Computational information geometry and its applications

Digitization is the main feature of modern Information Science. Conjoining the digits and the coordinates, the relation between Information Science and high-dimensional space is consanguineous, and the information issues are transformed to the geometry problems in some high-dimensional spaces. From this basic idea, we propose Computational Information Geometry (CIG) to make information analysis and processing. Two kinds of applications of CIG are given, which are blurred image restoration and pattern recognition. Experimental results are satisfying. And in this paper, how to combine with groups of simple operators in some 2D planes to implement the geometrical computations in high-dimensional space is also introduced. Lots of the algorithms have been realized using software.

Uncertainties in estimates of cropland area in China: a comparison between an AVHRR-derived dataset and a Landsat TM-derived dataset

The large uncertainties in estimates of cropland area in China may have significant implications for major cross-cutting themes of global environmental change-food production and trade, water resources, and the carbon and nitrogen cycles. Many earlier studies have indicated significant under-reporting of cropland area in China from official agricultural census statistics datasets. Space-borne remote sensing analyses provide an alternative and independent approach for estimating cropland area in China. In this study, we report estimates of cropland area from the National Land Cover Dataset (NLCD-96) at the 1:100,000 scale, which was generated by a multi-year National Land Cover Project in China through visual interpretation and digitization of Landsat TM images acquired mostly in 1995 and 1996. We compared the NLCD-96 dataset to another land cover dataset at I-km spatial resolution (the IGBP DIScover dataset version 2.0), which was generated from monthly Advanced Very High Resolution Radiometer (AVHRR)-derived Normalized Difference Vegetation Index (NDVI) from April, 1992 to March, 1993. The data comparison highlighted the limitation and uncertainty of cropland area estimates from the DIScover dataset. (C) 2003 Elsevier Science B.V. All rights reserved.

基于CAN总线的分布式数据采集与控制系统

随着电子技术和计算机技术的不断发展，工业生产过程的控制系统正在向着智能化、数字化和网络化的方向发展。传统的集散控制方式和计算机分层控制方式已经开始让位于智能终端与网络结合的总线网络控制方式。当今，在工厂中过程控制环境下的分布式自动化系统变得越来越复杂，尤其系统内部的各设备之间需要快速交换大量的信息，以便实现对被控系统更为精确的控制和提供一些辅助的评价函数。这就意味着要不断增加带宽和提高通信速率以满足网络通信的需要。在现有的多种可利用网络设备中，CAN总线以其清晰的定义、极高的可靠性及其独特的设计，被认为是最能有效地解决这一问题的途径之一。而且市场上基于通信技术的产品中，就实时性考虑，由于CAN总线采用的非表意性的通信方式，因此其结构更为简单，实时性更好。基于此背景，我们以CAN总线作为通信媒介，将分布于各控制现场的传感器、执行器和控制器有序地连接起来，构成了一个基于CAN总线的分布式局域网络控制系统。本文首先介绍了基于CAN总线的分布式数据采集与控制系统的总体结构。然后从硬件方面描述了基于CAN总线的通信协议转换单元、数据采集单元和输出控制单元的功能、硬件配置及各单元功能的具体实现过程，给出了各单元的性能指标。软件方面，以C语言作为平台，开发了基于CAN总线的上位计算机管理与监控软件，实现了对整个网络设备的系统管理和系统控制功能。对于该总线系统，作者运用了PID控制和模糊控制算法实现了对水箱液位的控制，达到了理想的效果。基于CAN总线的控制系统很好地解决了集散控制系统难以解决的难题，模糊控制的应用能很好地把总线控制系统应用到具有非线性、大时滞和难于获得精确模型的控制系统中。