Moiré条纹密度对条纹图噪声及相位混频的影响

分析了Moiré条纹密度对条纹图噪声及相位混频的影响,提出了一种调整Moiré条纹密度的方法以有效减少条纹图噪声与包裹相位图噪声,并消除条纹过密产生的相位混频。试验测量表明,该方法能够获得可靠的测量结果,具有较好的测量精度。     

T型接头横向埋藏裂纹扩展特性

基于断裂力学理论和有限元分析,研究T型接头横向埋藏裂纹扩展特性,实现了双向拉伸载荷作用下T型接头横向埋藏裂纹扩展形状变化的数值模拟。结果表明:T型接头横向埋藏裂纹扩展至结构表面时的形状为近圆形,与裂纹埋藏位置和初始形状无关;埋藏于焊趾端部板材中的裂纹扩展快于埋藏于焊缝中的裂纹扩展。     

基于动视觉特性的高速公路景观敏感区划分

分析了驾驶人动视觉特性,研究了驾驶人的水平视野角度、前景视图、注意力集中点与最深视野随着车速的变化规律,构建了基于驾驶人动视觉特性的高速公路景观敏感区模型,并推导了景观敏感区函数。运用景观敏感区模型计算了双向四车道高速公路在不同限速条件下的景观敏感区尺度,运用景观敏感区函数获得分级结果,并阐述了不同景观敏感区的属性与景观要素设计要点。通过对比试验,研究了边坡宽度与一级景观敏感区尺度之间的关系。研究结果表明:双向四车道高速公路景观敏感区尺度为545m,一~三级敏感区尺度分别为55、260、230m,其中一级景观敏感区属于最敏感区域;进行边坡景观设计时可通过修正坡度的方法改变边坡宽度占高速公路景观敏感区的比重,坡度越缓,景观敏感性越高。     

浮摆式波浪发电平台系泊系统设计

针对一台自行设计的浮摆式波浪发电平台的运动特点与采能方式,对其系泊系统进行设计。系泊型式为"平台—连接缆—浮筒—锚链—锚"的悬链线单点浮筒系泊。以试验海域海况参数为依据,计算系统载荷,并采用准静力分析法对系泊系统各构件进行选型设计。设计的连接缆为钢丝绳,浮筒为圆柱型钢制浮筒,锚链为二级有档锚链,作业锚为AC-14锚。经检验,设计的系统符合安全要求和平台稳定性需求。     

基于模式识别的短时交通流预测

实时准确可靠的短时交通流预测是智能运输系统的基础,有很多种方法被用来对交通流进行预测.基于模式识别的交通流预测方法是较新的预测方法之一.提出一个用于短时交通流预测的模式和对应的模式识别算法,并对城区道路的交通流做了实验预测,结果表明在趋势上较为准确.     

Port authorities view state coastal management programs

Abstract

An estuarine sanctuary is a store of public values due to the ecological, cultural, recreational, aesthetic, historic, and economic services provided by the preserve. These values may be expected to increase over time because of (1) growth in the population demanding estuarine services, (2) growth in the willingness to pay for such services, and (3) growth in the actual quality and quantity of services which are provided by a protected natural system. Thus an estuarine sanctuary will be more valuable to future generations than to current generations. When weighing the cost and benefits of a preservation or development decision, a preservation decision which seems currently undesirable may in fact be socially beneficial when growth in the value of estuarine services is included.     

论高等级公路景观设计

高等级公路景观设计在思想上要注重视觉对空间位置和颜色的要求,要保持公路线形在视觉上的连续性.设计中应注意路线与地形相吻合、线形组合与景现相配合以及景观造型对行车和沿线风景的影响.应采取措施减少因占地拆迁、资源运输、噪音和废气污染产生的环境影响.     

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.     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.