the competing candidates is the one that involves the simplest damage mechanics. The principle of Ockham’s razor seems to hold true for the composite materials investigated here since the data-fit of more complex models is penalized, as they extract more information from the data.     

Sparse Bayesian Learning for non-Gaussian sources

In this paper, we show that in the multiple measurement vector model we can take advantage of having multiple samples to learn the properties of the distributions of the sources as part of the recovery process and demonstrate that this improves the recovery performance. We propose a method to solve the simultaneous sparse approximation problem using a mixture of Gaussians prior, inspired by existing Sparse Bayesian Learning approaches. We justify our proposed prior by showing that there are a number of signals modelled better by a mixture of Gaussians prior than the standard zero-mean Gaussian prior, such as communications signals which often have a multimodal distribution. We further show that this method can be applied to data distributed according to an alpha-stable distribution. We also show that our proposed method can be applied to compressed sensing of ultrasound images and demonstrate an improvement over existing methods.     

一种应急管理信息系统在海油企业中的应用

本文主要介绍一套适合海油企业实际业务应用的应急管理信息系统,系统通过模块化、组件化模式实现灵活应用配置,并可根据用户实际应用需求进行模块组合、拆解,为不同领域的企业和厂区搭建适用的定制化应用系统,满足企业日常安全应急管理需求,为应急指挥工作提供辅助决策支持。     

Bayesian network construction from event log for lateness analysis in port logistics

The handling of containers in port logistics consists of several activities, such as discharging, loading, gate-in and gate-out, among others. These activities are carried out using various equipment including quay cranes, yard cranes, trucks, and other related machinery. The high inter-dependency among activities and equipment on various factors often puts successive activities off schedule in real-time, leading to undesirable activity down time and the delay of activities. A late container process, in other words, can negatively affect the scheduling of the following ones. The purpose of the study is to analyze the lateness probability using a Bayesian network by considering various factors in container handling. We propose a method to generate a Bayesian network from a process model which can be discovered from event logs in port information systems. In the network, we can infer the activities’ lateness probabilities and, sequentially, provide to port managers recommendations for improving existing activities.     

Bayesian estimation of parametric uncertainties,quantification and reduction using optimal design of experiments for CO2 adsorption on amine sorbents

Uncertainty quantification plays a significant role in establishing reliability of mathematical models, while applying to process optimization or technology feasibility studies. Uncertainties, in general, could occur either in mathematical model or in model parameters. In this work, process of CO₂ adsorption on amine sorbents, which are loaded in hollow fibers is studied to quantify the impact of uncertainties in the adsorption isotherm parameters on the model prediction. The process design variable that is most closely related to the process economics is the CO₂ sorption capacity, whose uncertainty is investigated. We apply Bayesian analysis and determine a utility function surface corresponding to the value of information gained by the respective experimental design point. It is demonstrated that performing an experiment at a condition with a higher utility has a higher reduction of design variable prediction uncertainty compared to choosing a design point at a lower utility.