Effect of the star limb darkening law on the probabilities of discovering detached close binary stars as eclipsing variables

The probabilities of discovering detached close binary (type DM) stars as eclipsing variables are calculated as a function of the mass of the main component, mass ratio, major semiaxis, and angle of inclination of the orbit. The case of total limb darkening (hypothesis “D”) is examined. This is compared with earlier results for uniformly bright stellar disks (hypothesis “U”). Based on data from Svechnikov and Kuznetsova’s Catalog of Approximate Photometric and Absolute Elements of Eclipsing Variables, the spatial density of stars of this type in the neighborhood of the sun is estimated to be ≈ 460 · 10 ⁻⁶ pc⁻³. __________ Translated from Astrofizika, Vol. 49, No. 1, pp. 151–169 (February 2006).     

显性语法知识传授中的发现原则

尽管语法在交际教学法鼎盛时期一度遭到冷遇，但现在又重新被英语语言教学所接纳，作为一种新的语法教学方式，发现原则综合了传统归纳发和演绎法的长处，概述了发现原则的理论依据，指出发现原则旨在鼓励学习者积极参与而非被动接受，符合当前认知主义对二语学习的认识。     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.     

便携式电子地图设计中需解决的几个问题

软性显示器和投影技术超微型化的发展,使得电子地图有足够的空间满足便携式电子地图的设计需求,本文通过介绍几种便携式材质的特性,提出便携式电子地图设计中需解决的几个硬件技术问题。     

基于支持向量机的CBERS-02卫星影像信息提取

CBERS卫星是由中国空间技术研究院与巴西空间研究院联合研制的地球资源遥感卫星,CBERS-02卫星数据总体质量比CBERS-01卫星有所提高,本文利用支持向量机方法对CBERS-02卫星影像信息进行提取。研究中首先用6S模式对影像进行大气校正,然后选择RBF为支持向量机方法的核函数,并用交叉验证方法得到影响RBF核函数的两个最佳参数值进行学习完成信息提取,最后将提取结果制作成矢量图。通过研究得出用大气校正后的数据进行信息提取分类精度有所提高;与最大似然法和最小距离法相比,支持向量机方法分类精度较高。通过将研究结果与ETM+影像进行比较得出,CBERS-02卫星影像精度能够满足应用需求并能代替TM/ETM+数据开展研究工作。     

Automated Kerogen Classification in Microscope Images of Dispersed Kerogen Preparation

We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naïve Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p<0.05) and indistinguishable from the other four classifiers. The initial set of 32 features was subsequently reduced to 6 features without compromising the classification accuracy. A further evaluation of the system alerted us to the possible sensitivity of the classification to the ground truth that might vary from one human expert to another. The analysis also revealed that the logistic classifier made most of the correct classifications with a high certainty.     

Application of discovery process models in estimating petroleum resources at the play level in China

Discovery process modeling has gained wide acceptance in the Chinese exploration community. In recent years, a variety of discovery process models have been applied to the prediction of undiscovered petroleum resources at the play level in sedimentary basins in China. However, challenging problems have been encountered, particularly when one method alone has been applied to small plays in nonmarine sedimentary basins or in plays with an unusual order of discovery wells. This paper presents results gotten by using the lognormal discovery process model of the Geological Survey of Canada and the geoanchored method for three petroleum plays in basins with different geologic settings. Although the predicted shapes of the parentsize distributions which use these two models, were not always similar, the expected values of the total resources and the number of fields (pools) to be discovered are comparable. The combined use of two discovery process models in the same play compensates for the weaknesses in one method compared with the other and vice versa. Thus, more reliable estimates are the result.     

发现过程模型法对大港陆地探区油气资源的评价

对现有油气资源基本评价方法的原理、优缺点、适应性做了分析总结，重点剖析了在油藏总体分布服从帕莱托分布基础上建立起来的发现过程模型法．同时根据已建立的油气藏总体预测模型，对符合发现过程模型的大港陆地古近系成藏体系进行了油气资源评价。     

地理国情普查时点核准作业方法探讨

通过核实从数据采集到标准时点阶段发生变化的内容,补充新增内容,消除普查前阶段由于资料时效性和外业局限性等导致的现势性不统一现象,对变化的地理国情信息进行核准,使地理国情普查数据成果在时间上达到统一,是开展地理国情监测的基础。     

浙江省青田县石平川钼矿床新发现及找矿前景

长期以来,石平川钼矿床一直被认为是典型的石英脉型矿床,矿体分布于石平川岩体内外接触带上下100m以内。经过近年的地质工作发现,距石平川岩体外接触带400m~500m的侏罗系西山头组第二岩性段地层中存在细脉型辉钼矿体,从而形成下部大脉型石英辉钼矿体、上部细脉型辉钼矿体的二元结构。这一发现为开展深部及外围找矿,扩大资源储量规模提供了新的前景。