城市交通网络面向对象的时空数据模型研究

原有的城市交通网络数据模型无法对大比例地图中道路的面状特征进行描述。本文引入面向对象的时空数据模型 ,把各种实体作为对象 ,把道路作为面状要素描述。作为面状要素的道路对象直接继承原有道路的非空间特征 ,通过道路中心线对象和交点对象来继承原有线状要素的道路特征 ,包括网络关系和叠加关系等 ,通过车道段对象来增加作为面状要素的道路特征     

黄金企业多元化经营的几点思考

在市场经济中，任何企业，任何产品都有其生命周期，企业为了生存和发展，在不同发展阶段采取相应的经营战略至关重要。其中，多元化经营战略是谈论最多的一种，实践证明，多元化经营战略既是机遇，又是陷阱。因此，对企业采用多元化经营战略要有正确的认识，这种战略只是企业发展到一定阶段自身积累得以完善，为了可持续发展而采取的形式，目前，我国黄金企业成功采用多元化经营战略很少，黄金企业同其他企业一样，只要在主导产业做强，做大的同时，在项目选择上决策正确，顺应市场，多元化发展就会取得成功。     

基于非线性理论的边坡稳定性评价模型

边坡稳定问题涉及到各类水利、港工、铁路和工业民用建筑工程。由于影响边坡稳定性的因素较多，而且其变形破坏机理复杂，边坡稳定性问题迄今仍然受到理论研究和工程实践的关注。本文采用非线性理论和方法来研究边坡的变形破坏机理，并建立稳定性评价模型。以分叉和突变理论引出突变级数来表征边坡的状态，并用神经网络从中获取稳定性评价和判断的知识，进而构建系统，并对各类边坡稳定状态做出分析评价。实例分析证明了该方法的有效性。     

人工神经网络在桩基工程中的应用综述

对人工神经网络在桩基工程中的应用研究工作进行了回顾与评述。总结了神经网络在单桩承载力、荷载－位移关系预测以及基桩动测完整性判释等方面的技术成果与水平，并分析和探讨了进一步的研究方向和应用前景。     

Self-organised clustering for road extraction in classified imagery

The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and inefficiency to meaningfully exploit multispectral imagery (MSI). With a ground sample distance (GSD) of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge definition. A self-organising road map (SORM) algorithm is presented, inspired from a specialised variation of Kohonen's self-organising map (SOM) neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input.     

Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches

 Neural networks are attractive tools for the derivation of thematic maps from remotely sensed data. Most attention has focused on the multilayer perceptron (MLP) network but other network types are available and have different properties that may sometimes be more appropriate for some applications. Here a MLP, radial basis function (RBF) and probabilistic neural network (PNN) were used to classify remotely sensed data of an agricultural site. The accuracy of these classifications ranged from 86.25–91.25%. The accuracy of the PNN classification could be increased through the incorporation of prior probabilities of class membership but the accuracy of each classification could also be degraded by the presence of an untrained class. Post-classification analyses, however, could be used to identify potentially misclassified cases, including those belonging to an untrained class, to increase accuracy. The effect of the post-classification analysis on the accuracy of the classification derived from each of the three network types investigated differed and it is suggested that network type be selected carefully to meet the requirements of the application in-hand. Received: 23 March 2000 / Accepted: 9 July 2000     

The development of computer networks: First results from a microeconomic model

 Computer networks like the Internet are gaining importance in social and economic life. The accelerating pace of the adoption of network technologies for business purposes is a rather recent phenomenon. Many applications are still in the early, sometimes even experimental, phase. Nevertheless, it seems to be certain that networks will change the socioeconomic structures we know today. This is the background for our special interest in the development of networks, in the role of spatial factors influencing the formation of networks, and consequences of networks on spatial structures, and in the role of externalities. This paper discusses a simple economic model – based on a microeconomic calculus – that incorporates the main factors that generate the growth of computer networks. The paper provides analytic results about the generation of computer networks. The paper discusses (1) under what conditions economic factors will initiate the process of network formation, (2) the relationship between individual and social evaluation, and (3) the efficiency of a network that is generated based on economic mechanisms. Received: 5 July 2000 / Accepted: 28 November 2000     

Forecasting Monsoon Precipitation Using Artificial Neural Networks

This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a ＇new＇ data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.     

前馈神经网络隐性单调与泛化能力

应用前馈人工神经网络对广域单调的两组样本进行了模拟反演，引入单调前馈网络的概念对其权值和阈值定解问题和泛化能力进行了较诉研究。表明前馈人工神经网络是一个表达形式简单的复杂系统，其单调特征是隐性的，而且训练网络的成熟性对样本数量和样本内在规律性有一定依赖。强调了前馈人工神经网络的应用效果，指出单调与复合问题还需进一步深入研究。     

River flow mass exponents with fractal channel networks and rainfall

An important problem in hydrologic science is understanding how river flow is influenced by rainfall properties and drainage basin characteristics. In this paper we consider one approach, the use of mass exponents, in examining the relation of river flow to rainfall and the channel network, which provides the primary conduit for transport of water to the outlet in a large basin. Mass exponents, which characterize the power-law behavior of moments as a function of scale, are ideally suited for defining scaling behavior of processes that exhibit a high degree of variability or intermittency. The main result in this paper is an expression relating the mass exponent of flow resulting from an instantaneous burst of rainfall to the mass exponents of spatial rainfall and that of the network width function. Spatial rainfall is modeled as a random multiplicative cascade and the channel network as a recursive replacement tree; these fractal models reproduce certain types of self-similar behavior seen in actual rainfall and networks. It is shown that under these modeling assumptions the scaling behavior of flow mirrors that of rainfall if rainfall is highly variable in space, and on the other hand flow mirrors the structure of the network if rainfall is not so highly variable.