基于ARM7的铜电解槽数据采集系统的设计

本文介绍了一种铜电解槽的温度和电压采集系统。此系统分为三层,下位机用LPC2119采集电解槽的温度和电压;下位机采集数据后,通过无线网络发送给基于LPC2119的网关;网关通过CAN总线将数据发送给上位机。本文还介绍了系统的总体设计方案以及关键技术的解决方法。系统具有较高的测量精度,良好的稳定性,有较高的应用价值和推广价值。     

在役轨道交通矮塔斜拉桥检测与评估

以某在役轨道交通矮塔斜拉桥的检测与评估为例,通过对矮塔斜拉桥受力特点的分析,确定了定期检测中矮塔斜拉桥的检测重点.主梁、主塔和墩柱的恒载几何形态测量表明,该矮塔斜拉桥整体保持稳定的状态,主梁跨中有沉降的趋势;通过桥梁自振特性、斜拉索索力和动位移的测试,明确了该桥的动力特性和动力响应;通过外观检查,明确了主梁主塔混凝土结...     

论城市道路横向空间使用权的分配与管理

做好道路横向空间使用权的合理分析与管理是提高城市道路交通运输系统效益的重要措施。该文结合天津城市道路横向空间使用权存在的问题进行了原因分析,并对新时期的道路横向空间使用权分配和管理提出了建议。     

现代城市多功能地铁换乘站综合体构筑方法

多功能地铁换乘站综合体构筑方法基于笔者发明的地下空间封闭式开挖理念（见《铁道建筑技术》2007—1期）。针对地铁换乘站结构体系的受力特点,在保持地基应力基本平衡及水土分离（地基土“疏干”）的条件下,运用盖挖逆筑法,自上（地面高程±0.00）而下一次性逐层将地铁换乘站主体结构筑成,是一种安全可行、高效环保、利于城市持续发展的好方法,也是具有广阔应用前景的地下空间开发新技术。     

响应表面法在事故再现结果不确定性分析中的应用

针对复杂事故再现模型的响应难以用显式函数表示的问题,借助试验设计方法和响应表面法,将试验设计方法中拟合的全部仿真结果,用一个近似的等效响应函数来表示,然后利用这个响应函数和各物理参数的上下极限值,直接计算出仿真结果的取值范围.这就构成了事故再现模型为隐性表达式时的不确定性分析近似方法.最后将此方法应用到一个具有简单模型的事故案例中,并与不确定度评定基本方法和蒙特卡罗方法进行对比,结果十分接近.     

北京市轨道交通自动售检票系统投资控制管理措施分析

本文对北京市轨道交通自动售检票系统投资控制影响因素进行了分析,并对其2008年至2012年的概算指标进行分析,最后对其应用的宏观控制措施和微观控制措施进行总结,可供其他城市轨道交通行业借鉴.     

上海自然博物馆基坑地下连续墙变形研究

新建上海自然博物馆基坑工程为深大异形坑中坑工程,周围环境复杂,施工风险大.利用三维有限元模拟方法,计算内外坑开挖过程中外坑地下连续墙的变形,选取模型中内外坑地下连续墙5种不同间距处的计算结果与现场监测数据对比,得出内外坑连续墙间距小于20m时外坑连续墙最大水平位移随内外坑间距增大而增大,但当内外坑地连墙间距大于20m时,内外坑地连墙间距变化对外坑连续墙最大水平位移影响不大;该工程坑中坑内坑开挖对外坑地连墙水平变形影响较小.     

基于模糊物元的台湾海峡客货滚装船船型经济论证

在两岸关系不断稳定发展的情况下,台湾海峡客货滚装运输具有良好的发展机遇和潜力。针对多准则、多目标的台湾海峡客货滚装船型经济论证问题,以模糊物元理论为基础,构建了基于模糊物元的船型论证方法和模型,并结合实际进行实证研究。实证算例分析结果表明:应用模糊物元理论进行台湾海峡滚装船型论证决策是可行的,此论证方法将定性与定量分析有机结合,方法非常实用,计算比较简便,论证结论客观、合理。     

Computational fluid dynamics (CFD) prediction of bank effects including verification and validation

Restricted waters impose significant effects on ship navigation. In particular, with the presence of a side bank in the vicinity of the hull, the flow is greatly complicated. Additional hydrodynamic forces and moments act on the hull, thus changing the ship's maneuverability. In this paper, computational fluid dynamics methods are utilized for investigating the bank effects on a tanker hull. The tanker moves straight ahead at a low speed in two canals, characterized by surface piercing and sloping banks. For varying water depth and ship-to-bank distance, the sinkage and trim, as well as the viscous hydrodynamic forces on the hull, are predicted by a steady state Reynolds averaged Navier–Stokes solver with the double model approximation to simulate the flat free surface. A potential flow method is also applied to evaluate the effect of waves and viscosity on the solutions. The focus is placed on verification and validation based on a grid convergence study and comparisons with experimental data. There is also an exploration of the modeling errors in the numerical method.     

Black-box modeling of ship manoeuvring motion based on feed-forward neural network with Chebyshev orthogonal basis function

Based on polynomial interpolation and approximation theory, a novel feed-forward neural network, the feed-forward neural network with Chebyshev orthogonal basis function, is proposed for black-box modeling of ship manoeuvring motion. The neural model adopts a three-layer structure, in which the hidden layer neurons are activated by a group of Chebyshev orthogonal polynomial activation functions and the other two layers’ neurons use identity mapping as activation functions. Weight update formulas are derived by employing the standard back-propagation (BP) training method. With the simulated 15º/15º zigzag test data as input and calculated values of the hydrodynamic forces and moment as output, the feed-forward neural network with Chebyshev orthogonal basis function and the BP neural network are applied to identify the nonlinear functions in the nonlinear hydrodynamic model of ship manoeuvring motion. With the simulated 20º/20º zigzag test data and 35º turning test data as input, the hydrodynamic forces and moment are predicted by using the identified nonlinear functions. Comparison between the calculated and predicted hydrodynamic forces and moment shows that the feed-forward neural network with Chebyshev orthogonal basis function is superior to the BP neural network in identifying the nonlinear functions of the nonlinear hydrodynamic model of ship manoeuvring motion and is an effective method to conduct the black-box modeling of ship manoeuvring motion.