基于Kriging法的海上短波通信可用频率插值方法研究

克里格法在统计意义上是一种对区域化变量的取值求线性最优、无偏内插估计的一种方法。分析了克里格法的插值原理及变异函数的确定方法,利用普通克里格法对海上短波通信可用频率实测数据进行插值,提出了一种新的短波通信可用频率插值方法。通过实测数据仿真验证了该方法的可行性和有效性。对海上短波通信可用频率的选用具有重要的应用价值。     

多测度加权克里金法在高边坡变形稳定性分析中的应用

针对传统的克里金法仅以欧式距离测度描述空间结构相关性的不足,基于最小方差准则提出了综合曼氏、欧式和切氏三种距离测度的多测度加权克里金方法,同时给出了一种简便的替代最小方差准则的改进定权准则。利用小湾水电站2号山梁高边坡监测点不同季节的变形速率数据进行实例计算,结果表明多测度加权克里金法提高了插值精度与可靠性,且改进定权准则的估计值接近理论最优值。应用所提出方法得到的2号山梁高边坡2004年春夏秋冬多时相变形速率场,有效地揭示了高边坡自下而上牵引式变形逐步得到控制并趋于平稳的演化特征,为边坡变形稳定性评估与防治提供依据。     

泛克立格法在非平稳区域化变量下渗透系数研究中的应用及存在的问题

考虑水文地质参数在时空上有较强的变异性,运用正交多项式方法确定渗透系数的二次漂移式,进而确定出渗透系数剩余R（x）的变差函数γR（h）,运用泛克立格法对渗透系数变异程度值进行了验证,结果表明非列线数据运用克立格法计算是符合工程要求的。     

改进Kriging插值的地面沉降监控模型的研究

随着城市化进程的加快,地下水超采、高层建筑的兴建、建筑容积率和建筑密度的增加以及特殊的地质环境所引起的城市地面沉降日趋严峻。因此,建立一种通用性更强、效率更高的沉降监控模型是十分必要的。Kriging插值以其独特的建模机制,在区域空间插值中已得到广泛应用,而在时域插值中却鲜有研究,借鉴Kriging空间插值模型,研究其在时间域插值的应用具有重要意义。为保证时域模型数据的时间相关性,减少扰动信息累积,需删除早期数据;而为了保证变异函数能真实反映数据的时间分布特征,模型又需要足够的样本数据。为此,探讨了一种基于灰色理论的等维动态Kriging时域插值模型,采用等维动态预测,保证数据时间相关性,减少信息扰动;采用对建模数据量要求较少的灰理论模型来拟合变异函数曲线,解决样本不足的问题,并结合工程实例,验证了该算法的实用性、可靠性和有效性。更多还原     

基于Kriging模型的圆锥动静压轴承优化设计

为提高圆锥动静压轴承的综合性能,以单位承载力下功耗最小和平均温升最低为优化目标,考虑几何结构约束条件,采用最优拉丁超立方进行设计空间的布点,进行有限元数值计算.基于计算结果,采用Kriging方法建立目标函数的近似代理模型.在此模型基础上,使用非劣分层遗传算法(NSGA-II)获得Pareto最优解集;最后通过权重系数...     

Kriging metamodel management in the design optimization of a CNG injection system

This paper deals with the use of Kriging metamodels in multi-objective engineering design optimization. The metamodel management issue to find the tradeoff between accuracy and efficiency is addressed. A comparative analysis of different strategies is conducted for a case study devoted to the design of a component of the injection system for Compressed Natural Gas (CNG) engines. The computational results are reported and analyzed for a performance assessment conducted with a data envelopment analysis approach.     

Mixed model-based inference in geoadditive hazard regression for interval-censored survival times

Mixed model-based estimation of additive or geoadditive regression models has become popular throughout recent years. It provides a unified and modular framework that facilitates joint estimation of nonparametric covariate effects and the corresponding smoothing parameters. Therefore, extensions of mixed model-based inference to a Cox-type regression model for the hazard rate are considered, allowing for a combination of general censoring schemes for the survival times and a flexible, geoadditive predictor. In particular, the proposed methodology allows for arbitrary combinations of right, left, and interval censoring as well as left truncation. The geoadditive predictor comprises time-varying effects, nonparametric effects of continuous covariates, spatial effects, and potentially a number of extensions such as cluster-specific frailties or interaction surfaces. In addition, all covariates are allowed to be piecewise constant time-varying. Nonlinear and time-varying effects as well as the baseline hazard rate are modeled by penalized splines. Spatial effects can be included based on either Markov random fields or stationary Gaussian random fields. Estimation is based on a reparametrization of the model as a variance component mixed model. The variance parameters, corresponding to inverse smoothing parameters, can then be determined using an approximate marginal likelihood approach. An analysis on childhood mortality in Nigeria serves as an application, where the interval censoring framework additionally allows to deal with the problem of heaped survival times. The effect of ignoring the impact of interval-censored observations is investigated in a simulation study.     

An Equivalence between SILF-SVR and Ordinary Kriging

Support vector regression (SVR) is a powerful learning technique in the framework of statistical learning theory, while Kriging is a well-entrenched prediction method traditionally used in the spatial statistics field. However, the two techniques share the same framework of reproducing kernel Hilbert space. In this paper, we first review the formulations of SILF-SVR where soft insensitive loss function is utilized and ordinary Kriging, and then prove the equivalence between the two techniques under the assumption that the kernel function is substituted by covariance function.     

Update strategies for kriging models used in variable fidelity optimization

Many optimization methods for simulation-based design rely on the sequential use of metamodels to reduce the associated computational burden. In particular, kriging models are frequently used in variable fidelity optimization. Nevertheless, such methods may become computationally inefficient when solving problems with large numbers of design variables and/or sampled data points due to the expensive process of optimizing the kriging model parameters in each iteration. One solution to this problem would be to replace the kriging models with traditional Taylor series response surface models. Kriging models, however, were shown to provide good approximations of computer simulations that incorporate larger amounts of data, resulting in better global accuracy. In this paper, a metamodel update management scheme (MUMS) is proposed to reduce the cost of using kriging models sequentially by updating the kriging model parameters only when they produce a poor approximation. The scheme uses the trust region ratio (TR-MUMS), which is a ratio that compares the approximation to the true model. Two demonstration problems are used to evaluate the proposed method: an internal combustion engine sizing problem and a control-augmented structural design problem. The results indicate that the TR-MUMS approach is very effective; on the demonstration problems, it reduced the number of likelihood evaluations by three orders of magnitude compared to using a global optimizer to find the kriging parameters in every iteration. It was also found that in trust region-based method, the kriging model parameters need not be updated using a global optimizer—local methods perform just as well in terms of providing a good approximation without affecting the overall convergence rate, which, in turn, results in a faster execution time.     

吉林公主岭土壤中砷、铬和锌含量的空间变异性及分布规律研究

在兼顾吉林省公主岭地区土壤类型与样点分布的相对均匀性的前提下 ,采集黑土、黑钙土和草甸土等土壤耕层 (0 -2 0cm )样品 70个 ,测定其全砷 (As)、铬 (Cr)和锌 (Zn)含量以及其它主要理化性质。采用经典统计学与半方差函数拟合相结合的方法 ,研究了土壤砷、铬和锌含量的空间变异性。根据半方差函数的拟合结果 ,在ArcViewGIS软件的支持下 ,编制Scripts进行克里格插值 ,以此来揭示该区域土壤砷、铬和锌含量的空间分布规律 ,并探讨这些重金属元素与主要土壤理化性质的关系