基于神经网络的单元自动机CA及真实和忧化的城市模拟

提出了一种基于神经网络的单元自动机（ＣＡ）。ＣＡ已被越来越多地应用在城市及其它地理现象的模拟中。ＣＡ模拟所碰到的最大问题是如何确定模型的结构和参数。模拟真实的城市涉及到使用许多空间变量和参数。当模型较复杂时，很难确定模型的参数值。本模型的结构较简单，模型的参数能通过对神经网络的训练来自动获取。分析表明，所提出的方法能获得更高的模拟精度，并能大大缩短寻找参数所需要的时间。通过筛选训练数据，本模型还可以进行优化的城市模拟，为城市规划提供参考依据。     

A study on the early-warning technique concerning debris flow disasters

According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN)real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system′s performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention.     

Sensitivity Analysis of a Feedforward Neural Network for Considering Genetic Mechanisms of Kuroko Deposits

Exploration for volcanogenic massive sulfide deposits of the kuroko-type is underway in many places. Clarifying the spatial patterns of the metals in kuroko deposits will be useful for understanding their genetic mechanisms and for future exploration of such types of deposits. This study represents a spatial distribution analysis on the contents of principal metals of kuroko deposits: Cu, Pb, and Zn, in the Hokuroku district, northern Japan, by a feedforward neural network and 1917 sample data at 143 drillhole sites. The network, which consists of three layers, was trained by the principle of SLANS in which the numbers of neurons in the middle layer and training data are changed to improve estimation accuracy. Using the weight coefficients connecting adjacent neurons, sensitivity analysis of the neural network was carried out to identify factors influencing spatial distributions of the three metals. The coordinates depth (z) direction, Bouguer gravity, and specific lithology such as dacite were determined to be influencing factors. The high frequency of the z coordinate signifies that the metal contents differ to a large extent by depth. The sensitivity vector was defined using sensitivity coefficients for x, y, and z coordinates of an estimation point. We determined that the directions of large vectors were different inside and outside of the Hanawa-Ohdate area. This characteristic is considered to originate from the differences in the permeability of fractures that became the paths for rising ore solutions, and the depths that the solutions mixed with sea water.     

湖泊水下数字高程模型建模方法及应用模型

介绍了当前湖泊水下地形测量的一般方法和作业过程。针对逐点插入构建不规则三角网算法，本文提出了效率改正算法和格网索引号快速计算算法。基于不规则三角网，给出了湖泊水下数字高程模型的一般应用模型——任意点高程计算、填挖方计算模型及等深线计算模型。     

平原河网区域来水组成原理

对于一般树状分布河网，上一级河道总是汇入至下一级河道，位于河道下游断面的流量总是由其上游汇集而至。但对于平原河网地区，特别是人工控制建筑物众多，又受潮汐影响的地区(如太湖流域)，河网错综复杂，水流方向不定，要跟踪某个断面的水流去向，或某个河段的水体、断面流量是从哪里汇集而来的，非常困难。但这样的问题在生产实践中往往对其很感兴趣。例如从常熟枢纽引长江水流进入望虞河后，流向何处?河网各断面流量中或各河段水体中常熟枢纽引江水量占多少比重?对这些问题的研究可以估计常熟枢纽引江的效果和影响范围。因此，平原河网区域来水组成方法在生产实践中具有重要意义。重点介绍了平原河网区域来水组成原理及其在太湖流域的应用。     

宽带接入用户认证技术分析与比较

给出了一个宽带接入用户认证系统的基本模型，并基于此模型对目前被广泛应用的PPPoE、WEB、DHCP等用户认证技术进行了分析与比较。最后，简单介绍了用户认证技术的新成员-802．1x(Port Based Network Access Control Protoco1)协议。     

巴彦浩特盆地火成岩的综合地球物理解释

采用综合地球物理方法识别巴彦浩特盆地火成岩,首先利用高精度重磁资料和大地电磁测深资料,初步确定火成岩的平面位置和深度,然后利用钻遇火成岩的已知测井资料,建立人工合成地震记录,在地震剖面上标定出火成岩并分析其波组特征,采用BP神经网络进行火成岩的精细识别.研究表明,综合地球物理解释是提高反演解释精度的有效途径.     

兰州数字遥测地震台网信号传输方式的设计

介绍了地震信号传输的常用方式及各自的优缺点，提出了数字遥测地震台网信号传输方式设计的总原则和基本原则。实际应用表明，兰州数字遥测地震台网信号传输方式的是符合要求的，提出的数字遥测地震台网信号传输方式的总原则和基本原则，对数字遥测地震台网建设有直接的借鉴作用。     

岩体三维不连续裂隙网络及其逆建模方法

通过拟合实测的二维裂隙编录图和钻孔裂隙编录图，优化裂隙三维大小和密度参数，使模型能够准确再现野外所观测到的实际现象，包括开挖面和钻孔上不同裂隙的数量、密度和长度，通过计算实例验证了方法的可行性。     

Seismic risk assessments and GIS technology: applications to infrastructures in the Friuli–Venezia Giulia region (NE Italy)

This paper illustrates the seismic risk preliminary estimates of two different groups of structures located on the territory of the Friuli–Venezia Giulia region (NE Italy) : the first group includes some special industrial plants, and the second group includes bridges and tunnels belonging to the regional highway network. The part of the study on special industrial plants tries to evaluate the degree of expected damage, taking into account their structural typology and ground shaking expressed in terms of macroseismic intensity. The second part of the study is an application of the HAZUS methodology to the tunnels and bridges of a highway network: the combination of expected ground shaking and the construction characteristics lead to very different risk levels, especially when considering the bridges. The resulting damage levels to bridges and tunnels are still only indicative because of the fragility curves used in the evaluations: they were developed for existing bridge and tunnel structural typologies in the U.S.A. Moreover, both examples show the power of GIS technology in storing, elaborating, and mapping spatial data. Copyright © 2003 John Wiley & Sons, Ltd.