青藏高原北缘公格尔山地区地形梯度的剖析

本文以青藏高原北缘西昆仑山脉的公格尔山地区为试验区域,基于SRTM-DEM数据,采用线状地形剖面和带状地形剖面的研究方法,对试验区从公格尔山顶到塔里木盆地的地形梯度进行了研究,并探讨了地形梯度与隆升过程之间的关系。研究结果表明:(1)线状地形剖面清晰直观,操作简单;带状剖面法能较好地反映区域的地形起伏状况。若将两种方法联合起来共同进行区域地形研究,则会获得更好效果。(2)从塔里木盆地到公格尔山顶,地形抬升共分3个梯度:分别是从海拔高度2 000m左右上升到约4 500m,从约4 500m上升到将近6 000m,从将近6000m上升到约7500m。地形抬升的不同梯度可能对应着公格尔山晚新生代地貌形成过程的不同隆升过程;而每个抬升梯度的拐点,则对应着不同隆升过程形成海拔高度的分界点。     

基于GF模型的BDS-3/GPS/Galileo三频模糊度固定性能分析

基于实测数据评估基于无几何（geometry-free,GF）模型的BDS-3/GPS/Galileo三频模糊度固定性能。首先给出适用于BDS-3/GPS/Galileo短基线TCAR（triple-frequency carrier ambiguity resolution）算法的三频线性组合;然后基于不同长度的实测短基线数据,评估BDS-3/GPS/Galileo超宽巷、宽巷及窄巷模糊度单历元固定性能。结果表明,对于5 m、3.6 km和13.1 km三条基线,BDS-3/GPS/Galileo超宽巷模糊度固定率相当（99.7%以上）,BDS-3/GPS宽巷模糊度固定率（97.2%以上）略优于Galileo（93.0%以上）,BDS-3窄巷模糊度固定率（92.2%以上）略优于GPS/Galileo（89.4%以上）。     

变差函数参数的计算

为了求出地质统计学中变差函数理论模型的参数，首先把相应模型化成线性函数的形式，多面手应用线性方程组非负解的理论计算出它的参数。     

基于改进的Elman神经网络的中长期径流预报

径流中长期预报长期以来一直都是人们关注的热点研究问题。现行的径流预报方法很多,传统的有时间序列法,多元回归分析法等,这些方法虽然简单易用,但是如果预报对象提供的样本容量偏小或者因子选择不够合理,都会造成预报精度偏差过大,难于有效的指导工程应用。鉴于此,本文提出一种改进的采用局部回归的Elman神经网络方法。并应用到凤滩水库优化调度的径流预报中。结果表明,与回归分析法、BP网络相比较,该方法不仅提高了算法的效率,而且提高了预报的精度,在径流预报中具有有效性和优越性。     

Robust H∞ control for aseismic structures with uncertainties in model parameters

This paper presents a robust H∞ output feedback control approach for structural systems with uncertainties in model parameters by using available acceleration measurements and proposes conditions for the existence of such a robust output feedback controller. The uncertainties of structural stiffness, damping and mass parameters are assumed to be norm-bounded. The proposed control approach is formulated within the framework of linear matrix inequalities, for which existing convex optimization techniques, such as the LMI toolbox in MATLAB, can be used effectively and conveniently. To illustrate the effectiveness of the proposed robust H∞ strategy, a six-story building was subjected both to the 1940 El Centro earthquake record and to a suddenly applied Kanai-Tajimi filtered white noise random excitation. The results show that the proposed robust H∞ controller provides satisfactory results with or without variation of the structural stiffness, damping and mass parameters.     

路堤沉降预测的Gompertz模型应用研究

高速公路路堤填筑过程可近似地看为一个线性加载的过程。笔者从土体应力-应变关系出发,证明了在线性加载或近似线性加载情况下,路堤沉降-时间曲线呈反“S”型特征,并由此建立了Gompertz沉降-时间预测模型,其中3个参数a、b和c可以通过SAS程序求解得到。多个工程实例的应用证明了该模型在路堤沉降预测中的合理性和实用性。     

基于双线性内插值的CD雷达垂直剖面算法

该文针对贵州CD型号新一代多普勒天气雷达反射率因子,采用C#编程,经坐标转换后处理成三维网格数据,基于双线性内插值法,实现任意方向雷达反射率因子垂直剖面制作,结合贵阳雷达分析,结果表明双线性内插值法放大后的图像质量较高,解决了因垂直剖面图像较小不利于显示及放大的问题。     

A formulation for diagnostic anomaly models

It is shown that the anomaly model governing the difference between two integrations of the equations of motion can be written in a form that is formally linear in the anomalies, by choosing the basic state to be the mean of the results of the two integrations.     

Asymptotic Rayleigh instantaneous unit hydrograph

The instantaneous unit hydrograph for a channel network under general linear routing and conditioned on the network magnitude,N, tends asymptotically, asN grows large, to a Rayleigh probability density function. This behavior is identical to that of the width function of the network, and is proven under the assumption that the network link configuration is topologically random and the link hydraulic and geometric properties are independent and identically distributed random variables. The asymptotic distribution depends only on a scale factor, , where is a mean link wave travel time.     

The equivalence of predictions from universal kriging and intrinsic random-function kriging

A proof is provided that the predictions obtained from kriging based on intrinsic random functions of orderk are identical to those obtained from anappropriate universal kriging model. This is a theoretical result based on known variability measures. It does not imply that people performing traditional universal kriging will get the same predictions as those using intrinsic random functions, because traditionally these methods differ in how variability is modeled. For intrinsic random functions, the same proof shows that predictions do not depend on the specific choice of the generalized covariance function. It is argued that the choice between these methods is really one of modeling and estimating the variability in the data.