Empirical analysis and comparisons about time-allocation patterns across segments based on mode-specific preferences

A three-stage approach, i.e., factor-cluster-multi-group Structural Equation Modeling (SEM), is designed to explore the commonalities and diversities with respect to relationships between socio-demographic characteristics and time-use patterns across different segments. Factor-cluster analysis is conducted to extract meaningful factors from attitudinal statements, and then group the sample population into three segments, each with a unique combination of mode preferences for public transit, private car, and motorcycle. By virtue of multi-group SEM, the relationships between socio-demographics and time allocated to activities and travel are found to be significantly different across segments. This study highlights the importance of latent psychological factors in segmentation. For policy implication, specific population with unique psychological features must be targeted in order to efficiently and effectively design and implement transport measures.     

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.     

基于能量法的气缸盖低周热机疲劳寿命预测方法研究

通过测试气缸盖本体解剖试样,获得气缸盖材料的循环应力应变特性,并利用仿真方法验证其合理性。在此基础上,依据发动机低周疲劳台架考核方法,运用子模型分析技术,得到考核循环内气缸盖火力面的应力分布和塑性变形特性。基于塑性应变能理论,结合试验测试,对火力面低周热机疲劳寿命进行预测和评估,分析表明排气鼻梁区的寿命较低,约为1100次。     

橡胶沥青开级配排水沥青混凝土（RA-OGFC）的试验研究

采用橡胶沥青（RA）作为胶结料,进行开级配排水沥青混凝土（OGFC）配合比设计;对橡胶沥青及RA-OGFC的性能进行评价,并考察抗车辙剂和纤维对RA-OGFC高低温性能的影响。结果表明,添加抗车辙剂和纤维可显著改善混合料的高温性能,弥补RA-OGFC混合料高温稳定性的不足。     

Mazda6轿车转向传感器信号的simulink识别

转向传感器信号是车辆控制系统的重要输入信号,但转向传感器的输出不是直接给出转向角大小而是方波信号。为了探究转向传感器原理以及Mazda6轿车实际转向过程中转向传感器信号,本文将介绍如何用simulink来识别该方波信号,输出转向角,最后用该方法识别出实际转向过程的转向角大小。     

基于模糊C均值聚类的模拟电路故障诊断

介绍了模糊C均值聚类分析的理论,并将该理论应用于模拟电路故障诊断.在模拟电路故障仿真分析中引入有容差的各类数据样本,运用迭代优化的模糊聚类算法生成故障模式,然后,将故障模式应用于未知故障情形进行诊断,仿真表明,该方法高效准确.     

基于模型试验的板桩墙支护变形机理分析

在准二维平面应变条件下,运用所研制的深基坑开挖室内试验模型,通过模拟板桩墙支护深基坑在无支撑悬臂式支护、单支撑和双支撑支护等3种支护形式下的开挖,测量出深基坑支护结构的位移、土体表面的沉降及土体位移场的变化,探讨了板桩墙的插入比λ、有无横向支撑及支撑的位置对基坑开挖变形的影响规律,为基坑变形机理的研究及预测提供依据.     

废胶粉改性沥青的离析机理研究

废胶粉改性沥青的存储稳定性能是影响橡胶粉改性沥青性质的重要因素,也是研究人员长期试图解决的问题.通过采用离析试验、荧光显微分析和傅立叶变换红外光谱(FTIR)等手段来评价废胶粉改性沥青的相容性,并且探讨离析的机理,研究结果表明:废胶粉改性沥青的离析程度会受储存时间、温度等贮存条件的影响,但是最主要的影响因素还是基质沥青的自然特性与改性剂的性质、剂量.     

基于自适应算法的单矢量传感器抗相干干扰

将矢量传感器组合指向性vs的零点对准已知的相干干扰方向,获得"干净"的目标信号,然后结合自适应算法抵消矢量传感器原始数据中各通道的相干干扰,达到抗相干干扰的目的.文中阐述了方法的原理,进行了多种情况下的仿真,并给出实验结果.仿真和实验结果表明该方法能有效抑制方位已知的相干干扰.     

Application of Lead Viscoelastic Dampers to Wind Vibration Control on Big-Span Power Transmission Tower

To study the wind vibration response of power transmission tower, the lead viscoelastic dampers (LVDs) were applied to a cup tower. With time history analysis method, the displacement, velocity, acceleration and force response of the tower was calculated and analyzed. The results show that the control effect of lead viscoelastic dampers is very good, and the damping ratio can reach 20% or more when they are applied to the tower head.