SS4B电力机车转向架可视化标准作业系统研究

SS_4B型电力机车转向架结构复杂,检修工序繁琐,对作业人员的检修技能培训要求高,针对目前培训方式存在的问题,提出建立转向架可视化标准作业系统,综合利用多媒体技术、数字化建模技术以及虚拟现实技术,构建集三维动画和人机交互功能于一体,信息集中化、培训作业标准化、培训时空自由化、周期短且考核针对性强的可视化培训系统,实现对SS_4B型电力机车转向架系统的内部结构、检修流程及其动态拆装过程的逼真模拟,提升实操培训效率和沉浸式体验,提高检修人员技能水平。     

车务可视化信息系统设计与实现

车务可视化信息系统通过三维模型及虚拟漫游的展现形式,将铁路运行及运营的动态信息进行集成和柔性展示,实现旅客服务信息及运输管理相关数据的整合,通过可视化的展现方式既方便旅客及管理人员,又充分展示铁路信息化建设的成果。     

Multi-criteria optimization of traffic signals: Mobility,safety, and environment

Two-dimensional multi-objective optimizations have been used for decades for the problems in traffic engineering although only few times so far in the optimization of signal timings. While the other engineering and science disciplines have utilized visualization of 3-dimensional Pareto fronts in the optimization studies, we have not seen many of those concepts applied to traffic signal optimization problems. To bridge the gap in the existing knowledge this study presents a methodology where 3-dimensional Pareto Fronts of signal timings, which are expressed through mobility, (surrogate) safety, and environmental factors, are optimized by use of an evolutionary algorithm. The study uses a segment of 5 signalized intersections in West Valley City, Utah, to test signal timings which provide a balance between mobility, safety and environment. In addition, a set of previous developed signal timing scenarios, including some of the Connected Vehicle technologies such as GLOSA, were conducted to evaluate the quality of the 3-dimensional Pareto front solutions. The results show success of 3-dimensinal Pareto fronts moving towards optimality. The resulting signal timing plans do not show large differences between themselves but all improve on the signal timings from the field, significantly. The commonly used optimization of standard single-objective functions shows robust solutions. The new set of Connected Vehicle technologies also shows promising benefits, especially in the area of reducing inter-vehicular friction. The resulting timing plans from two optimization sets (constrained and unconstrained) show that environmental and safe signal timings coincide but somewhat contradict mobility. Further research is needed to apply similar concepts on a variety of networks and traffic conditions before generalizing findings.     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.     

A model for the visualization and analysis of elevator traffic

ABSTRACT

Analysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.     

基于OpenGL选择机制的模型变换组件的实现

为了便于用鼠标控制模型的平移、缩放和旋转变换,构建了三种模型变换组件,将3D场景中对模型的多种变换命令用几何图元进行可视化表示,并利用OpenGL选择机制,实现了各种模型变换命令的快速鼠标选择与切换,能够使用鼠标灵活地进行多种模型变换.这些组件已经应用到所开发的虚拟环境及仿真软件中,方便了用户操作,精简了用户界面.     

数据融合技术在铁路地质遥感判释中的应用研究

判释效果。为提高地质遥感判释的精度和质量,本文探讨数据融合技术在地质遥感判释中的应用。研究结论:(1)采用多源数据融合技术进行地质遥感判释,可提高不良地质判释的准确度;(2)二维和三维遥感判释相结合,可实现不良地质由定性到定量的判释和分析;(3)今后应加强遥感与物探等勘察手段的融合应用研究,拓宽地质遥感判释的应用范围。     

基于虚拟现实技术的列车网络实时控制可视化

针对TCN列车控制网络,通过自主研发的MVB/USB网关,在解决了MVB网络数据捕获及解析等难题的基础上,上位机采用＂生产者—消费者模型＂完成数据采集及处理,满足网络调试及人机交互对实时性的要求,最后综合应用网络通信技术、虚拟现实技术、三维图像技术、虚拟仪器技术,设计并实现了一套列车网络实时控制可视化的系统,以虚拟动画的形式实时响应来自列车网络的控制指令及状态信息。可用于试验教学、技术培训以及机车车辆的网络研发等。     

用于发动机开发的气缸内测试技术

描述了用于揭示气缸内过程的光学诊断技术.说明了可在气缸内安装光学通路的可视化发动机特点和激光诊断技术的原理.给出了典型的测试结果,如气体流动、燃油喷射、混合气形成、燃烧及燃烧产物.此外,探讨了光学诊断技术在发动机内部及外部废气再循环研究中的应用.     

车体强度分析结果可视化方法探讨

文章在车体结构强度分析结果数据处理中引入结构材料利用度云图和安全系数云图．实现结构安全性分析结果的可视化,使车体强度分析的结果更直观和全面。