基于分布式网络的分步滤波算法的改进

在实际的多传感器动态系统中,传感器之间网络通讯时要进行坐标转换,该过程具有的不确定性将对融合结果产生不利影响。文章对已有的基于分布式网络结构的分步式滤波算法进行了改进,考虑了这种不确定性产生的误差。在某一时刻所有传感器测量信息被获得后,先将这些测量信息进行坐标统一处理,再用中心坐标系统下的测量信息依次对先前的估计逐个进行滤波更新,最终得到该时刻的全局融合估计。文章通过计算机仿真说明了该算法的精确度不仅与集中式算法的精确度仍然相当,而且具有更实际的应用价值。     

A Bayesian model to assess rail track geometry degradation through its life-cycle

One of the major drawbacks in rail track investments is the high level of uncertainty in maintenance, renewal and unavailability costs for the Infrastructure Managers (IM) during the life-cycle of the infrastructure. Above all, rail track geometry degradation is responsible for the greatest part of railway infrastructure maintenance costs. Some approaches have been tried to control the uncertainty associated with rail track geometry degradation at the design stage, though little progress has improved the investors' confidence. Moreover, many studies on rail track life-cycle cost modelling tend to forget the dynamic perspective in uncertainty assessments and do not quantify the expected reduction of the uncertainty associated with degradation parameters as more inspection data is collected after operation starts.     

Refined parameter and uncertainty estimation when both variables are subject to error. Case study: estimation of Si consumption and regeneration rates in a marine environment

The problem of estimating parameters and their uncertainty from experimental measurements in marine ecosystems is a common task and often necessitates solving nonlinear equations. If the measurements are subject to individually varying errors (i.e., heteroscedastic data), the parameters are often estimated using a Weighted Least Squares (WLS) method. For estimating the parameter uncertainties, a linearized expression for the covariance matrix exists. Yet, both methods assume that the errors on the independent variable, also called “input”, is negligible, which is often not true. For instance, in order to determine uptake and regeneration rates of silicic acid by phytoplankton, concentration and isotopic abundance measurements are performed at the beginning (input) and at the end (output) of an incubation experiment. Here, the so-called input and output are measurements of the same quantities, i.e., determined in exactly the same way, only differing by the time at which the measurements were performed. Clearly, there is no reason to assume that the input measurements are subject to less error than the output measurements. We propose a refinement of the two abovementioned estimation methods which enlarges their applicability to cases where input noise is not negligible. The refined methods are evaluated on the uptake and regeneration processes of silicic acid and compared to the original procedures using Monte-Carlo simulations. The results reveal a smaller bias for the refined WLS estimator compared with the original one. An additional advantage of using the refined WLS cost function is that its residual value can be interpreted as a sample from a χ² distribution. This property is especially useful because it enables an internal quality control of the results. In addition, the parameter uncertainty estimation is significantly improved. By neglecting the effect of the input noise, a (potentially) important origin of the parameter variation is simply ignored. Therefore, without the refinement, the parameter uncertainties are systematically underestimated. Using the refined method, this systematic error disappears and on the whole, the parameter standard deviations are accurately estimated.     

列车轴承内径的测量方法和不确定度分析

针对传统测量方法很难保证轴承内圈直径尺寸的高精度自动测量,提出一种在十字定位测量轴承内径基础上的比较测量方法,它可以改善传统测量方法的不足。介绍该测量系统设计的整体方案,对其测量的不确定度作了分析。该测量系统实现了自动校准、定位和测量,减小了人工测量误差,提高了测量精度,有力地保证了轴承选配数据的准确性,具有很高的实用价值。     

轨道电路传输电压测量不确定度评估方法研究

信号动态检测系统对轨道电路传输特性进行检测,可以发现信号设备存在的隐患。随着设备应用水平的提高,精度要求更高。介绍了轨道电路传输电压测量不确定度的评估方法,以此来验证现有设备的测量精度。     

蒙特卡洛法评定测量不确定度的理解和应用

结合JJF 1059.2—2012《用蒙特卡洛法评定测量不确定度》和实际应用蒙特卡洛法(MCM)评定测量不确定度的经验,从测量不确定度评定的主要操作流程、测量模型要求、评定方法、GUM的不确定度验证、评定结果差异和评定结果应用等方面比较了MCM与GUM的差异,分析MCM中使用自适应法的客观性,并总结应用MCM评定测量不确定度的优势,有助于相关人员深入了解2种测量不确定度评定方法的要求,便于在实际工作中正确理解和掌握内容实质。     

液化气体铁路罐车容积测量不确定度的评定

对采用几何测量法检定的液化气体铁路罐车容积进行不确定度评定,建立数学模型,确定灵敏系数,从内竖直径测量、内横直径测量、内总长测量、封头容积和筒体变形等方面评定该方法的标准不确定度。几何测量法检定结果的扩展不确定度为3.2×10-3（k=2）,满足液化气体铁路罐车容积检定规程的计量性能要求。     

Paying for performance: Uncertainty, asymmetric information and the payment model

Privatisation has led to a growing interest in more complex contractual forms designed to give public transport operators the incentives for effort that maximise value for money. Contract theory provides a rich research basis for selecting an appropriate contractual form, with an emphasis on the effects of uncertainty and asymmetric information. To date, however, there have been few applications of contract theory in the field of transport. This paper identifies the key empirical results from the multi-disciplinary literature to help transport researchers and practitioners place contractual decision-making in the broader theoretical context, suggesting aspects of transport contracting that merit future research.     

FFT算法截断不确定度评估

截断不确定度是快速傅里叶算法(Fast Fourier Transform,FFT)不确定度评估中的一个重要来源。将截断不确定度分解为：频谱泄露不确定度、加窗和栅栏效应不确定度进行分析。将采样周期个数分解为整部部分和小数部分,确定小数部分引起的频谱泄露误差区间,以此为基础给出频谱泄露不确定度的评估方法;将加窗和栅栏效应引入的不确定度作为一个整体分析, 避开分量之间的相关性,以三角窗为例给出了一个通用的评估方法。之后利用GUM(Guide to the Expression of Uncertainty in Measurement)中的B类方法对其进行评定,评定时假设舍入测量不确定度为反正弦分布,并通过对频谱泄露不确定度、加窗和栅栏效应不确定度进行合成,得到最终的截断不确定度,在此基础上建立了FFT截断不确定度的通用评估方法。     

Urban Traffic Flow Prediction Using a Spatio-Temporal Random Effects Model

Traffic prediction is critical for the success of intelligent transportation systems (ITS). However, most spatio-temporal models suffer from high mathematical complexity and low tune-up flexibility. This article presents a novel spatio-temporal random effects (STRE) model that has a reduced computational complexity due to mathematical dimension reduction, with additional tune-up flexibility provided by a basis function capable of taking traffic patterns into account. Bellevue, WA, was selected as the model test site due to its widespread deployment of loop detectors. Data collected during the 2 weeks of July 2007 from 105 detectors in the downtown area were used in the modeling process and traffic volumes predicted for 14 detectors for the entire month of July 2008. The results show that the STRE model not only effectively predicts traffic volume but also outperforms three well-established volume prediction models, the enhanced versions of autoregressive moving average (ARMA) and spatiotemporal ARMA, and artificial neural network. Even without further model tuning, all the experimental links produced mean absolute percentage errors between 8% and 16% except for three atypical locations. Based on lessons learned, recommendations are provided for future applications and tune-up of the proposed STRE model.