Non-linear methods in remotely sensed multispectral data classification

The aim of this research is to develop an effective approach being able to deal with the stochastic nature of remote sensing data. In order to achieve this objective it is necessary to structure the methodological knowledge in the area of data mining and reveal the most suitable methods for the prediction and decision support based on large amounts of multispectral data. The idea is to establish a framework by decomposing the task into functionality objectives and to allow the end-user to experiment with a set of classification methods and select the best methods for specific applications. As a first step, we compare our results from Bayesian classification based on non-parametric probability density estimates of the data to the results obtained from other classification methods. Tree scenarios are considered, making use of a small benchmark dataset, a larger dataset from Corine land cover project for Bulgaria and analyzing different features and feature selection methods. We show that the theoretically optimal Bayesian classification can also achieve optimal classification in practice and provides a realistic interpretation of the world where land cover classes intergrade gradually.     

Feature selection for high-dimensional data in astronomy

With an exponentially increasing amount of astronomical data, the complexity and dimension of astronomical data are likewise growing rapidly. Extracting information from such data becomes a critical and challenging problem. For example, some algorithms can only be employed in the low-dimensional spaces, so feature selection and feature extraction become important topics. Here we describe the difference between feature selection and feature extraction methods, and introduce the taxonomy of feature selection methods as well as the characteristics of each method. We present a case study comparing the performance and computational cost of different feature selection methods. For the filter method, ReliefF and fisher filter are adopted; for the wrapper method, improved CHAID, linear discriminant analysis (LDA), Naive Bayes (NB) and C4.5 are taken as learners. Applied on the sample, the result indicates that from the viewpoints of computational cost the filter method is superior to the wrapper method. Moreover, different learning algorithms combined with appropriate feature selection methods may arrive at better performance.     

数据通信微小卫星星座系统的发展及应用

随着数据应用的发展,用户对数据传输的精确性、时效性、广域性提出了更高的要求.文章介绍了ORBC()MM、Argos、Aprize等几个国外典型数据微小卫星星座的发展情况,阐述了发展数据微小卫星系统的意义及必要性,从灾害监测、环境保护和商业应用等方面列举了系统的应用需求,分析了实时转发和储存转发两种业务发展模式,给出了系...     

经验公式与施肥效果分析的数学模型

本文利用数据拟合等方法讨论了一个数学建模问题:施肥效果分析,给出了两种模型,每种模型又都讨论了产量模型和效益模型。     

一种电子政务数据交换覆盖网络

数据交换是电子政务应用的核心问题.构建一种应用层数据交换覆盖网络成为解决这类问题的关键.分析了电子政务数据交换的特点,提出了电子政务数据交换覆盖网络eGON(e-Government data exchange Overlay Network)的概念模型,在树型结构组织网络的基础上,引入结点间组织距离和结点适应度概念,提出了一种eGON拓扑结构模型——双尺度异质组织网络模型DSHON(Dual Scale Heterogeneous Organization Network),该模型按照与组织距离和能力适应度2种尺度相关的概率添加快捷边,实现网络性能改善.设计了eGON的总体结构、构建与维护机制、路由维护协议和基于组织距离优先的消息路由算法,并进行了路由算法实验验证.      

ECK2007模型与其他密钥协商协议模型安全强弱关系的形式化证明

比较了安全性模型Extended Canetti＆Krawczyk 2007（ECK 2007）与其他4个安全模型（Bellare＆Rogaway 1993（BR93）,Bellare＆Rogaway 1995（BR95）,Bellare,Pointcheval＆Rogaway 2000（BPR 2000）和Canetti＆Krawczyk 2001（CK2001））的不同点,并形式化地分析了这些模型之间的强弱关系,对模型间存在的蕴含关系提供了形式化证明,对非蕴含关系提供了反例。     

基于BP神经网络算法的飞机发动机火警信号系统研究

针对某型飞机发动机火警探测系统存在误报率较高的问题,设计了基于BP神经网络算法的飞机发动机火警信号系统,采用了速率检测算法结合绝对温度的局部决策机制,构建了3层数据融合结构,进行了仿真验证.证明此种方法对解决发动机报警灵敏度和误报率之间的矛盾,具有较高的价值.     

分布嵌入式大气数据系统算法的初步研究

针对某型飞机给出了DFADS系统的构型描述,进行了DFADS的算法设计,算法采用了一种非物理映射的方法来建立各测压点压力和基本大气参数之间的关系.先是利用制定的多传感器数据表决规则表决出2～3对相关的测压点,然后通过建立相关测压点压力和其压力系数值之间的关系,利用这几对测压点的压力对事先建立的Cpi(α,β,Ma)映射关系表格数据库进行查表计算,从而得到当前的飞行状态.本文利用计算流体动力学(CFD)计算手段获得了332个飞行状态下飞机前机身14个测压点的表面压力系数数据,并以此为基础对DFADS的算法进行了仿真验证.结果表明,该DFADS的算法可以根据14个点的压力输入,正确地解算当前的大气参数.     

SCADA系统在石油储运中的应用

应用组态软件及PLC技术实现石油储运数据库的SCADA系统的构建,详细阐述了模拟石油储运中数据采集的硬件需求设计、软件构成,说明了石油储运数据库的SCADA系统的特点、功能、控制原理,完成了石油储运数据库的建立,为石油储运的研究提供了大量的实验数据,为石油储运新方法、新技术的推广提供依据。     

液体火箭发动机试验频率量信号的处理与仿真

液体火箭发动机试验中频率量（流量、转速）数据曲线不规整,存在着“毛刺”。影响了数据的准确性。采用仿真方法优化频率量信号采集模式,并用实际试验数据进行了验证。结果表明,仿真方法优化频率信号采集模式能有效地减少频率量曲线中的“毛刺”。