The Reinforcement Analysis of Prestressed Anchor Frame Beam in the Deep Cutting Slope under Three Dimensional Strong EarthquakeEI北大核心

Research purposes: In order to further study the reinforcement of prestressed anchor frame beam in the deep cutting slope under three dimensional strong earthquake, a model of the deep cutting slope is built through FLAC 3D simulation software, the seismic wave to the model from three directions of x,y and z is input, and the dynamic response of the slope is analyzed. Then, the prestressed anchor frame beam reinforcement measures are applied to the model, and this paper analyzes the reinforcement effect of the prestressed anchor frame beam to the deep cutting slope under three dimensional strong earthquake. Research conclusions:(1) The prestressed anchor frame beam has a good effect on restraining the horizontal displacement of the deep cutting slope. (2) Under the three dimensional strong earthquake, the prestressed anchor frame beam has a good effect on the horizontal acceleration and horizontal velocity of the deep cutting slope, which reduces the peak of horizontal acceleration and horizontal velocity. (3) Through the comparison of the response before and after the reinforcement of the prestressed anchor frame beam is given under the 9 degrees three dimensional strong earthquake, it can be concluded that the prestressed anchor frame beam can provide good reinforcement effect to the deep cutting slope. (4) This research can be used for reference to the earthquake resistance of the slope engineering. © 2018, Editorial Department of Journal of Railway Engineering Society. All right reserved.     

主变压器差动保护装置极性问题的探讨

阐述牵引变电所三相V/v接线牵引变压器差动保护极性的影响因素及其校验方法（微机保护的极性、主变压器的极性和电流互感器的极性3方面）,分析了包西线某牵引变电所主变压器设计和接线的缺陷,提出并验证了三相V/v接线牵引变压器差动保护接线方式的正确性。     

三塔悬索桥风-车-桥耦合动力分析

以温州市某大桥为例,分别考虑风荷载的平均成分和脉动成分对车桥系统的影响,建立了风荷载作用下三塔悬索桥的车桥耦合动力分析模型,并根据势能驻值原理及形成结构矩阵的"对号入座法则",导出了车桥系统的空间振动方程,采用计算机模拟的方法,计算与分析了该桥列车通过时的桥梁动力响应和列车走行性。研究结果为三塔悬索桥的动力设计提供了理论依据。     

车辆碰撞事故空间模拟再现系统开发研究

在完成了车辆碰撞事故分析计算方法研究的基础上,建立汽车三维四轮动力学计算模型,并引入OpenGL标准图形库与VisualC 编程环境相结合的三维真实感图形技术,开发了针对实际车辆碰撞交通事故的空间模拟再现系统。     

三维植被网路基边坡防护

三维植被网作为一种新型的坡面防护材料 ,具有很好的使用性能和推广价值。本文详细介绍其特点、优点以及施工工艺。并给出运用实例     

车载动态实时可视化交通信息服务系统

一种新型城市交通信息服务系统,它采用GPS接收机、数码像机、有线/无线智能传感器、辅以人工等方式完成路况信息采集,经控制中心处理后按1～3次/min频率生成全面、综合反映路况信息的实时、动态交通图,通过无线传输为公众提供实时、动态路况信息,并用全新概念的车载显示器Skipper显示路况信息,实现道路车辆均衡化、提高道路利用率。论述了其开发背景、系统构成及工作原理、核心产品开发与运用等。     

轿车三维分离流动特性的研究

本文介绍了使用激光片光技术及激光多普勒测速仪对某型国产轿车尾流的三维分离流的尾迹和速度分布测定的试验研究。本项研究表明，光学流态显示和数字图象技术相结合，可以有效地揭示汽车的尾流结构，诊断阻力来源，本项研究为建立汽车三维分离流动的动态仿真理论模型提供了依据。     

Simulation on progressive collapse behaviour of whole ship model under extreme waves using idealized structural unit method

To ensure the safety of navigating ship, working loads and structural load-carrying capacity are two important aspects. In the present paper, a total simulation system combing load calculation and structural collapse analysis is applied to simulate progressive collapse behaviour of a single-hull Kamsarmax type bulk carrier. A three dimensional singularity distribution method is adopted to calculate pressure distribution with time history. A mixed structural model, collapse part simulated by ISUM elements and remaining part by elastic FEM elements with relative coarse mesh, is proposed for collapse analysis. Progressive collapse behaviour obtained by ISUM is good agreement with that by nonlinear software package, MARC. However, the calculation time of ISUM analysis is about 1/70 of MARC analysis. The applicability to structure system, high accuracy and sufficient efficiency of ISUM had been demonstrated.     

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.     

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.