小净间距交叉隧道振动规律试验研究

随着国家路网建设规模的不断扩大,铁路、公路隧道出现近接平行、交叉的现象越来越普遍.受地形等因素的影响,交叉隧道的净间距也在不断减小,由此产生的工程问题逐渐变得复杂.文章以丹大铁路中的草莓沟2#隧道为例(该隧道下穿盘道岭公路隧道),开展6种不同加载工况下的振动台试验,输入地震波峰值分别为0.1 g、0.15 g、0.2 ...     

船舶碰撞危险度模型优化

介绍船舶碰撞危险度的研究概况,阐述空间碰撞危险度的概念及理论模型,根据船舶在开阔海域能见度良好情况下的操纵实际,对碰撞危险度模型进行优化,使之更适用于决策避碰.     

基于ARM的指静脉识别系统设计

指静脉识别是一种利用手指中静脉特征来进行身份安全验证的一种技术,与其它指纹识别技术相比,具有非接触采集、活体识别、内部特征安全、高精确性的优势,具有巨大的应用前景,目前已经成为关注的对象。本设计将手指静脉识别技术和嵌入式系统结合起来,实现了基于ARM的指静脉识别系统,契合了安全设备小型化的未来的趋势,识别准确率较高。     

盲源分离技术在车辆轮胎噪声提取中的应用

介绍Kullback-Leibler发散度,推导基于该发散度和自然梯度算法的盲信号处理的计算方法,并通过一个仿真实例说明运用该方法对车辆轮胎噪音提取的可行性,从而为车辆的车轮故障诊断和车体音频信号的盲分离提供一个新的方法。     

便携式制动性能测试仪的使用经验

在汽车检测中,经常会遇到各种问题,影响到检测的效果.高精度和耐冲击性的便携式制动性能测试仪的应用,提高了车辆制动性能的检测效果.     

飞机起飞滑跑距离数值积分改进算法

应用数值计算理论对飞机起飞滑跑距离数值积分算法进行了研究,用插值法对发动机瞬时推力和起飞气动数据的确定方法进行了改进,用迭代法确定了离地速度,基于改进算法编制了起飞滑跑距离计算程序。在发动机推力曲线已知时,对5种飞机的起飞滑跑距离进行计算,原算法的平均误差为55.6 m,改进算法的平均误差为23.4 m;在发动机推力曲线未知时,用程序计算某型飞机在12种条件下的起飞滑跑距离与实际滑跑距离对比的平均相对误差为2.9%。计算结果表明改进算法计算精度优于原算法。     

Energy dissipation in high-energy ship-offshore jacket platform collisions

Ship collisions with offshore structures may be characterized by large amounts of kinetic energy that can be dissipated as strain energy in either the ship, or the installation, or shared by both. In this paper a series of FE numerical simulations are performed with the aim of providing a clearer understanding on the strain energy dissipation phenomenon, particularly upon the ship-structure interaction. Ships of different dimensions and layouts are modelled for impact simulations. Likewise, three platform jacket models of different sizes and configurations are considered. The collision cases involve joints, legs, and braces and are simulated for several kinetic energy amounts of the vessels and different impact orientations. An overview of the plastic deformation mechanisms that can occur in both ship and jacket structure is also given. The results from the various models with different collision scenarios are compared in terms of the strain energy dissipation with respect to the different ship/installation strength ratios. From the FEA simplified approaches are also derived in terms of the relative stiffness of the two structures for assessing the responses and energy absorptions of the two structures. The conclusions drawn from this study can be applied to a broader range of collision assessment of offshore steel jacket platforms subjected to high-energy ship impacts.     

The impact of outcome framing and psychological distance of air pollution consequences on transportation mode choice

Recent years, air pollution phenomenon has become one of the crucial problems of Tehran, Iran. Due to main political and economic role of Tehran, population of this metropolis is high and increasing. Urban transportation of this highly populated city contributes more than 70% of air pollution problem in this city. Although a number of urban transport developments, policy measures and regulations have been employed, Tehran’s air pollution has remained crucial thus far. Finding ways to encourage individuals to behave more sustainable can be considered as a substantial approach of tackling environmental problems such as air pollution, since it can be highly cost-effective and fast. This research attempt to evaluate the impacts of two factors of outcome framing and psychological distance of air pollution on citizen’s willingness to behave environmental friendly, particularly to change the travel mode choice. Results illustrate that communicating the consequences of air pollution can provoke individuals’ to act more environment friendly or in particular to change their intention for using more sustainable mode of transportation. Framing the positive consequences of mitigating air pollution take precedence over framing the negative consequences. Moreover the gains of mitigating air pollution have an impact on the willingness to use of bicycle and bus. Results also show that decreasing the psychological distance of air pollution in order to make manipulated frame more personally relevant has no significant impact on respondents.     

Cycleways and footpaths: What separation is needed for equivalent air pollution dose between travel modes?

Cycling and walking are being promoted in many urban areas as alternatives to motorised transport for health, environmental, and financial reasons. The reduced congestion and resulting decrease in the overall amount of pollution reduced can be expected to result in health benefits for the community. However, active commuters, due to their increased respiration rates and often increased travel times can expect to receive larger doses of air pollution compared with those using motorised forms of transport. However, given the large dropoff in concentrations away from a road, it can be expected that significant reductions can be achieved even with relatively small increases in separation between the path of cyclists/pedestrians and motor vehicles.This study presents a simple methodology for calculating the separation needed for cyclists and pedestrians to experience the same air pollution dose as car commuters. An example is given based on carbon monoxide (CO) data collected in a field campaign consisting of a car driver, a cyclist and a pedestrian travelling on a 2600 metre loop of road in Auckland. For this case study, the estimated distance from the centreline needed for cyclists and pedestrians to receive an equivalent dose of CO as motorists was found to range from 5.8 to 14.2 m depending on the commuting mode and the dispersion state of the atmosphere at the site. This was equal to a CO concentration reduction of 0.1–0.14 ppm per metre. Recommendations on facility modifications and route selections have been made to make active mode commuting safer.     

A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction

With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.