三维GIS在铁路勘测设计中的应用

传统的铁路勘测设计成果多为二维地图,三维表示则独立地以其他CAD系统实现,勘测设计无法集成到同一平台.而三维GIS应用于铁路勘测设计则可以实现数据获取、处理与应用的一体化、表示的多维化与动态化.具体技术包括数据获取和处理、数据集成管理、数据可视化等.     

用~3H和地高辛配基标记TNF—α cDNA探针进行原位杂交的比较

用~3H和地高辛配基标记肿瘤坏死因子-αcDNA探针以及原位杂文技术检测33例子宫颈癌和28例阴道尖锐湿疣活检标本中的TNF mRNA。结果表明,两种标记探针原位杂交结果基本一致,~3H标记探针敏感性稍高于地高辛配基标记探针。但放射性核素探针的固有缺点及非同位素探针的安全、快速、方便使非同位素探针有着日益广泛的应用前景。     

多抗甲素对S_（180）荷瘤小鼠红细胞免疫功能的影响

用动物实验方法建立Ｓ１８０荷瘤小鼠模型，观察荷瘤小鼠红细胞免疫功能的改变及多抗甲素对Ｓ１８０小鼠红细胞免疫功能的影响。结果发现：①荷瘤小鼠ＲＢＣ－Ｃ３ｂ受体花环率下降，ＲＢＣ－ＩＣ花环率上升；血清中ＲＢＣ－Ｃ３ｂ受体免疫粘附促进因子活性下降，而抑制因子活性上升。②多抗甲素治疗后，荷瘤小鼠ＲＢＣ－Ｃ３ｂ受体花环率、ＲＢＣ－ＩＣ花环率均明显上升；血清中ＲＢＣ－Ｃ３ｂ受体免疫粘附促进因子活性上升，抑制因子活性下降。提示红细胞免疫功能的检测可作为肿瘤发生发展的指标之一，多抗甲素可恢复和提高荷瘤机体的红细胞免疫功能。     

真3维公路设计错误检测算法的研究与实现

由于真3维公路设计与传统2维公路设计的差异,如何在真3维的环境中进行设计错误的检测,至关重要。文章针对该问题,利用数学理论进行了2种检测方法的研究,从编程和精度角度分析其优缺点,利用线段长度比较的方法对平面线设计错误进行检测,达到了很好的应用效果,并能够在进行线位调整的同时,自动给出错误的检测结果,为真3维公路设计提供了参考。     

中央扣对大跨悬索桥动力特性和汽车车列激励响应的影响

为了探讨大跨悬索桥在动力激励下中央扣的作用,以四渡河悬索桥为研究背景,建立了该大跨钢桁架加劲梁悬索桥的3种中央扣模式的空间动力计算模型,对其动力特性和在移动汽车车列激励下的时程反应进行了空间非线性分析。研究结果表明:中央扣提高了结构的反对称抗扭刚度,限制了结构的纵飘特性;在静车列作用下,中央扣对全桥内力分布影响很小,但是在移动车列激励下,中央扣使得加劲梁的应力动响应显著增加,且随车列速度增加而增大;中央扣对加劲梁纵桥向位移的限制作用在常规速度车列激励下表现不明显;中央扣的设置方式宜采用刚性中央扣或3对柔性中央扣。     

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．     

Intelligent drug delivery system using UML diagrams analysis

A novel intelligent drug delivery system potential for the more effective therapy of the diabeticswas proposed, and the composition of system was analyzed. Based on the design of micro-electro-mechanicalsystems (MEMS), an iterative modeling process was introduced. Unified modeling language (UML) was em-ployed to describe the function requirement, and different diagrams were built up to explore the static model,the dynamic model and the employment model. The mapping analysis of different diagrams can simply verifythe consistency and completeness of the system model.     

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.     

基于CIVIL 3D的防波堤工程量计算分析

本文基于CIVIL 3D建立某海外港口防波堤模型,解决在原有防波堤基础上新建防波堤后的工程量计算问题。本文将介绍整个建模过程和思路,通过软件分析得到新建防波堤的精准工程量,并通过构建原有防波堤BIM模型对比实际已建防波堤的工程量验证模型的正确性,最后通过对比传统防波堤计算方法表明基于CIVIL 3D软件的BIM建模更加精准有效。