基于GIS平台的农业非点源污染研究

开发了一种运算法自动生成AGNPS模型所需要的大部分资料，并把模拟结果转换成GIS文件格式，利用遥感软件和开发的程序，从3个来源生成AGNPS模型需要的数据，包括来自于地形地貌图的数字化文件，土壤调查图集的数字化土壤分类图和地球资源卫星TM图像的植被覆盖分类图件；一旦生成了需要的GIS文件，格式转换软件能把GIS文件转换为AGNPS要求的表格文件格式；最后把AGNPS所有输出结果转换成GIS文件格式，以便用于进一步工作分析。     

面向对象的虚拟FMS建模及加工仿真

为解决柔性制造系统中资源重组引起的布局问题,采用面向对象的建模方法建立了柔性制造系统的虚拟仿真系统.该虚拟仿真系统通过对制造系统中各设备进行几何和行为建模,建立虚拟设备模型;采用生产计划流建立车间布局方案和调度模型.通过仿真,不仅能验证加工代码的正确性,还能根据加工工艺仿真结果分析布局的合理性和可行性,并对布局进行优化.最后,对2种布局进行了仿真,结果表明所建立的仿真系统是可行和有效的.     

大型船用斜齿轮参数化建模及其接触有限元分析

阐述了利用ANSYS参数化设计语言进行大型船用斜齿轮参数化建模及映射分网的方法和具体的实现过程.通过循环加载预定义的主从齿轮转角表数组的方法进行啮合周期内斜齿轮的接触非线性求解计算,表明在大型船用斜齿轮啮合过程中,最大接触应力和齿根最大弯曲应力同时存在突变,但前者没有后者突变明显.啮入时,接触应力和弯曲应力最大;啮合过程中,各数值的变化相对稳定.     

基于OPENGL技术与3DS MAX的3D虚拟校园情景开发

研究在普通的PC机下开发高质量的视景仿真系统。在Window XP系统下,以Visual C 为开发平台,利用OpenGL和3DS MAX,运用面向对象编程的思想,采用Win32Application的编程方法。首先介绍了OpenGL及如何利用它生成地面和天空。采用OpenGL函数进行摄像漫游和调入由3DS MAX绘制的3DS模型,较好地解决了用OpenGL绘制复杂图形的困难性。     

基于虚拟现实技术的高速公路安全设施

依托于在建西汉高速公路秦岭北麓路段施工图和现场勘查资料,在对研究路段运行车速预测的基础上,采用虚拟现实技术对道路、隧道和桥梁等进行安全性评价,从而发现西汉高速公路秦岭北麓路段设计过程中存在的行车安全性问题．然后设计出包括主动控速设施和配套标志的主动安全控制系统方案,采用虚拟现实技术对公路安全设施建立虚拟模型并应用于虚拟的高速公路模型中,通过虚拟试验优化高速公路安全设施设计方案．     

Using high-resolution event-based data for traffic modeling and control: An overview

Research on using high-resolution event-based data for traffic modeling and control is still at early stage. In this paper, we provide a comprehensive overview on what has been achieved and also think ahead on what can be achieved in the future. It is our opinion that using high-resolution event data, instead of conventional aggregate data, could bring significant improvements to current research and practices in traffic engineering. Event data records the times when a vehicle arrives at and departs from a vehicle detector. From that, individual vehicle’s on-detector-time and time gap between two consecutive vehicles can be derived. Such detailed information is of great importance for traffic modeling and control. As reviewed in this paper, current research has demonstrated that event data are extremely helpful in the fields of detector error diagnosis, vehicle classification, freeway travel time estimation, arterial performance measure, signal control optimization, traffic safety, traffic flow theory, and environmental studies. In addition, the cost of event data collection is low compared to other data collection techniques since event data can be directly collected from existing controller cabinet without any changes on the infrastructure, and can be continuously collected in 24/7 mode. This brings many research opportunities as suggested in the paper.     

Improving rail network velocity: A machine learning approach to predictive maintenance

Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad.Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.     

Including passengers’ response to crowding in the Dutch national train passenger assignment model

Transit passengers’ response to crowded conditions has been studied empirically, yet is limitedly included in transport models currently used in the design of policy and infrastructure investments. This has consequences for the practical applicability of these models in studies on, for instance, timetabling, train capacity management strategies, project appraisal, and passenger satisfaction. Here we propose four methods to include the effect of crowding, based on existing studies on passengers’ perception and response as well as often-used crowding indicators. These four alternative methods are implemented in the train passenger assignment procedure of the Dutch national transport model, and evaluated with respect to their impacts on the model results for the Dutch railway network. The four methods relate to four different ways in which an additive trip penalty and/or time-multiplier can be incorporated in the train utility function for different travel purposes, to capture the disutility of crowding as measured by the load factor. The analyses of the test case favor the hybrid method using both a boarding penalty (capturing seat availability upon boarding) and a time-multiplier (capturing physical comfort and safety throughout the trip). This method produces consistent results, while the additional computational effort that it imposes is acceptable. Further empirical underpinning is needed to conclusively show which of these methods best captures passengers’ response behavior quantitatively (for different travel purposes and conditions).     

船用螺旋桨三维建模方法研究

从分析船用螺旋桨三维模型主要几何特征入手,阐述其三维坐标计算方法。在探讨了螺旋桨某些特殊部位的处理方法后,运用三维模型设计软件,以某螺旋桨建模实例演绎其方法步骤。     

Simultaneous modeling of individuals’ duration and expenditure decisions in out-of-home leisure activities

In this paper, we propose an activity model under time and budget constraints to simultaneously predict the allocation of time and money to out-of-home leisure activities. The proposed framework considers the activity episode level, given that the activity is scheduled. Thus, the model considers the decision of the quantities for duration and expenditure spent during the activity. We use a flexible utility function and show how the simultaneous equations can be estimated by using structural equations model (SEM) estimation techniques to handle the endogeneity problem of time and expenditure. The estimation results are based on a large national leisure diary data set collected in 2008 in the Netherlands, which provides detailed information about time and money spent as well as timing and location attributes of the activities. The analysis reveals that socio-demographics, travel party, timing and location variables influence the duration and expenditure of activity episodes. It shows that various socio-demographic groups display different preferences in terms of the time and money spent on activities. The results also indicate substitution relationships between spending more time and money for various activity categories. Thus it is concluded that the analysis provides useful results for a better understanding of combined time and money allocation decisions for leisure activities.