基于Bootstrap的模糊度法方程更新算法的改进

阐述了GPS数据处理中基于Bootstrap的模糊度固定策略,在此基础上对二维法方程以及参数更新的算法进行改进,推导了更高效的三维法方程以及参数更新算法,能够提高解算速度,对GPS数据处理具有一定的参考价值。     

A BOOTSTRAP RESAMPLING SCHEME FOR USING THE CANONICAL CORRELATION TECHNIQUE IN RANK ESTIMATION

Rank estimation by canonical correlation analysis in multivariate statistics has been proposed as analternative approach for estimating the number of components in a multicomponent mixture.Amethodological turning point of this new approach is that it focuses on the difference in structure ratherthan in magnitude in characterizing the difference between the signal and the noise.This structuraldifference is quantified through the analysis of canonical correlation,which is a well-established datareduction technique in multivariate statistics.Unfortunately,there is a price to be paid for having thisstructural difference:at least two replicate data matrices are needed to carry out the analysis.In this paper we continue to explore the potential and to extend the scope of the canonical correlationtechnique.In particular,we propose a bootstrap resampling method which makes it possible to performthe canonical correlation analysis on a single data matrix.Since a robust estimator is introduced to makeinference about the rank,the procedure may be applied to a wide range of data without any restrictionon the noise distribution.Results from real as well as simulated mixture samples indicate that when usedin conjunction with this resampling method,canonical correlation analysis of a single data matrix isequally efficient as of replicate data matrices.     

有限数据条件下边坡可靠度分析的Bootstrap方法

基于Bootstrap抽样技术提出了有限数据条件下边坡可靠度分析方法。简要介绍了传统的边坡可靠度分析方法。采用Bootstrap方法模拟了抗剪强度参数概率分布函数的统计不确定性。以无限边坡为例研究了抗剪强度分布参数和分布类型不确定性对边坡可靠度的影响规律。结果表明：基于有限数据估计的样本均值、样本标准差和AIC值具有较大的变异性,这种变异性进一步导致了抗剪强度参数概率分布函数存在明显的统计不确定性。在考虑抗剪强度参数概率分布函数的统计不确定性时,边坡可靠度指标应为具有一定置信度水平的置信区间,而不是传统可靠度分析中的固定值。边坡可靠度指标的置信区间变化范围随安全系数的增加而增大,同时考虑分布参数和分布类型不确定性计算的可靠度指标具有更大的变异性和更宽的置信区间变化范围。Bootstrap方法为有限数据条件下抗剪强度参数概率分布函数统计不确定性的模拟以及边坡可靠度的评估提供了一条有效的途径。     

Bias assessment in Incremental Dynamic Analysis due to record scaling

Incremental Dynamic Analysis (IDA) involves a series of nonlinear response history analyses with a suite of incrementally scaled ground motion records. Although IDA is perhaps the most comprehensive seismic performance assessment method, it receives criticism because several ground motion records are scaled up until the structure collapses. The scaling practice often results to unrealistic multipliers, thus modifying the amplitude of the ground motion and introducing bias on the structural performance estimation. Record scaling is a common practice in earthquake engineering due to the lack of natural records corresponding to large magnitudes and/or small distances from the fault rupture location. In this work we use a large number of ground motion records to compare the predictions of IDA with that of unscaled ground motions and we propose a new methodology in order to quantify the bias introduced in IDA. Apart from natural records, we have conducted broadband ground motion simulations for rupture scenarios of weak, medium and large magnitude events in order to expand our record database. The investigation is performed on a series of inelastic single-degree-of-freedom systems and on two multistory steel moment frame buildings. The results pinpoint both qualitatively and quantitatively, for the full range of limit-states, the bias that IDA introduces on the structural performance estimation.     

地震活动中期预测指标研究及其空间图像演化

在研究应用模糊数学和非线性科学某些方法的基础上,通过实际预报检验,对一些中期预报较好的方法,如平静异常μｇ值,自相似从属函数μｓ值,自动统计方差σＢＭ值进行深入研究,提取中期预报定量化指标,探索孕震后期地震活动图像演化特征。结果表明：μｑ,μｓ,σＢＭ值能够较妇地反映地震前中期－短期异常变化特征,可以作为中期预报定量化指标;空间时序图像系列的显示,能定性反映震前异常区域及地震活动图像演化特征。     

