基于客流导向的长大市域轨道交通组织优化方法研究

以北京市轨道交通平谷线为依托,基于线路功能定位和客流需求,对车辆选型、运营交路方案进行探索性创新研究。提出一种行车组织优化模型及求解算法,并以运力运量、工程投资、客流-车流匹配率等关键性指标为比选目标,确定该市域快线的最佳行车组织方案,以期为类似市域快线行车组织方案的合理选择提供理论支撑。     

三维物探船6缆扩充到8缆物探作业系统的升级改造

三维物探船拖带数字地震电缆能力是衡量物探船作业效率的重要依据。本船在设计初期,为可扩充为三维六缆的物探船。随着设计深入,对于船舶的空间及动力系统,稳性,船体结构强度等进行合理评估,尾部物探作业系统进行了进一步的优化,升级为国内第一艘三维八缆作业的物探船。通过对尾部物探作业系统的合理空间位置预留,为升级改造提供了可行的方案。经过此次升级改造,提高了本船的物探作业效率,物探作业性能指标达到国际先进水平。     

基于3DEXP的船舶MBD属性扩展技术研究

【目的】目前,基于3DEXPERIENCE(三维体验平台,3DEXP)的船舶MBD(Model Based Definition,基于模型定义)技术研究及应用还处于起步阶段。【方法】针对MBD文字类属性合理表达和管理的要求,基于3DEXP分析了船舶MBD属性扩展方式和优缺点,结合工程应用实践提出了MBD属性扩展建议,【结果】支持船舶MBD信息合理表达、获取和管理。【结论】通过实例表明,研究成果兼顾了3DEXP应用水平、MBD实施灵活性和高效性,为MBD技术实船应用奠定了技术基础。     

激光探测与测量视觉技术在堆取料机全自动控制中的应用

针对散货料场堆取料机人工操作堆取作业随意性大、精度差、效率低等现状,设计了一种堆取料机全自动控制系统。该系统基于激光雷达(Lidar)机器视觉技术,通过3D激光扫描仪实时获取料堆形状的点云数据,并完成对目标料堆的在线实时建模,最后控制堆取料机完成自动作业;提出了一种基于视觉图像处理算法的作业路径规划算法,通过实时提前预测作业轨迹来指导堆取料机自动作业。实践表明该控制系统提高了设备的作业效率,降低了作业成本。     

基于Intergraph Smart 3D的邮轮电缆自动敷设功能优化开发

Intergraph Smart 3D软件的电缆敷设功能在化工电力行业应用较为广泛,考虑到邮轮电气生产设计中的电缆具有长度长(3 000 km～5 000 km)、种类多、通道网络复杂和分段敷设的特点,直接应用Smart 3D原生的电缆敷设功能进行电缆敷设存在较大的风险。从邮轮电缆敷设需求出发,对Smart 3D原生电缆敷设解决方案进行优化,充分考虑电气设计人员的使用习惯,基于贪心算法和单源最短路径算法(Dijkstra算法)优化算法解决断续托架的电缆敷设和电缆分区敷设最优路径选取的问题,提高电气设计人员的电缆敷设效率。     

解读铁总企标《CTCS-3级列控系统总体技术规范》

介绍铁总企标《CTCS-3级列控系统总体技术规范》(Q/CR661-2018)的编制背景、主要技术内容,并重点解读有关新标准与原规范具有较大差异的主要技术变更项,最后提出未来改进方向。     

BIM技术在深圳机场三跑道填海工程设计中的应用

针对BIM技术在填海工程设计中的应用,深圳机场三跑道填海工程设计中采用AUTODESK平台,以基于云端的协同平台进行协同设计,将各专业更加紧密地联系在一起,解决了传统设计的沟通、协调难题,且根据勘察资料创建三维地质模型,并采用Civil 3D软件进行基槽和海堤施工图设计,提高设计效率及精度,为BIM技术在水运行业的推广及应用提供借鉴。     

基于FLAC3D的高填方路堤通道涵洞受力特性数值分析

文章以厦蓉高速公路广西境内灌阳至全州段K13＋710通道涵洞为工程实例,利用FLAC3D（快速拉格朗日分析）软件对高填方路堤中的顶板通道进行数值仿真分析,总结了高填方路堤通道涵洞的受力特性,并提出了通道涵洞设计施工中应注意的问题。     

Virtual 3D city model as a priori information source for vehicle localization system

This paper aims at demonstrating the usefulness of integrating virtual 3D models in vehicle localization systems. Usually, vehicle localization algorithms are based on multi-sensor data fusion. Global Navigation Satellite Systems GNSS, as Global Positioning System GPS, are used to provide measurements of the geographic location. Nevertheless, GNSS solutions suffer from signal attenuation and masking, multipath phenomena and lack of visibility, especially in urban areas. That leads to degradation or even a total loss of the positioning information and then unsatisfactory performances. Dead-reckoning and inertial sensors are then often added to back up GPS in case of inaccurate or unavailable measurements or if high frequency location estimation is required. However, the dead-reckoning localization may drift in the long term due to error accumulation. To back up GPS and compensate the drift of the dead reckoning sensors based localization, two approaches integrating a virtual 3D model are proposed in registered with respect to the scene perceived by an on-board sensor. From the real/virtual scenes matching, the transformation (rotation and translation) between the real sensor and the virtual sensor (whose position and orientation are known) can be computed. These two approaches lead to determine the pose of the real sensor embedded on the vehicle. In the first approach, the considered perception sensor is a camera and in the second approach, it is a laser scanner. The first approach is based on image matching between the virtual image extracted from the 3D city model and the real image acquired by the camera. The two major parts are: 1. Detection and matching of feature points in real and virtual images (three features points are compared: Harris corner detector, SIFT and SURF). 2. Pose computation using POSIT algorithm. The second approach is based on the on–board horizontal laser scanner that provides a set of distances between it and the environment. This set of distances is matched with depth information (virtual laser scan data), provided by the virtual 3D city model. The pose estimation provided by these two approaches can be integrated in data fusion formalism. In this paper the result of the first approach is integrated in IMM UKF data fusion formalism. Experimental results obtained using real data illustrate the feasibility and the performances of the proposed approaches.     

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.