土地覆盖制图：基于最优化遥感数据的支撑向量机分类

遥感数据具有在不同空间、光谱和时间尺度上获取地表测量信息的能力,使其成为获取土地覆盖信息的一个主要数据源。影像分类即把卫星影像上的相关像元划分给某类已知的土地覆盖类型的过程。支撑向量机（SVMs）是一种土地覆盖分类的新技术。三种常用的SVMs是：基于线性和多项式的SVM以及具有高斯核函数的SVM分类器,分类能否成功地应用有赖于其各自选择的最佳参数。但是海量的遥感数据使得这些参数的确定速度十分缓慢。本文研究了一种新的基于最优化遥感数据压缩技术的SVM分类方法。研究显示用于获取SVM参数的数据量能够在不影响土地覆盖的分类精度的前提下进行压缩。数据压缩成功的应用于多项式和高斯核函数的SVM分类,而线性SVM的分类精度却非常低。     

利用多角度POLDER偏振资料实现陆地上空大气气溶胶光学厚度和地表反照率的同时反演 I. 理论与模拟

陆地上空标量辐射对地表反射率和大气气溶胶散射都具有很强的敏感性,而偏振反射只对大气气溶胶敏感,对地表不敏感。根据这个原理并结合POLDER(POLarization and Directionality of Earth Reflectance)资料的特点,作者提出综合利用标量辐射和偏振反射信息来实现陆地上空大气气溶胶和地表反照率的同时反演。首先,利用多角度偏振辐射观测提取大气气溶胶光学参数,再利用标量辐射测量对偏振反演结果作进一步筛选和订正,同时获得地表反射率。数值模拟试验结果证明,仅利用偏振信息只能获取大气气溶胶信息,而且其结果误差较大,特别是对于散射作用较强的短波长通道如670 nm误差更大,但经过标量辐射订正后的结果得到明显改善,气溶胶光学厚度和地表反射率与真实值之间相关系数都达到0.99以上。为提高查找表的计算效率,提出并建立了反演方案所需要的半参数化数值表,利用内插方法寻求气溶胶光学厚度和地表反射率的数值解的反演方法。     

Environmental risk factors for diseases transmitted by vectors: a case study in North-Argentina

The study quantifies the environmental risk factors for two diseases with different vectors and cycles of transmission: malaria and Chagas' disease in N.W. Argentina near the Bolivian border. This is the area within Argentina where malaria is still a serious health problem. Chagas' disease is to some extent present in many parts of the country. The field work for the study concerned 5903 people in 1466 dwellings. The study resulted in detailed maps of risk factors: particularly water quality and contacts with migrants from Bolivia in the case of malaria; and thatched roofs and dogs in the case of Chagas' disease. This revised version was published online in August 2006 with corrections to the Cover Date.     

试用矢量表示岩层真厚度

在野外地质测量工作中,岩层真厚度历来使用标量计算。文中证明：如果规定在导线前进方向上,凡是自下部向上部测得的岩层真厚度为正厚度,反之则为负厚度,可以使用公式Ｍ＝Ｌ·［ｓｉｎβ·ｃｏｓε·ｃｏｓ（－λ）＋ｓｉｎε·ｃｏｓβ］进行岩层真厚度计算。该公式各参数的取值范围与野外实测剖面过程中各数值可能出现的范围相同,而且所有数据均可直接取于野外实测剖面数据记录表,不需要对数据作任何处理,计算过程简单准确。其计算结果不但可得出岩层的真厚度,而且还可真实地反映出岩层之间的上下关系,从而使岩层厚度具有矢量性质。     

用SAVQ技术实现太阳射电频谱的数据压缩

对自适应矢量量化（SAVQ）技术在太阳射电频谱数据压缩中的应用进行了有意义的讨论．给出一种压缩比和失真可调的SAVQ压缩方法,指出提高数据压缩比的有效方法之一为样本集成,同时介绍了用该技术对北京天文台太阳射电频谱仪上的两个波段（1．0GHz－2．0GHz和2．6GHz－3．8GHz）的观测数据进行压缩的实用效果,并与国际同行所用的ICON方式进行性能比较．最后展示了数据压缩技术及其在天文上的应用．     

