车桥耦合振动数值模拟及软件实现

在对高速铁路曲线梁车－桥耦合振动的研究中,建立了一种基于激励非线性振动的数值计算方法,并在Ｗｉｎｄｏｗｓ９５工作环境下利用ＰｏｗｅｒＳｔａｔｉｏｎ完成了计算程序的编制,取得了较好的计算结果。与其他模型相比较,保证精度的前提下,较大地节省了计算时间。     

国外某桥梁工程坐标系建立的过程分析

以国外某大型桥梁的建设为案例,通过地图投影的变形值计算,分析了使用UTM投影坐标系带来的精度误差,由此阐述了UTM投影和横轴墨卡托投影在工程中的适用性,重点说明了建立工程坐标系的步骤、方法以及精度验证过程,同时给出了工程坐标系与UTM坐标系的坐标转换方法,供国外类似的工程项目参考.     

2000国家大地坐标系转换的技术方法

研究长江航道现行测绘成果向2000国家大地坐标系转换的模型,提出基于采用二维多项式逼近模型的转换技术路线。经检验,模型平均精度优于0.15 m,可满足小于1∶2 000比例尺航道图转换精度要求,为长江航道图转换到2000系奠定了技术基础。     

反光膜逆反射性能综述

基于交通标志用反光膜材料的类别、结构、逆反射原理,及其在GB5768—1999和GB／T18833—2002中的等级对照,对反光膜的逆反射性能加以详细的论述．并对反光膜逆反射色的色度性能技术要求作一介绍,有利于该材料的推广与应用。     

多种武器综合使用新方式探究

随着潜艇技术、武器技术的发展以及新概念武器的出现,基于单平台的单武器使用已经越来越显示出其局限性.而突破了单平台探测能力、发控能力、单武器性能等因素的限制的多武器综合使用技术,预期将能达到较好的效果.本文在综合考虑各种武器及其发射平台性能的基础上,提出了8种武器综合使用的新方式,在对每一种方式可能的作战使用模式及其存在的优点进行了较深入论述的基础上,提出了与之相关并急需解决的关键技术.随着有关技术的进一步发展,不同武器间的综合使用方式,将在充分发挥各武器的性能的基础上得到广泛的应用.     

不规则波作用下斜坡堤越浪量试验研究

通过物理模型试验,研究不规则波作用下挡浪墙顶高度、宽平台长度及高度、波高和消浪墩等因素对越浪量的影响,发现挡浪墙、平台和消浪墩对减小越浪量有较好的作用。将现有计算越浪量公式值与实际物模试验值进行比较,推荐了越浪量计算方法。     

ANPC五电平逆变器SVPWM与SPWM的统一理论研究

目前多自由度多电平领域的空间矢量调制(SVPWM)和三角载波正弦脉宽调制(SPWM)的统一理论研究较少.本文在线电压坐标系下,对有源中点钳位型五电平逆变器(5L-ANPC)的SVPWM调制技术进行分析,研究开关序列L和复用矢量调节系数k对SVPWM等效调制波的影响.用SPWM调制波加零序电压的方式等效SVPWM,并推导...     

基于点特征的车载LiDAR与GNSS/IMU联合标定方法研究

激光雷达(LiDAR)、全球导航卫星系统(GNSS)与惯性测量单元(IMU)之间的外部参数标定精度是影响多传感器融合及高精度地图的主要因素。基于此，提出一种适用于无人车的LiDAR与GNSS/IMU标定方法，该方法可实时提取标定板点云中心坐标，并利用点特征进行外部参数标定。首先，分析了LiDAR、GNSS、IMU及通用横墨卡托格网系(UTM)坐标系的变换关系；其次，基于IMU安装平面与地面平行的假设，通过车辆前方地面的法向量计算LiDAR俯仰角和滚转角的初值，并利用标定板中心偏移量计算偏航角初值；然后，假设车辆在平面上保持直线运动，采用恒定姿态运动将旋转角度的求解问题转化为最优化问题，并根据标定板UTM坐标不变的约束求解出平移参数；最后，通过分析算法误差、传感器测量误差以及中心点匹配误差验证了方案的可行性，并通过无人矿车采集LiDAR、GNSS和IMU的同步数据，对所提出的外部参数标定方法进行测试。试验结果表明：提出的方法只需要一定范围的平坦区域和标定板就可以解决3自由度运动估计6自由度外部参数的退化问题，且标定后的外部参数可以保证点云地图在20 m内的拼接误差小于20 cm。     

Analysis and Potential Alternatives for the Disputed South China Sea from Ocean Governance in the Polar Regions

Sovereignty claims over insular features and maritime jurisdiction in the South China Sea have been disputed for decades, and a governance regime to address ocean-related issues is urgently needed. This article first introduces the notion of a regime, and examines details of cooperation mechanisms in the Polar Regions. Lessons that can be applied to the South China Sea include that both soft and hard law regimes work to bring States concerned together to cooperate on the “commons” issues even when military conflicts or sovereignty disputes still exist. Consensus among bordering States would be necessary to make the South China Sea a “zone of peace.” Mechanisms that accommodate the various sovereignty claims and freeze existing and new claims to, as well as to prohibit military activities in, the South China Sea are recommended. Lastly, if a cooperative mechanism were to be established in the future, the Arctic regime would be more applicable to the South China Sea than the Antarctic regime due to their geographic nature. Thus, only States bordering the South China Sea should have voting and decision-making rights in the cooperative mechanism. As always, the political will of all parties is paramount to the success of such an endeavor.     

Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10

The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM₁₀) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM₁₀ concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM₁₀ at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM₁₀ concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM₁₀, gaseous pollutants and meteorological variables respectively.