Contact Binaries: A Study of the Proximity Effects and Gravity Darkening Based on Kopal’s Fourier Method

A new method for the determination of the proximity effects and gravity darkening exponents in contact binaries of W UMa type is presented. The method is based on Kopal’s method of Fourier analysis of the light changes of eclipsing variables in the Frequency Domain. The method was applied to 36 W UMa systems for which geometric and photometric elements have been derived by the most powerful techniques. The derived values are very close to those predicted by the existing theory of radiative transfer or convective equilibrium.     

UV Leo: The Binary with the Two Suns

BV light curves of the eclipsing binary UV Leo obtained at the Kryonerion Astronomical Station of the National Observatory of Athens, Greece, are analyzed. The analysis is based on a Roche configuration with two spots on the secondary surface. The elements of the two components of the system are calculated and the spot characteristics are given.     

On the Period Variation of the W UMa-type Contact Binary V502 Ophiuchi

The variation in the orbital period of the W UMa type contact binary V502 Oph is analyzed. The orbital period exhibits a wavelike variation with a periodicity of 23.0 years and an amplitude of △P = 1.24×10~(-6) days superimposed on secular decrease of dP/dt = 1.68×10-7 day per year. The long-term decrease may be accompanied by the contraction of the secondary at a rate of 83 m per year and a mass transfer rate from the primary to the secondary of 4.28×10~8 M per year. The short-term oscillation may be explained by the presence of a third component. Orbital elements of the third body and its possible mass are presented.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

高维遥感图像的快速分类算法

为了实现对高维遥感图像的快速准确分类,提出了一种基于k均值二叉树支持向量机(SVM)的分类方法。该方法通过对选取的训练样本进行k均值聚类,生成支持向量机分类二叉树,作为确定最佳分类顺序的依据,以降低分类过程中的误差累积并提高整体分类精度,而且可缓解由样本数量不均衡导致的分类误差。该方法可在不进行降维处理的情况下,对高维遥感图像进行快速准确分类。测试结果表明,其分类速度和分类精度都优于传统的支持向量机分类结果。     

基于离散元模拟的二元颗粒柱坍塌特性研究

常见的地质灾害如滑坡、泥石流、岩崩等通常都涉及不同形状的颗粒物质运动,这些形状不同的颗粒又多具有不同的尺寸和含量。基于典型的颗粒柱坍塌试验,首先根据试验方法确定了离散元模拟所需的各项参数,然后采用随机多面体方法生成了可控制长细比的大颗粒,利用离散元法就不同大颗粒含量下形态变化对二元颗粒柱坍塌特性的影响开展研究,研究结果表明:(1)利用离散元法可以较好地重现室内试验中小球和多面体组成的二元颗粒系统的颗粒柱坍塌过程;(2)在不同长细比的不规则大颗粒和小球组成的二元颗粒柱系统中,当大颗粒含量高于临界含量值20%时,二元颗粒柱坍塌持续的时间随非球形大颗粒长细比的增加而增加;(3)在不同长细比的不规则大颗粒和小球组成的二元颗粒柱中,当大颗粒含量高于临界含量值20%时,在相同百分比的大颗粒含量下,大颗粒长细比的增加会提高大颗粒平均配位数以及降低颗粒的运动能力,大颗粒间形成更强的互锁作用,降低了颗粒柱的整体流动性,使其最终堆积高度更高、最大跑出距离更短以及更小的归一化动能峰值。(4)在不同长细比的不规则大颗粒和小球组成的二元颗粒柱中,小颗粒可以较为明显降低大颗粒间摩擦及互锁作用,增加流动性,降低大骨料形态对坍塌过程的影响。     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models： An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

高光谱图像波段选择的改进二进制布谷鸟算法

波段选择是高光谱遥感图像分类的重要前提,本文提出了一种用于高光谱遥感图像波段选择的改进二进制布谷鸟算法,通过使用混合二进制编码算法更新子代鸟巢和使用遗传算法交叉方式更新被发现鸟巢两个方面对二进制布谷鸟算法进行改进,找出在图像中起主要作用且相关性低的波段,实现对高光谱遥感图像降维。将本文算法运用于PaviaU数据集和AVIRIS数据集,并与二进制布谷鸟算法、二进制粒子群算法、最小冗余最大相关算法、Relief算法等进行对比分析。结果表明,改进二进制布谷鸟算法波段特征选择效率更高,且选取的波段更具代表性,能够较好地提高后续分类精度。     

HS Herculis: Not apsidal motion,but a third body

Todoran's (1992) postulate of apsidal motion to explain the observed minimum times of HS Her is shown to be physically inconsistent. Instead, a third body in the system is postulated. Minimum timings in the next few years can settle the matter.