基于Markov的系统级动力定位控制系统可靠性分析

针对Markov模型在处理大型冗余系统可靠性问题时会遭遇系统状态量过大,计算耗费时间长,甚至无法有效计算等问题,提出将冗余DP控制系统进行有效分割,分别建立主、备控制系统的Markov模型,进而结合分系统的故障率建立系统级的Markov分析模型。该策略将子系统作为独立的可靠性分析单元,无需获取复杂DP控制系统的所有状态量,大幅减少了系统状态转移的分析量,从而可有效提高冗余控制系统可靠性分析的效率。     

马尔柯夫灰色残差模型在剐构桥控制中的应用

文章结合罗天乐特大桥的马尔柯夫灰色残差模型预测实例,介绍马尔柯夫灰色残差模型在线性控制中的原理、计算及应用过程,并根据实际监控经验提出了建议。罗天乐特大桥的具体监控应用实践表明,马尔柯夫灰色残差模型预测精度高,能较好地应用于大跨连续刚构桥的施工线形控制。     

The price of uncertainty in pavement infrastructure management planning: An integer programming approach

Currently there is a true dichotomy in the pavement maintenance and rehabilitation (M&R) literature. On the one hand, there are integer programming-based models that assume that parameters are deterministically known. On the other extreme, there are stochastic models, with the most popular class being based on the theory of Markov decision processes that are able to account for various sources of uncertainties observed in the real-world. In this paper, we present an integer programming-based alternative to account for these uncertainties. A critical feature of the proposed models is that they provide – a priori – probabilistic guarantees that the prescribed M&R decisions would result in pavement condition scores that are above their critical service levels, using minimal assumptions regarding the sources of uncertainty. By construction of the models, we can easily determine the additional budget requirements when additional sources of uncertainty are considered, starting from a fully deterministic model. We have coined this additional budget requirement the price of uncertainty to distinguish from previous related work where additional budget requirements were studied due to parameter uncertainties in stochastic models. A numerical case study presents valuable insights into the price of uncertainty and shows that it can be large.     

基于马尔可夫过程的快速路OD矩阵估计方法

为得到城市快速路网络的OD矩阵,引入马尔可夫理论.运用吸收马尔可夫过程对快速路网络进行建模分析,给出相应求解算法推导路段流量,小区OD矩阵表达式,并介绍了运用MATLAB软件进行矩阵求解的方法.最后进行了算例分析,利用VISSIM仿真,通过布设线圈检测器,对OD矩阵估计模型及算法进行精度评价.结果显示,模型可很好地拟合...     

A competing Markov model for cracking prediction on civil structures

Cracks on the surface of civil structures (e.g. pavement sections, concrete structures) progress in several formations and under different deterioration mechanisms. In monitoring practice, it is often that cracking type with its worst damage level is selected as a representative condition state, while other cracking types and their damage levels are neglected in records, remaining as hidden information. Therefore, the practice in monitoring has a potential to conceal with a bias selection process, which possibly result in not optimal intervention strategies. In overcoming these problems, our paper presents a non-homogeneous Markov hazard model, with competing hazard rates. Cracking condition states are classified in three types (longitudinal crack, horizontal crack, and alligator crack), with three respective damage levels. The dynamic selection of cracking condition states are undergone a competing process of cracking types and damage levels. We apply a numerical solution using Bayesian estimation and Markov Chain Monte Carlo method to solve the problem of high-order integration of complete likelihood function. An empirical study on a data-set of Japanese pavement system is presented to demonstrate the applicability and contribution of the model.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

Daily activity pattern recognition by using support vector machines with multiple classes

The focus of this paper is to learn the daily activity engagement patterns of travelers using Support Vector Machines (SVMs), a modeling approach that is widely used in Artificial intelligence and Machine Learning. It is postulated that an individual’s choice of activities depends not only on socio-demographic characteristics but also on previous activities of individual on the same day. In the paper, Markov Chain models are used to study the sequential choice of activities. The dependencies among activity type, activity sequence and socio-demographic data are captured by employing hidden Markov models. In order to learn model parameters, we use sequential multinomial logit models (MNL) and multiclass Support Vector Machines (K-SVM) with two different dependency structures. In the first dependency structure, it is assumed that type of activity at time ‘t’ depends on the last previous activity and socio-demographic data, whereas in the second structure we assume that activity selection at time ‘t’ depends on all of the individual’s previous activity types on the same day and socio-demographic characteristics. The models are applied to data drawn from a set of California households and a comparison of the accuracy of estimation of activity types and their sequence in the agenda, indicates the superiority of K-SVM models over MNL. Additionally, we show that accuracy in estimating activity patterns increases using different sets of explanatory variables or tuning parameters of the kernel function in K-SVM.     

基于灰色马尔可夫理论的油气管道腐蚀剩余寿命预测

以灰色理论的标准GM（1,1）模型和马尔可夫TPM理论为基础,提出了基于灰色马尔可夫理论的油气管道腐蚀剩余寿命预测方法。利用灰色马尔可夫理论预测腐蚀油气管道剩余寿命的步骤主要包括：最大允许腐蚀深度的确定,腐蚀速率的预测以及剩余寿命预测。并基于该方法,采用VB系统开发了实用软件,简便可靠。该方法可以在腐蚀速率波动比较大的情况下预测油气管道的剩余寿命,为油气管道腐蚀检测周期的确定提供了科学依据。     

路段平均行程时间估计方法

为了有效利用线圈检测数据,精确估计路段平均行程时间,提出了一种路段平均行程时间估计方法。将路段平均行程时间分为平均行驶时间、平均排队时间和平均通过路口时间三部分。考虑线圈埋设的特点,通过估计平均行驶速度得到平均行驶时间。用分段时齐Poisson过程描述车辆驶入路段过程和驶离过程,用Markov排队模型描述车辆排队过程,用生灭过程描述排队车辆数,得到车辆排队模型,计算了路段有、无初始排队的平均排队时间。基于选取与路口相关的饱和流率和平均车长,计算了平均通过路口时间。计算结果表明:平均行程时间估计值与实测值的误差小于12%,说明路段平均行程时间估计方法可行。     

Reliability Analysis of Excavator Rectifier Feedback System with Multi-State Components Based on Belief Universal Generating Function Method

In view of the complexity and uncertainty of system, both the state performances and state probabilities of multi-state components can be expressed by interval numbers. The belief function theory is used to characterize the uncertainty caused by various factors. A modified Markov model is proposed to obtain the state probabilities of components at any given moment and subsequently the mass function is used to represent the precise belief degree of state probabilities. Based on the primary studies of universal generating function(UGF)method, a belief UGF(BUGF) method is utilized to analyze the reliability and the uncertainty of excavator rectifier feedback system. This paper provides an available method to evaluate the reliability of multi-state systems(MSSs) with interval state performances and state probabilities, and also avoid the interval expansion problem.