空间信息网络服务中的数据安全策愀

以地理信息网络服务的安全策略为研究对象，根据我国空间数据生产到应用整个流程保密管理规定，研究地理空间数据在存储、分发、传输、表现以及与其他系统集成应用阶段上的安全策略，解决地理空间数据共享与空间数据安全之间的矛盾。     

STUDY ON DEVELOPMENT STRATEGY OF SHIPPING CENTERS AND TRANSPORTATION NETWORKS IN THE YELLOW SEA RIM IN THE YELLOW SEA RIM

It is very important to establish cooperative mechanism to guarantee all members to develop their e-conomies in the Yellow Sea Rim. In this paper, the development strategies ofshipplng centers and transportation networkare discussed based on economic giobalization tendency. The results argue that a united transportation network should bebuilt in order to promote the economic competition of Northeast Asia in the world. As a key component of the economiccooperation, a hierarchical shipping centers network should be established with Hong Kong, Shanghai, Pusan, Koho,and Tokyo as cores. The authorities of China, Japan, R. O. Korea and D. P. B. Korea should make more efforts tobuild a set of cooperation institutions based on raising the transportation efficiency.     

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

Generalized strain probing of constitutive models

Advanced material constitutive models are used to describe complex soil behaviour. These models are often used in the solution of boundary value problems under general loading conditions. Users and developers of constitutive models need to methodically investigate the represented soil response under a wide range of loading conditions. This paper presents a systematic procedure for probing constitutive models. A general incremental strain probe, 6D hyperspherical strain probe (HSP), is introduced to examine rate‐independent model response under all possible strain loading conditions. Two special cases of HSP, the true triaxial strain probe (TTSP) and the plane‐strain strain probe (PSSP), are used to generate 3‐D objects that represent model stress response to probing. The TTSP, PSSP and general HSP procedures are demonstrated using elasto‐plastic models. The objects resulting from the probing procedure readily highlight important model characteristics including anisotropy, yielding, hardening, softening and failure. The PSSP procedure is applied to a Neural Network (NN) based constitutive model. It shows that this probing is especially useful in understanding NN constitutive models, which do not contain explicit functions for yield surface, hardening, or anisotropy. Copyright © 2004 John Wiley & Sons, Ltd.     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

关于网络用户经济效益评测模型的研究

着重讨论了网络用户经济效益的内涵以及用户的投入量和产出量，并提出了评价网络用户经济效益的指标体系，包括成本降低率，成本利润增长率、产品开发时间约率、市场占有增长率等系列指标。     

卫星激光测距的新进展

扼要综述了近几年国际上卫星激光测距的进展，介绍了国内激光测距网的现状，展望了未来卫星激光测距的发展。     

VPN技术在浙江省气象网络中的应用

随着浙江省气象业务的不断发展，网络数据交换日益增多，同时更多基于网络的气象业务的增加，如网络视频会商、自动站数据传输等，使目前浙江省气象系统专线网络运行已达到极限，所以需要建立VPN网以对现有的网络作链路上的备份，而VPN的建立同时还可以实现移动办公，领导能在远程调用内网的资料进行远程决策服务和汇报工作。主要介绍VPN技术并叙述VPN技术在浙江省气象系统网络中的具体应用。     

水下滑翔器主体外形优化设计

对水下滑翔器主体外形的优化设计问题进行研究.首先,在考虑内部机械结构限制的条件下,建立了主体外形优化数学模型,并采用CFX计算了若干主体形状的绕流阻力.在此基础上建立了映射主体外形的尺寸参数与绕流阻力的BP神经网络.然后将建立的神经网络作为优化设计问题的目标函数,采用坐标轮换法对滑翔器主体外形进行了优化设计,确定了尺寸参数的最优解.