刚度分配法在重庆朝天门长江大桥扣索力计算与研究中的应用

重庆朝天门长江大桥为世界第1跨度公轨2用拱桥,设计中主要以应力控制为主。阐述柔索索力传统计算方法,指出在计算扣索力时用传统先估算索力的计算方法误差很大。在实际有限元设计中,采用刚度分配法计算扣索力,不仅计算方便、快捷、准确,而且兼顾了目标控制应力和控制位移。     

纯铜渗铝渗剂的铝含量对α—Cu相结构的影响

研究了纯铜渗铝民支的铝浓度分布及对α－Ｃｕ相结构的影响。结果表明,渗层表面的铝浓度与渗铝剂中的铝成分有关,当铝成分≤４％时,渗层铝浓度分布与扩散定律计算值相符。渗层组织主要是α－Ｃｕ固溶体,α－Ｃｕ相的点阵常数与铝浓度呈线性关系。用内氧化技术在渗铝的试样上成功地制备了Ａｌ２Ｏ３表面弥散铜基复合材料。     

轿车用铝基复合材料制动盘的制备

对制备铝基复合材料的搅拌熔炼－液态模锻成形工艺进行了研究,得出了较合理的工艺参数,并利用该工艺铸造了用于桑塔纳轿车前制动器ＳｉＣ颗粒增强铝基复合材料制动盘毛坯,经机加工后试制了表面质量合格的制动盘成品。对该制动盘进行了材料性能检测试验和微观结构分析,并在ＳＣＨＥＮＣＫ制动试验台上进行了制动性能试验。结果表明,开发的铝基复合材料制备工艺参数合理,成本低廉,适于民用工业规模性生产;制备的ＳｉＣ颗粒增强     

碳纤维增强铝基复合材料的制备及其拉伸性能

利用挤压铸造方法,采用三种不同的凝固条件,制备了连续长碳纤维增强铝基复合材料的拉伸试样,并对其拉伸性能进行了试验．试验结果表明,该复合材料的抗拉强度和弹性模量皆随碳纤维的体积含量增加而增加．含48％碳纤维的复合材料比基体材料的拉伸强度高50％以上．但塑性指标随碳纤维含量的增加而显著降低．当连续碳纤维的体积含量较小时,凝固条件对该复合材料的抗拉强度影响较大,对弹性模量的影响较小;当碳纤维的体积含量较大时,凝固条件对该复合材料的抗拉强度影响较小,而对弹性模量的影响较大．     

拓扑型极大极小定理及其在抽象经济平衡问题上的应用

应用文 [1 ]中得到的参数型拓扑有限交定理 ,首先证明了区间空间上向量值极大极小定理 ,并得到几个推论 .作为应用 ,本文得到几个拓扑型截口定理及其等价形式 ,并研究了抽象经济Nash平衡和Shafer Son neinschein平衡存在问题 .     

考虑服务水平的纯电动快速公交发车间隔优化研究

随着电动汽车被引入城市快速公交领域,由于其充换电等特殊的运营调度需求,在发车间隔计划等制定过程中传统方法不再适用.本文在综合考虑服务水平和车辆充电时间约束条件下,对纯电动快速公交(E-BRT)发车间隔优化进行了研究.根据分析服务水平指标项与车辆能耗的相关性,建立了面向能耗评估的服务水平评价体系;综合考虑服务水平和充电时间约束条件,以车辆运营能耗为目标函数,建立了发车间隔优化模型;基于金华市E-BRT1号线运营数据进行实例验证,结果表明,在满足相同服务水平情况下,本文提出的优化模型可以节约能耗6.21%.本文构建的纯电动快速公交发车间隔优化模型具有较强实用性,为企业在满足一定服务水平的基础上降低运营能耗提供了依据.     

一种基于几何约束的轨道提取方法研究

针对基于Hough变换或螺旋曲线模型的视觉轨道检测方法存在的不足,本文提出了一种基于几何约束的轨道提取方法.该方法利用摄像机和轨道平面之间的成像关系近似满足单因矩阵的特点,利用逆透视映射(IPM)将输入图像转换为Bird-view图像,并采用一种改进的边缘检测方法进行边缘检测.然后将二值化的边缘图像在垂直方向上分割为多个区段,在每个区段上,利用先验知识生成的系列模板图像,对分段IPM图像进行去噪处理和Chamfer距离变换后进行距离匹配检测,将轨道检测转换为一个二维匹配搜索过程.在分段检测结果的基础上,进一步利用曲线拟合得到边缘图像中完整的轨道曲线方程.该曲线方程通过已知的单因矩阵转换为原始图像中的曲线描述,实现在原始图像中的检测和定位.实验验证了所提方法的可行性和可靠性.     

Statistical inference on transit route‐level origin–destination flows using automatic passenger counter data

We consider inferring transit route‐level origin–destination (OD) flows using large amounts of automatic passenger counter (APC) boarding and alighting data based on a statistical formulation. One critical problem is that we need to enumerate the OD flow matrices that are consistent with the APC data for each bus trip to evaluate the model likelihood function. The OD enumeration problem has not been addressed satisfactorily in the literature. Thus, we propose a novel sampler to avoid the need to enumerate OD flow matrices by generating them recursively from the first alighting stop to the last stop of the bus route of interest. A Markov chain Monte Carlo (MCMC) method that incorporates the proposed sampler is developed to simulate the posterior distributions of the OD flows. Numerical investigations on an operational bus route under a realistic OD structure demonstrate the superiority of the proposed MCMC method over an existing MCMC method and a state‐of‐the‐practice method. Copyright © 2015 John Wiley & Sons, Ltd.     

舰船主船体水线以下部分武器

采用符拉索夫计算法计算武器命中后的舰船浮态和外载荷的变化。以薄壁梁理论为基础,用迁移矩阵法计算武器命中后的舰船的内力,并以某舰船为计算模型进行了数值计算分析,找出正应力和剪应力随不同破口部位及尺寸的变化规律,从而为舰船破损后的剩余强度研究提供了依据。     

Bayesian committee of neural networks to predict travel times with confidence intervals

Short-term prediction of travel time is one of the central topics in current transportation research and practice. Among the more successful travel time prediction approaches are neural networks and combined prediction models (a ‘committee’). However, both approaches have disadvantages. Usually many candidate neural networks are trained and the best performing one is selected. However, it is difficult and arbitrary to select the optimal network. In committee approaches a principled and mathematically sound framework to combine travel time predictions is lacking. This paper overcomes the drawbacks of both approaches by combining neural networks in a committee using Bayesian inference theory. An ‘evidence’ factor can be calculated for each model, which can be used as a stopping criterion during training, and as a tool to select and combine different neural networks. Along with higher prediction accuracy, this approach allows for accurate estimation of confidence intervals for the predictions. When comparing the committee predictions to single neural network predictions on the A12 motorway in the Netherlands it is concluded that the approach indeed leads to improved travel time prediction accuracy.