机载激光雷达数据的建筑物三维模型重建

机载激光雷达技术可以快速获取建筑物高精度高密度的三维空间信息.文章提出一种建筑物三维模型重建方法,该方法利用栅格数据使用RANSAC算法提取建筑边界上的长线段,采用规则化策略获取建筑轮廓;在模型生成阶段,基于原始点云和屋顶拓扑关系约束,获取屋顶间的交线和交点,并结合已得到的建筑边界、屋顶间交线和交点获取三维建筑模型.实验结果表明,该方法思路清晰、科学可行且适用性强,提取的建筑边界和模型垂直精度高.     

Bayesian modelling the retreat of the Irish Sea Ice Stream

We present an 8000‐year history spanning 650 km of ice margin retreat for the largest marine‐terminating ice stream draining the former British–Irish Ice Sheet. Bayesian modelling of the geochronological data shows the ISIS expanded 34.0–25.3 ka, accelerating into the Celtic Sea to reach maximum limits 25.3–24.5 ka before a collapse with rapid marginal retreat to the northern Irish Sea Basin (ISB). This retreat was rapid and driven by climatic warming, sea‐level rise, mega‐tidal amplitudes and reactivation of meridional circulation in the North Atlantic. The retreat, though rapid, is uneven, with the stepped retreat pattern possibly a function of the passage of the ice stream between normal and adverse ice bed gradients and changing ice stream geometry. Initially, wide calving margins and adverse slopes encouraged rapid retreat (～550 m a^?1) that slowed (～100 m a^?1) at the topographic constriction and bathymetric high between southern Ireland and Wales before rates increased (～200 m a^?1) across adverse bed slopes and wider and deeper basin configuration in the northern ISB. These data point to the importance of the ice bed slope and lateral extent in predicting the longer‐term (>1000 a) patterns and rates of ice‐marginal retreat during phases of rapid collapse, which has implications for the modelling of projected rapid retreat of present‐day ice streams. Copyright © 2013 John Wiley & Sons, Ltd.     

基于贝叶斯网络的Web访问模式挖掘模型系统

根据用户的访问记录,以及每次检索输入的关键词、用户的动作来动态更新用户的兴趣点.提出了一个能较好吻合这一思想的数据挖掘模型,以用户的停留时间、访问频率和用户输入的检索关键词为数据对象,进行用户兴趣分析,动态跟踪用户需求意向,简化检索过程,提高WEB资源的使用效率.     

The Kepler problem: A unifying view

This paper is an attempt to bring unity in the study of the classical Kepler problem by combining, through simple vectorial and quaternionic techniques, its two peculiar aspects: the determination of the constants of the motion and the regularization at the origin.Research supported by the Consiglio Nazionale delle Ricerche of Italy (C.N.R.-G.N.F.M.).     

一种基于流形正则化的半监督指纹定位算法

针对传统指纹定位算法采集带标签训练数据成本高的问题,本文提出了一种基于流形正则化的半监督指纹定位算法。首先以流形假设为依据,利用批量输入的带标签数据与无标签数据之间的相似度构建图拉普拉斯算子;然后与极限学习机算法相结合,通过随机特征映射建立隐含层;最后在流形正则化框架下,求解隐含层和输出层之间的权值矩阵,从而建立位置估计模型。仿真结果表明,与INN、SVR、ELM 3种算法相比,该算法的训练和测试时间相对较短,且在带标签训练数据稀疏的前提下仍能保持较高的准确率与稳定性。     

Bayesian analysis of De distributions in optical dating: Towards a robust method for dealing with outliers

In optical dating, especially single-grain dating, various patterns of distributions in equivalent dose (D_e) are usually observed and analysed using different statistical models. None of these methods, however, is designed to deal with outliers that do not form part of the population of grains associated with the event of interest (the ‘target population’), despite outliers being commonly present in single-grain D_e distributions. In this paper, we present a Bayesian method for detecting D_e outliers and making allowance for them when estimating the D_e value of the target population. We test this so-called Bayesian outlier model (BOM) using data sets obtained for individual grains of quartz from sediments deposited in a variety of settings, and in simulations. We find that the BOM is suitable for single-grain D_e distributions containing outliers that, for a variety of reasons, do not form part of the target population. For example, D_e outliers may be associated with grains that have undesirable luminescence properties (e.g., thermal instability, high rates of anomalous fading) or with contaminant grains incorporated into a sample when collected in the field or prepared in the laboratory. Grains that have much larger or smaller D_e values than the target population, due to factors such as insufficient bleaching, beta-dose heterogeneity or post-depositional disturbance, may also be identified as outliers using the BOM, enabling these values to be weighted appropriately for final D_e and age determination.     

A three-level framework for multi-risk assessment

The effective management of the risks posed by natural and man-made hazards requires all relevant threats and their interactions to be considered. This paper proposes a three-level framework for multi-risk assessment that accounts for possible hazard and risk interactions. The first level is a flow chart that guides the user in deciding whether a multi-hazard and risk approach is required. The second level is a semi-quantitative approach to explore if a more detailed, quantitative assessment is needed. The third level is a detailed quantitative multi-risk analysis based on Bayesian networks. Examples that demonstrate the application of the method are presented.     

A Bayesian network approach for predicting seismic liquefaction based on interpretive structural modeling

The Bayesian network (BN) is a type of graphical network based on probabilistic inference that has been gradually applied to assessment of seismic liquefaction potential. However, how to construct a robust BN remains underexplored in this field. This paper aims to present an efficient hybrid approach combining domain knowledge and data to construct a BN that facilitates the integration of multiple factors and the quantification of uncertainties within a network model to assess seismic liquefaction. Initially, only using given domain knowledge, a naive network model can be constructed using interpretive structural modeling. Thereafter, some effective information about the naive model is provided to construct a robust model using structural learning of BN from historic data. Finally, the returning predictive results and the predictive results are compared to other methods including non-probabilistic and probabilistic models for seismic liquefaction using the metrics of the overall accuracy, the area under the curve of receiver operating characteristic, prediction, recall and F₁ score. The methodology proposed in this paper achieved better performance, and we discussed the power and value of the proposed approach at the end of this paper, which suggest that BN is a good alternative tool for seismic liquefaction prediction.     

基于机载LiDAR数据的建筑物轮廓规则化方法

在点云数据分类和建筑物轮廓提取的基础上,提出一种基于最小外包矩形的建筑物轮廓规则化方法。以任意两相邻轮廓点构建初始外包矩形,以迭代的方式将内点数最多者作为最终外包矩形,继而以面积比率、距离和均方根作为指标对规则化结果给出评判,最后以实际数据进行实验。实验结果表明,文中提出的方法能够有效对分类后的建筑物轮廓点进行规则化。