Least‐square support vector machine applied to settlement of shallow foundations on cohesionless soils

This paper examines the potential of least‐square support vector machine (LSVVM) in the prediction of settlement of shallow foundation on cohesionless soil. In LSSVM, Vapnik's ε‐insensitive loss function has been replaced by a cost function that corresponds to a form of ridge regression. The LSSVM involves equality instead of inequality constraints and works with a least‐squares cost function. The five input variables used for the LSSVM for the prediction of settlement are footing width (B), footing length (L), footing net applied pressure (P), average standard penetration test value (N) and footing embedment depth (d). Comparison between LSSVM and some of the traditional interpretation methods are also presented. LSSVM has been used to compute error bar. The results presented in this paper clearly highlight that the LSSVM is a robust tool for prediction of settlement of shallow foundation on cohesionless soil. Copyright © 2008 John Wiley & Sons, Ltd.     

一种基于正交投影的波束形成算法

提出了一种基于正交投影的波束形成算法.首先由MVDR算法确定初始权向量;其次根据该权向量与其它用户波达角方向的关系,建立干扰信号的导向矢量矩阵;然后通过正交投影原理,将期望信号的导向矢量投影到干扰信号的零空间上,从而求得最优权值.仿真结果证明了该算法的有效性.     

Data selection using support vector regression

Geophysical data sets are growing at an ever-increasing rate, requiring computationally efficient data selection(thinning)methods to preserve essential information. Satellites, such as Wind Sat, provide large data sets for assessing the accuracy and computational efficiency of data selection techniques. A new data thinning technique, based on support vector regression(SVR), is developed and tested. To manage large on-line satellite data streams, observations from Wind Sat are formed into subsets by Voronoi tessellation and then each is thinned by SVR(TSVR). Three experiments are performed. The first confirms the viability of TSVR for a relatively small sample, comparing it to several commonly used data thinning methods(random selection, averaging and Barnes filtering), producing a 10% thinning rate(90% data reduction), low mean absolute errors(MAE) and large correlations with the original data. A second experiment, using a larger dataset, shows TSVR retrievals with MAE < 1 m s-1and correlations 0.98. TSVR was an order of magnitude faster than the commonly used thinning methods. A third experiment applies a two-stage pipeline to TSVR, to accommodate online data. The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment, is an order of magnitude faster than the nonpipeline TSVR. Therefore, pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set. This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.     

An agent-integrated irregular automata model of urban land-use dynamics

Urban growth models are useful tools to understand the patterns and processes of urbanization. In recent years, the bottom-up approach of geo-computation, such as cellular automata and agent-based modeling, is commonly used to simulate urban land-use dynamics. This study has developed an integrated model of urban growth called agent-integrated irregular automata (AIIA) by using vector geographic information system environment (i.e. both the data model and operations). The model was tested for the city of San Marcos, Texas to simulate two scenarios of urban growth. Specifically, the study aimed to answer whether incorporating commercial, industrial and institutional agents in the model and using social theories (e.g. utility functions) improves the conventional urban growth modeling. By validating against empirical land-use data, the results suggest that a holistic framework such as AIIA performs better than the existing irregular-automata-based urban growth modeling.     

基于指标规范值的海水水质评价的SVR模型

为了建立具有普适通用的海水水质评价的支持向量机模型,在设置各指标参照值和指标规范变换式,并对指标进行规范变换的基础上,应用免疫进化优化算法,建立基于指标规范值的海水水质评价的回归支持向量机模型。将优化好的模型用于珠江口海水水质的评价,其评价结果与BP神经网络的评价结果基本一致,从而表明基于指标规范值的支持向量机模型用于海水水质评价是可行的,且该模型较传统的支持向量机评价模型具有较好的普适性和通用性。