REAL-TIME FILTERING OF DATA FROM MOBILE,PASSIVE REMOTE INFRARED SENSORS WITH PRINCIPAL COMPONENT MODELS OF BACKGROUND

Real-time monitoring of pollutant levels from a mobile measuring platform requires fast,flexible dataanalysis methods.This paper reports a method for rapid analysis of passive remotely sensed infrared datawith the aid of a Kalman filter.The background spectra produced by emission from the atmosphere aremodelled at the start of the data collection sequence with a simple principal components model obtainedby eigenanalysis of the initial‘blank’data taken with the spectrometer.The species of interest areincluded in the state space model by a separate measurement of their infrared spectra.It is demonstratedthat for best filter performance in detecting the simulated pollutant species SF_6 in the atmosphere,a filtermodel with two principal components describing the emission background works best.The filter‘maps’of SF_6 closely follow the integrated spectral intensities measured after removal of suitable backgrounds.     

Difference between model parameter estimation and curve fitting

Within a single well, a least-squares model such as Ø = a +bT _sonic may be appropriate, insofar as random errors in core measurement and in sonic tool response are concerned. However, if several wells are in the reservoir, and if structural variation across the field exists, no single least-squares solution will adequately describe all wells with the same precision. Interpolation, via a cubic spline, is offered as an alternative to model this type of variation. However, to control both inter- and intra-well variability, a combination of least squares and interpolation is the model of choice.     

RECENT DEVELOPMENTS IN MULTIVARIATE CALIBRATION

With the goal of understanding global chemical processes,environmental chemists have some of the mostcomplex sample analysis problems.Multivariate calibration is a tool that can be applied successfully inmany situations where traditional univariate analyses cannot.The purpose of this paper is to reviewmultivariate calibration,with an emphasis being placed on the developments in recent years.The inverseand classical models are discussed briefly,with the main emphasis on the biased calibration methods.Principal component regression(PCR)and partial least squares(PLS)are discussed,along with methodsfor quantitative and qualitative validation of the calibration models.Non-linear PCR,non-linear PLSand locally weighted regression are presented as calibration methods for non-linear data.Finally,calibration techniques using a matrix of data per sample(second-order calibration)are discussed briefly.     

地震趋势预测的一种新模型研究

 本文根据自激励门限自回归模型原理,对多年的地震活动形势进行了分析,建立了用于地震大形势分析的客观化数值预测模式-在线预测模型。根据1980年以来中国大陆实际发生的地震资料对这一模型的可靠性进行了检验。统计结果表明,利用在线预测模型在对未来地震趋势进行预测时,其年度形势特征量的最大误差为0.3,平均误差为0.12;因此该模型能够比较客观地反映地震形势变化的自然特征及规律,较好的预测未来的地震活动趋势。这一模型的建立为促进地震趋势向客观化、定量化预测方向发展,提供了一种科学途径     

“十五”福建省地壳形变数字化监测网简介

该文主要对福建省地形变监测数字化监测网的特色 ,监测网的内容 ,布网目的 ,主要技术指标以及数据处理中心建设作了简介 ,最后对地壳形变数字化监测网在“数字福建”工程中的作用作了阐述     

“蓝牙”技术与数字地震监测

“蓝牙”技术是一种无线数据与语音通信的开放性全球规范 ,它以低成本的近距离无线连接为基础 ,为固定与移动设备通信环境建立一个特别连接。它将给数字遥测地震台网的通讯技术带来一场新的革命。本文通过介绍“蓝牙”技术的基本原理 ,分析数字遥测地震台网的通讯要求 ,展望“蓝牙”技术在地震台网中的应用前景。     

淮河流域大型水库联合优化调度的动态规划模型解

淮河流域暴雨洪水频繁,常常遭遇恶劣雨型的组合。为使大型水库群在大范围暴雨洪水期间综合效益达到最优,采用动态规划模型求解淮河流域大型水库群的联合优化调度问题,利用国产曙光１０００巨型计算机的高速度和大容量的优势,解决了优化中的“维数灾”和“网格灾”问题,给出了淮河流域９个大型水库联合优化调度模型极效地提高了防洪水库的利用率。     

航空三线阵CCD影像测制城市数字正射影像

提出了利用三线阵ＣＣＤ相机测制城市数字正射影像和建立三维模型与目标,开展航空直接获取数字影像的摄影测量。着重讨论了利用三线阵ＣＣＤ影像计算自由外方位无素并生成模型ＤＥＭ然后利用少量控制点建立地面－模型坐标转换关系,生成地面坐标系的ＤＥＭ和正射影像。     

气候变化对中国农业生产的影响

根据１９５８￣１９９７年中国３１０个气象站的月平均最高、最低气温,月平均降水量,日照时数等气象参数分析了中国气候的背影特征。用３个大气环流模型,即ＨａｄＣＭ２、ＣＧＣＭ１、ＥＣＨＡＭ４来模拟未来中国气候变化的情景。同时,建立未来气候变化的６种方案。最后,根据３个ＧＣＭｓ模拟的结果,利用国际应用系统分析研究所（ＩＩＡＳＡ）发展并改进的农业生态地带模型（ＡＥＺ）评估气候变化对中国农业生产的影响。评估主     

西部开发与“数字地球”

我国实施西部开发战略,是采取以科技为先导,以信息产业为突破口,使之避免和减少对资源掠夺性开发,减少对环境污染,实现资源与经济的可持续发展。“数字地球”在这方面可以发挥巨大的作用。西部开发还必需放在全球变化、东亚大陆时空变迁规律的大环境中,从整体环境和地球大气圈、水圈、固体圈各因子相互作用和耦合过程的角度考虑。而“数字地球”正是具有独特的全球化宏观性,将地球作为一个有机整体来研究。因此,西部开发应充分运用当代“数字地球”这一先进技术群为西部社会、经济和环境的提高发挥更大的作用。使西部信息资源通过智力资本的投入,成为快速增值的知识资产.