Combination of Dependent Realizations Within the Gradual Deformation Method

Gradual deformation is a parameterization method that reduces considerably the unknown parameter space of stochastic models. This method can be used in an iterative optimization procedure for constraining stochastic simulations to data that are complex, nonanalytical functions of the simulated variables. This method is based on the fact that linear combinations of multi-Gaussian random functions remain multi-Gaussian random functions. During the past few years, we developed the gradual deformation method by combining independent realizations. This paper investigates another alternative: the combination of dependent realizations. One of our motivations for combining dependent realizations was to improve the numerical stability of the gradual deformation method. Because of limitations both in the size of simulation grids and in the precision of simulation algorithms, numerical realizations of a stochastic model are never perfectly independent. It was shown that the accumulation of very small dependence between realizations might result in significant structural drift from the initial stochastic model. From the combination of random functions whose covariance and cross-covariance are proportional to each other, we derived a new formulation of the gradual deformation method that can explicitly take into account the numerical dependence between realizations. This new formulation allows us to reduce the structural deterioration during the iterative optimization. The problem of combining dependent realizations also arises when deforming conditional realizations of a stochastic model. As opposed to the combination of independent realizations, combining conditional realizations avoids the additional conditioning step during the optimization process. However, this procedure is limited to global deformations with fixed structural parameters.     

MAPGIS明码文件的MATLAB编程实现及其意义

作者通过对ＭＡＰＧＩＳＪＥ明码格式文件的解读,应用ＭＡＴＬＡＢ语言实现了外部数据转化为ＭＡＰＧＩＳ明码格式的方法,并讨论了其实现意义。     

多晶冰蠕变机制的研究进展

与绝大多数材料遵循基于位错攀移的指数蠕变机制不同,多晶冰遵循基于位错滑移的指数蠕变机制,这种机制引起了冰川学家极大的兴趣.以前人的研究为基础,综述了冰中质子无序及质子点缺陷的形成过程,质子点缺陷对位错滑移影响的微观机理,以及多晶冰蠕变的微观机制.研究表明:冰晶体中的氢原子(质子)无序使得位错在滑移过程中形成质子点缺陷(D、L、H₃O⁺和OH^-缺陷),从而降低了位错的滑移速率.质子点缺陷的形成需要氢原子(质子)跃迁,其激活能大于水分子自扩散所需的激活能.同时,多晶冰的蠕变激活能与质子跃迁的激活能相当,而大于水分子自扩散所需的激活能,因此多晶冰的蠕变控制机制是位错滑移,而不是位错攀移.     

基于光谱和纹理的SVM矿化蚀变信息提取研究

针对传统矿化信息提取方法单一,利用光谱或纹理、信息量相对较少、需要大量样本的缺陷,利用基于光谱和纹理的支持向量机(SVM)原理,建立矿化信息提取模型.选择青海泽库县析界日地区作为典型研究区.首先提取研究区光谱和纹理信息,选取训练样本;然后求解最优超平面,进而确定决策函数;最后泛化推广识别其他待识别的样本.通过所提取的遥感蚀变异常信息与重砂异常点叠加分析,叠加基本吻合;从野外实地验证来看,均发现了不同程度的矿化现象,并指出了5个重点异常区.     

分数阶傅里叶变换联合支持向量机的建筑物变形预测

提出一种分数阶傅里叶变换(fractional Fourier transformation, FrFT)与支持向量机(support vector machine, SVM)相结合的建筑物变形组合预测模型。首先利用FrFT对变形时间序列进行多尺度分析,将复杂时间序列分解为一系列结构较为简单的子序列;然后利用SVM对每个子序列分别建立预测模型,通过将各个子序列的预测结果进行综合叠加,得到最终预测结果;同时考虑到SVM模型参数选择的难题,提出一种改进果蝇优化算法(improved fruit fly optimization algorithm, IFOA)对其进行全局寻优,提升预测性能。以西南地区某混凝土坝变形实测数据为例开展验证实验,结果表明,本文组合预测模型能够充分挖掘数据中隐含的趋势性和规律性信息,获得较高的预测精度。     

球面线要素的多分辨率实时简化方法

在分析球面3维数据可视化特点的基础上,提出了视点相关的球面矢量数据LOD简化方法,基本原理是:以视点为基础在屏幕空间获取投影误差,反投影到模型空间得到动态简化阈值,并结合Douglas-Peucker算法,对矢量数据进行多分辨率实时简化表达.最后,开发实验系统并对相关方法进行了验证.结果表明:在不影响球面图形可视化效果下,数据量减少了约4～5倍,有效地提高了图形渲染速度和效率.     

Non‐linear system identification of the versatile‐typed structures by a novel signal processing technique

Non‐linear structural identification problems have raised considerable research efforts since decades, in which the Bouc–Wen model is generally utilized to simulate non‐linear structural constitutive characteristic. Support vector regression (SVR), a promising data processing method, is studied for versatile‐typed structural identification. First, a model selection strategy is utilized to determine the unknown power parameter of the Bouc–Wen model. Meanwhile, optimum SVR parameters are selected automatically, instead of tuning manually. Consequently, the non‐linear structural equation is rewritten in linear form, and is solved by the SVR technique. A five‐floor versatile‐type structure is studied to show the effectiveness of the proposed method, in which both power parameter known and unknown cases are investigated. Copyright © 2007 John Wiley & Sons, Ltd.     

水下圆形浅滩附近波浪绕射的计算

采用波数矢量无旋和波能守恒方程对圆形浅滩附近水域波浪绕射进行了数值计算,计算模型中采用Ｂａｔｔｊｅｓ关系与波数矢量无旋,波能量守恒方程一起联合求解圆形浅滩附近水域波浪折射影响下的波浪要素。本文的数值计算模型对圆形浅滩水域波浪折射绕射现象的验证结果表明,计算所得结果与试验结果是吻合的,数学模型是可靠和合理的,具有实用价值。     

流速逆变张量隐式求解方法及其在航道港池流场计算中的应用

运用高分辨率的边界适应网格进行流体动力学数值计算时,如何提高计算稳定性和减少计算量成为数值求解的关键性问题.在非正交的边界适应坐标系中,每个动量方程中同时出现了两个交叉方向的水位偏导数项,给隐式求解带来困难,而显式格式下的时间步长由于受与空间步长有关的Courant-Friedrichs-Lewy条件限制,计算量成倍增加.本文从广义曲线坐标系下浅海动力学方程组出发,导出了流速的逆变张量所满足的动量方程组,使方程中的水位偏导数项变成了沿某一协变基向量方向占优的形式,方便地采用了交替方向隐式差分格式,从而提高了计算稳定性并减小了计算量.本文通过对澳门海域航道和港池中流场的计算,证实了该模式是一种进行高分辩率数值计算的有效方法.     

Study of tide prediction method influenced by nonperiodic factors based on support vector machines

Harmonic analysis, the traditional tidal forecasting method, cannot take into account the impact of noncyclical factors, and is also based on the BP neural network tidal prediction model which is easily limited by the amount of data. According to the movement of celestial bodies, and considering the insufficient tidal characteristics of historical data which are impacted by the nonperiodic weather, a tidal prediction method is designed based on support vector machine (SVM) to carry out the simulation experiment by using tidal data from Xiamen Tide Gauge, Luchaogang Tide Gauge and Weifang Tide Gauge individually. And the results show that the model satisfactorily carries out the tide prediction which is influenced by noncyclical factors. At the same time, it also proves that the proposed prediction method, which when compared with harmonic analysis method and the BP neural network method, has faster modeling speed, higher prediction precision and stronger generalization ability.