层次化网络SCADA通讯模型及其应用

为在大型网络分布式SCADA（Super Control and Data Acquisition）应用系统中解决诸如电力、通信、石油、化工、制造业等行业或领域，在过程、流程、状态方面的数据监视、数据控制、数据采集与数据管理的高效、实时问题，解决各级子系统之间人工逐级汇接的问题，提高应用系统的稳定性、可维护性，这里提出了一种层次化网络SCADA通讯模型。该模型是通过在层次化网络SCADA模型中，引入基于TCP／IP协议的客户机／服务器查询方式数据通讯技术和点对点主动数据传递技术而建立的。根据该模型所设计的产品，经过生产实践证明，应用该模型可以较方便地构造分布在不同区域计算机上的SCADA系统，进行高效的实时通信。该模型为具有多层结构的大型分布式网络化集中SCADA系统的设计、开发，探索出了一条十分有效的新路径。     

石太线下行K135滑坡特征与成因分析

滑坡位于石太线下行K135路段右侧偏西，平面上呈近似椭圆形，由原有老滑坡复活而形成。根据滑坡勘察，滑坡所处地貌单元属侵蚀剥蚀低中山区，滑坡区地层组成为第四系松散堆积物下伏晚古生代二叠系石千峰组泥岩、砂岩互层。在分析滑坡几何特征、地质构造特征、水文地质条件及滑坡变形破坏特征的基础上，探讨了滑坡的成因，认为老滑坡的成因以自然形成为主，新滑坡则发育并活动于老滑坡堆积物中，因某部门在老滑坡体前缘修建收费站，进行了大面积不适当的工程开挖，形成临空面，加之降雨的作用降低了土体的粘聚力及抗剪强度，在上覆岩土体的重力作用下形成新滑坡。老滑坡为巨型厚层滑坡，属于自然滑坡，新滑坡为大型中层滑坡，属于工程滑坡。     

GPS静态定位技术在滆湖公路大桥测量中的应用

介绍了利用全球定位系统(GPS)静态定位技术，如何为滆湖公路大桥建立控制网，延伸出时控制网平差方案的确立上，以及如何使控制网中的成果与公路主线控制网中的成果互相兼容，从而为工程的设计与施工提供必要的控制资料。     

附加系统参数平差法在导线网中的应用

本文探讨了附加系统参数平差法在带有测距系统误差的导线网中的应用，说明了当系统误差比较显著时．采用附加系统参数平差法的可行性、必要性及其优越性。     

Hyper Redundancy for Accuracy Enhancement in Automated Close Range Photogrammetry

A generic network design in close range photogrammetry is one where optimal multi-ray intersection geometry is obtained with as few camera stations as practicable. Hyper redundancy is a concept whereby, once the generic network is in place, many additional images are recorded, with the beneficial impact upon object point precision being equivalent to the presence of multiple exposures at each camera position within the generic network. The effective number of images per station within a hyper redundant network might well be in the range of 10 to 20 or more. As is apparent when it is considered that a hyper redundant network may comprise hundreds of images, the concept is only applicable in practice to fully automatic vision metrology systems, where it proves to be a very effective means of enhancing measurement accuracy at the cost of minimal additional work in the image recording phase. This paper briefly reviews the network design and accuracy aspects of hyper redundancy and illustrates the technique by way of the photogrammetric measurement of surface deformation of a radio telescope of 26 m diameter. This project required an object point measurement accuracy of σ  = 0·065 mm, or 1/400 000 of the diameter of the reflector.     

对江西农经网如何更好地提供信息服务的探讨

分析了江西农村经济信息网(www.jxagriec.gov.cn)的现状,指出农经网信息服务正面临着挑战,并在此基础上阐述了农经网信息服务的发展思路。     

广西新一代"区-地-县"三级气象信息网络系统

介绍广西新一代“区 -地 -县”三级气象信息网络系统在拓扑结构、业务软件和运行管理等方面的基本情况 ,总结了建设、运行管理过程中成功经验以及存在的问题 ,指出 :充分利用现有通讯网络资源 ,强化业务软件的开发应用 ,提高网络传输监控管理水平 ,是经济欠发达地区扩展气象信息网络业务能力的重要途径     

矿产勘查与评估的统计地球化学方法

统计地球化学在矿产勘查与评估中的应用，主要包括采术规则，临界品位计算，资源量／储量公式，边界品位确定，网度判别，矿床经济分类以及矿床开发风险等内容。     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

Modelling evaporation using an artificial neural network algorithm

This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.