Modeling railway disruption lengths with Copula Bayesian Networks

Decreasing the uncertainty in the lengths of railway disruptions is a major help to disruption management. To assist the Dutch Operational Control Center Rail (OCCR) during disruptions, we propose the Copula Bayesian Network method to construct a disruption length prediction model. Computational efficiency and fast inference features make the method attractive for the OCCR’s real-time decision making environment. The method considers the factors influencing the length of a disruption and models the dependence between them to produce a prediction. As an illustration, a model for track circuit (TC) disruptions in the Dutch railway network is presented in this paper. Factors influencing the TC disruption length are considered and a disruption length model is constructed. We show that the resulting model’s prediction power is sound and discuss its real-life use and challenges to be tackled in practice.     

GIS-aided prediction for sea-ice distribution

A method of applying the geographic information system (GIS) to predict sea-ice distribution is proposed. The prediction computations are performed using the distributed mass/discrete floe (DMDF) model. The GIS is used to prepare the initial data for the DMDF model, and to display the simulation results together with a digital map and observational data for evaluation and further use. Two areas in the Arctic Ocean were selected to test the computations in this study. The results of the case studies show that this method can help with (1) free-area and free-scale prediction fitting for various purposes, (2) a high-resolution output to provide more detailed data, and (3) a reasonable combination of different data sets to make the best use of the information obtained. The results also suggest that the DMDF model is a good simulation of the ice motion not only for large-scale but also for local-scale predictions. Received: March 19, 2002 / Accepted: June 20, 2002 Acknowledgments. This study was financially supported by the Japan Science and Technology Corporation. The SSM/I sea-ice data were obtained from the EOSDIS NSIDC Distributed Active Archive Center (NSIDC DAAC), University of Colorado at Boulder. The authors thank the persons concerned. Address correspondence to: H. Yamaguchi (yama@fluidlab.naoe.t.u-tokyo.ac.jp)     

遇难船舶漂流轨迹预测技术

概观了国内外有关海上搜救以及遇难船舶漂流轨迹预测技术研究的现状。在综合考察相关研究成果的基础上,探讨了在既往的漂流轨迹预测系统里追加波浪所引起的漂流速度成分,以提高漂流轨迹预测的精度。     

隧道岩爆预测的距离判别分析模型研究及应用

将洞室最大切向应力和岩石单轴抗压强度的比值、岩石单轴抗压强度和岩石单轴抗拉强度的比值、弹性能量指数这3项影响隧道岩爆的主要指标作为判别因子,建立隧道岩爆预测的距离判别分析模型。以国内外重大隧道工程发生岩爆的实测资料作为学习样本进行训练,建立相应判别函数对待判样本进行预测。该模型在预测判别中综合考虑了影响隧道发生岩爆的多项因素,通过判别模型的学习功能获得岩爆与其各影响因素之间的复杂关系,排除了一般准则中建立判据时人为因素的影响。实例证明该模型利用回代估计法所得到的误判率为零,并具有较强的判别能力。     

船艇波浪中自航试验研究

对一艘船艇作了波浪中自航试验研究，得到了规则波中自航因子变化曲线，用直接功率法作了失速预报，并用谱分析法预报了该艇的纵摇、升沉及螺旋桨出水概率。本文的重要结论：船艇在波浪中的自航因子在简化处理时，可以认为相当于静水中的自航因子。     

船用燃机性能降级新型预测模型建立及应用分析

船用燃气轮机气路维护是舰船动力系统健康管理当中必要的环节,采用视情维护的方法是当前十分先进的维护手段,其中对气路降级趋势的预测是维护方法的核心。气路降级数据具有明确的增长趋势与不规律的波动性,符合灰色系统的特征。本文采用无偏灰色预测模型改进灰色马尔科夫模型,在此基础上扩大原始数据的维数,采用灰色关联度模型,将类似于当前序列的参考序列引入,最终构成了一种新的混合预测模型。最后,选择燃气轮机压气机结垢的过程作为实例来验证这一新的模型。通过研究模型参数和预测准确性之间的关系,为实例提出了建议的最佳参数。从预测准确性与对波动性的预测角度与不同的预测模型的比较结果表明,新的模型优于一些其他序列预测模型。     

非饱和粘性路基土回弹模量预估模型探讨

文章根据双应力状态变量理论及Bishop提出的非饱和土有效应力公式,对Uzan模型及NCHRP 1-28A推荐的三参数复合模型进行了改进.通过室内重复加载三轴试验及土-水特性曲线标定,对模型的准确性进行了验证.结果表明:模型中引入了控制路基土力学性状的应力状态变量,不仅符合路基土的实际工作状态,还同时考虑了应力状况和含水率对路基土回弹模量的影响,弥补了原有模型未考虑湿度或将其与应力状况分开考虑之不足.     

A Novel Method to Predict Traffic Features Based on Rolling Self-Structured Traffic Patterns

In this study, a novel method is proposed to predict the traffic features in a long freeway corridor with a number of time steps ahead. The proposed method, on the basis of rolling self-structured traffic patterns, utilizes the growing hierarchical self-organizing map model to partition the unlabeled traffic patterns into an appropriate number of clusters and then develops the genetic programming model for each cluster to predict its corresponding traffic features. For demonstration, the proposed method is tested against a 110-km freeway stretch, on which 48 time steps of 5-min traffic flows are predicted (i.e., a 4-h prediction). The prediction accuracy of the proposed method is compared with other models (ARIMA, SARIMA, and naive models) and the results support the superiority of the proposed method. Further analyses indicate that applications of the proposed method to larger scale freeway networks require sufficient lengths of observation to acquire enough traffic patterns for training and validation in order to achieve higher prediction accuracy.     

基于耐撞性的多学科近似模型预测精度研究

以整车100%正面碰撞有限元模型为基础研究了三种近似模型的预测精度,分析并选取前部结构中对汽车碰撞安全性影响较大的12个部件厚度为变量,利用最优拉丁超立方试验设计方法生成80个样本数据并进行计算,应用多学科优化中常用的二次多项式响应面(Quadratic Polynomial Response Surface,QPRS)、Kriging以及径向基函数(Radial Basis Function,RBF)三种近似方法分别对选取部件的总质量、吸收总能量、B柱最大加速度和踏板侵入量建立近似模型。结果表明:RBF近似方法对部件总质量、吸收总能量、B柱最大加速度预测精度高于其他两种方法,Kriging近似方法对踏板侵入量预测模型具有较好的精度,QPRS近似方法适合于部件总质量的近似建模。     

基于对线控位策略的UUV回收运动控制研究

研究了水下坞舱回收UUV (Unmanned Underwater Vehicle) 过程中的运动控制问题,详细介绍了坞舱搭载回收UUV的原理,建立了UUV回收中的数学模型。提出了一种基于对线控位策略的回收方法,通过一个双参考点的定位控制来实现UUV的回收。设计了UUV回收的位置和姿态的灰色预测PID控制器,以减小UUV回收控制中的超调量和调整时间。仿真验证结果表明了对线控位策略对于坞舱回收UUV是可行的,所设计的灰色预测PID控制器可以实现UUV回收的精确控制。