农村公路典型路段事故风险评价方法

农村公路急弯、陡坡、路侧险要等典型路段往往是事故易发多发路段。在现阶段农村公路安保工程资金有限的情况下,需对既有典型路段的事故风险进行定量评价和排序,为隐患路段的排查和安保工程分期实施提供依据。在现有研究成果的基础上,构建典型路段事故风险评价指标体系,提出一种基于贝叶斯网络的农村公路典型路段事故风险评价方法。该方法能有效集结专家知识和经验定量评价典型路段(包括单一路段和组合路段)的事故风险,并可以随着数据的更新不断完善模型。在农村公路事故资料相对缺乏的情况下,该方法具有较强的实用性。     

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.     

融合电容层析成像先验信息的集合卡尔曼滤波统计估计

提出融合先验信息的电容层析成像(ECT)系统信息融合成像法——基于Bayesian重建算法的集合卡尔曼滤波融合算法(EnKF)。其优势在于能充分利用流体动力方面的先验信息,进一步修正原始迭代算法的重建图像质量。Bayesian重建算法在重建图像的迭代过程充分考虑噪声和图像的概率分布,完成初步成像过程。集合卡尔曼滤波算法利用先验信息实现对多相流流体流动的预测,继而改善成像质量。此外,为了便于估算误差的协方差,多相流的相分布作为EnKF融合方法的估计对象,用状态向量空间的灰度差分统计描述,明显改善了通过状态向量空间模型获得的统计估算结果。仿真和试验的结果都充分表明了在ECT系统中集合卡尔曼滤波融合方法的可行性。     

Iterative improvement of parameter estimation for model migration by means of sequential experiments

Determining an optimal design for estimation of parameters of a class of complex models expected to be built at a minimum cost is a growing trend in science and engineering. We adopt a scale-bias adjustment migration strategy for integrating base and new models based on similar nature underlying processes. Further, we propose a Bayesian sequential algorithm for obtaining the statistically most informative data about the migrated model for use in parameter estimation. The benefits of the proposed strategy over traditional approaches presented in recent reported work are demonstrated using Monte Carlo simulations.     

Detection of Changes of Multiple Poisson Processes Monitored at Discrete Time Points Where the Arrival Rates are Unknown

Abstract

We observe a Poisson process in several categories where the arrival rates in each category change at some unknown integer. For some of these categories the arrival rates increase, whereas in other categores the arrival rates decrease. The point at which the process changes may be different for each category. We assume that both the arrival rates for each category as well as the change-point are unknown. We develop procedures for detecting when a change has occurred in at least one of these categories. We provide some numerical results to illustrate the effectiveness of the detection procedures.     

Monitoring a Poisson process subject to gradual changes in the arrival rates

Abstract

We look at a Poisson process where the arrival rates change from a known λ₁ to a known λ₂. Whereas in most of the literature the change-point is abrupt, we model the more realistic assumption that states that the change happens gradually over a period of time η where η is known. We calculate the probability that the change has started and completed. We also look at optimal stopping rules assuming that there is a cost for a false alarm and a cost per time unit to stop early. We conclude with some numerical results.     

Myopicx sequential allocation procedures for bayesian simultaneou estimation

Observations may be taken from two populations where the simultaneous estimation of the population means is of interest. While the total number of observations, or horizon, is fixed, the observations may be taken sequentially with the decision to take an observation from one population or the other at any intermediate stage depending on past information. Such sampling schemes are called sequential allocation procedures or policies. A Bayesian exponential family distributions. For loss and prior distributions of the natural conjugate form, myopic or one—step—ahead sequential allocation procedures are derived and shown to be asymptotically optimal. Also, the asymptotic efficiency of one allocation to another is defined and then considered for the best nonrandom policy and the optimal policy.     

Modeling of single-lap composite adhesive joints under mechanical and thermal loads

Two-dimensional (plane-stress and plane-strain) theoretical models are presented for stress analysis of adhesively bonded single-lap composite joints subjected to either thermal or mechanical loading or a combination thereof. The joints consist of similar/dissimilar orthotropic or isotropic adherends and an isotropic adhesive interlayer. The governing differential equation of the problem is obtained using a variational method which minimizes the complementary strain energy in the bonded assembly. In this formulation, through-thickness variation of shear and peel stresses in the interlayer is considered. Both shear and normal traction-free boundary conditions are exactly satisfied. Peel and shear stresses obtained from plane-strain analytical models considering a homogeneous adhesive interlayer are in close agreement with those of the finite element predictions. A systematic parametric study is also conducted to identify an ideal set of geometric and material parameters for the optimal design of single-lap composite joints.     

Colour‐appearance modeling using feedforward networks with Bayesian regularization method— Part I: Forward model

In this article, a method of predicting colour appearance (from colorimetric attributes to colour‐appearance attributes, i.e., forward model) using an artificial neural network is presented. The neural network model developed is a multilayer feedforward neural network model for predicting colour appearance (FNNCAM for short). The model was trained by LUTCHI colour‐appearance datasets. The Levenberg–Marquardt algorithm is incorporated into the back‐propagation procedure to accelerate the training of FNNCAM and the Bayesian regularization method is applied to the training of neural networks to improve generalization. The results of FNNCAM obtained are quite promising. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 424–434, 2000