GIS空间数据采集中概率松弛与神经网络的组合模型

本文介绍一种ＧＩＳ空间数据采集中概率松弛与神经网络的组合模型，该模型用于地图分色和立体影像的整体匹配。     

人工神经网络在岩石变形温度估算中的应用

Ｃｈｒｉｓｔｉｅ（１９６２）、Ｓｍｉｔｃｈ（１９７２）和张翊钧（１９８５）都提出了钠质斜长石的Δ１３１－１３１（或σ）、Ａｎ与温度Ｔ的关系图，后来又提出了钠质斜长石变形温度的计算公式。但由于图解的不便之处及目前应用的公式中推断分界点的不确定性和σ、Ａｎ与Ｔ之间存在一种非线性关系的特点，本文基于具有高度非线性映射能力的人工神经网络，提出了求解岩石变形温度的新方法。文中先介绍了变形温度计的研究概况，然     

MATLAB工具箱在测绘数据处理中的应用

本文主要介绍了MATLAB工具箱的特点和功用。根据其在测绘领域的作用,对两个实例进行了分析,取得了良好的效果。相比其它编程工具,使用MATLAB工具箱大大简化了编程工作,同时获取了较高的数据处理精度,值得在测绘领域广泛应用。     

Hazard distribution and risk assessment in the network systems

In the age when transport flows in the railway and road systems, information flows in the internet channels, etc. intensify, hazard distribution in the network systems becomes a more relevant and important issue. In the paper various theoretical hazard type distributions in the network systems are analysed. Conditions and assumptions that hazard in the network system cells would settle and became steady are defined. Cases are analysed, when hazard is initiated in one or several network nodes, while in other nodes hazard is decreased, i.e. they have immunity to the hazard. In all cases hazard distribution equilibrium equations are constructed and their solutions‘ existence conditions are defined. Theoretical results are illustrated by simulation of hazard fuel transportation in the Lithuanian roads. It is shown that using this methodology it is possible to construct an algorithm, which enables to operate and decrease fuel transportation hazard and risk. The novelty of the work is associated with employing Markov process to describe hazard distribution mechanism and to determine limited hazard distribution in the nodes of networks. The hazard of tank trucks in the network of Lithuanian roads has been assessed for the first time.     

火山碎屑岩层孔隙度的计算方法

火山碎屑岩岩性复杂,因此骨架参数难以选择,影响孔隙度的计算效果。针对大庆油田海拉尔盆地的火山碎屑岩储层,对计算孔隙度的一般性方法进行研究;针对火山碎屑岩的成分,根据岩心分析资料计算其理论骨架参数值;采用3层BP神经网络进行训练学习,通过编程处理,实现人工神经网络计算火山碎屑岩储层孔隙度,其结果与岩心分析孔隙度比较,平均误差小于2%,能满足储量计算要求。     

Topographic relations: developing a heuristic device for conceptualising networked relations

This paper considers whether the deployment of a topographical metaphor may add value to the recent discussion of networked relations. The paper discusses how viewing relations as part of a relational landscape may add a third dimension to the discussion by allowing an appreciation of the strength, or entrenchment, of relations, and how these entrenchments impact on the development of new relations and the resistance to watershed events. The heuristic device is explored through the case of air survey in the 1920s and 1930s. This illustrative case demonstrates how relations are held together, resisted and reformed to different degrees depending on the varying topography of the relational landscape.     

A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis

The creep strain is proportional to the logarithm of the time under load, and is proportional to the stress and the temperature. At higher temperatures the creep rate falls off less rapidly with time, and the creep strain is proportional to a fractional power of time, with exponent increasing as the temperature increases and reaching a value ∼1/3 at temperatures, of about 0.5 T _m. At these temperatures the creep increases with stress according to a power greater than unity and possibly exponentially increases with temperature as (−U/kT), where U is an activation energy and k is Boltzman’s constant. There are different methods to determine the creep strain and the energy of Jog (B) such as by experimental methods and multivariate regression analysis etc. These methods are cumbersome and time consuming. In conjunction with statistics and conventional mathematical methods, a hybrid method can be developed that may prove a step forward in modeling geotechnical problems. In the present investigation, Artificial Neural Network (ANN) technique and Co-active neuro-fuzzy inference system (CANFIS) backed Genetic algorithm technique have been used for the prediction of creep strain and energy of Jog (B), and a comparative study has made between the two models.     

基于SDCORS的网络RTK在工程测量中的精度分析

针对基于SDCORS网络RTK测量精度进行研究,通过实验分别对两种GPS接收机安置方式的测量精度进行对比,并对网络RTK测量精度的时间变化进行分析,结果表明:1)网络RTK平面控制测量时应该使用三脚架安置GPS接收机。对于高程测量,利用三脚架与对中杆安置GPS接收机对大地高程测量精度影响较小;2)网络RTK测量偶有大误差测量结果出现,应取多次观测结果进行成果检核;3)网络RTK测量相近历元的观测误差具有相关性,如果有条件,最好在不同时间段多次测量取平均值。     

An ANN application for water quality forecasting

Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-a. A time lag up to 2Deltat appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R(2)), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models.     

Neural network prediction of a storm surge

The occurrence of storm surge does not only destroy the resident's lives, but also cause the severe flooding in coastal areas. Therefore, accurate prediction of storm surge is an important task during the coming typhoon. Conventional numerical methods and experienced methods for storm surge prediction have been developed in the past, but it is still a complex ocean engineering problem which many factors, including the central pressure of typhoon, the speed of the typhoon, the heavy rainfall, coastal topography and local features influence the variation of storm surge. In fact, this problem is still a complex nonlinear relationship that can not solved efficiently by these two methods. Therefore, this paper presents an application of the neural network for forecasting the storm surge. The original data of Jiangjyun station in Taiwan will be used to test the performance of the present model. The results indicate that the neural network can be efficiently forecasted storm surge using the four input factors, including the wind velocity, wind direction, pressure and harmonic analysis tidal level.