利用多源数据对海冰密集度反演的算法验证

海冰密集度对全球气候变化研究有重要的意义,其反演结果的验证工作也被广泛关注,但结合多源数据反演,同时对两种算法验证的研究较少的现状,该文利用ASPeCt船测海冰密集度数据对Bootstrap算法和NASA Team(NT)算法基于SSM/I数据估算的海冰密集度精度进行验证,并与MODIS影像反演获得的海冰密集度进行对比。研究结果显示两种海冰密集度算法获得的反演结果与ASPeCt船测值偏差分别为2.26%和7.27%,均方根误差分别为11.39%和12.32%。相比之下,MODIS结果与ASPeCt船测海冰密集度比较得到偏差为3%,均方根误差为5.21%。Bootstrap算法、NT算法与ASPeCt船测值比较的偏差和均方根误差显示两种算法精度相近;由于MODIS数据分辨率与ASPeCt船测数据相近,所以其反演精度较优;但因时空分辨率的限制,各种结果都具有一定的不确定性。     

中国东部浅水湖泊参照状态确定的非参数方法

根据中国东部浅水湖泊受人类活动影响较严重的情况,将季节分解的非参数局部线性回归模型、频率分析和几何分块自助法有机结合,提出了一种基于非参数方法的湖泊参照状态确定的新方法。该方法首先将季节分解模型用于湖泊营养盐及其响应物的观测值,选取出适合用于推断参照状态的时间段;其次使用频率分析法分析此时间段内的观测值,并给出湖泊总氮、总磷和叶绿素a的参照状态值;最后用几何分块自助法给出各自的置信区间。该方法能有效克服前人提出方法的缺点。以太湖为例,采用该方法推断了参照状态浓度,总氮为0.78mg/L,总磷为0.030mg/L,叶绿素a为2.63μg/L;给出相应的95%置信区间分别为0.57~0.83mg/L、0.025~0.046mg/L和1.86~2.65μg/L。该方法也可适用受人类活动影响较大的中国东部其他浅水湖泊。     

Accounting for image uncertainty in SAR-based flood mapping

Operational flood mitigation and flood modeling activities benefit from a rapid and automated flood mapping procedure. A valuable information source for such a flood mapping procedure can be remote sensing synthetic aperture radar (SAR) data. In order to be reliable, an objective characterization of the uncertainty associated with the flood maps is required.This work focuses on speckle uncertainty associated with the SAR data and introduces the use of a non-parametric bootstrap method to take into account this uncertainty on the resulting flood maps. From several synthetic images, constructed through bootstrapping the original image, flood maps are delineated. The accuracy of these flood maps is also evaluated w.r.t. an independent validation data set, obtaining, in the two test cases analyzed in this paper, F-values (i.e. values of the Jaccard coefficient) comprised between 0.50 and 0.65. This method is further compared to an image segmentation method for speckle analysis, with which similar results are obtained. The uncertainty analysis of the ensemble of bootstrapped synthetic images was found to be representative of image speckle, with the advantage that no segmentation and speckle estimations are required.Furthermore, this work assesses to what extent the bootstrap ensemble size can be reduced while remaining representative of the original ensemble, as operational applications would clearly benefit from such reduced ensemble sizes.     

ESTIMATION IN COMPOSITIONAL DATA ANALYSIS

Compositional data arise naturally in several branches of science,including chemistry,geology,biology,medicine,ecology and manufacturing design.In chemistry,these constrained data seem to occur typicallywhen raw data are normalized or when output is obtained from a constrained estimation procedure,suchas might be used in a source apportionment problem.It is important not only for chemists to be awarethat the usual multivariate statistical techniques are not applicable to constrained data,but also to haveaccess to appropriate techniques as they become available.The currently available methodology is dueprincipally to Aitchison and is based on log-normal models.This paper suggests new parametric andnon-parametric approaches to significantly improve the existing methodology.In the parametric setting,some recent work of Rayens and Srinivasan is extended and a practical regression model is proposed.In the development of the non-parametric approach,minimum distance methods coupled withmultivariate bootstrap techniques are used to obtain point and region estimators.     

Improvement of overtopping risk evaluations using probabilistic concepts for existing dams

Hydrologic risk analysis for dam safety relies on a series of probabilistic analyses of rainfall-runoff and flow routing models, and their associated inputs. This is a complex problem in that the probability distributions of multiple independent and derived random variables need to be estimated in order to evaluate the probability of dam overtopping. Typically, parametric density estimation methods have been applied in this setting, and the exhaustive Monte Carlo simulation (MCS) of models is used to derive some of the distributions. Often, the distributions used to model some of the random variables are inappropriate relative to the expected behaviour of these variables, and as a result, simulations of the system can lead to unrealistic values of extreme rainfall or water surface levels and hence of the probability of dam overtopping. In this paper, three major innovations are introduced to address this situation. The first is the use of nonparametric probability density estimation methods for selected variables, the second is the use of Latin Hypercube sampling to improve the efficiency of MCS driven by the multiple random variables, and the third is the use of Bootstrap resampling to determine initial water surface level. An application to the Soyang Dam in South Korea illustrates how the traditional parametric approach can lead to potentially unrealistic estimates of dam safety, while the proposed approach provides rather reasonable estimates and an assessment of their sensitivity to key parameters.