MATLAB在运用系统建模处理南海气象数据过程中应用

介绍了MATLAB语言特点和系统建模方法的基本理论.根据南海气象数据的实际建模处理过程,给出了建模的详细步骤及其MATLAB实现过程以及MATLABTM的主要实现程序.试验讨论和结果表明利用MATLAB语言可以方便地对南海气象数据用系统建模方法进行建模和处理,MATLAB在运用系统建模法处理南海气象数据方面具有明显的优越性.     

台湾地层检讨(续)

所谓‘大寮层’者,以页岩与砂岩之交互层为主,厚约350公尺,在其上部,砂岩发育较佳,而在山下部则反是。砂岩往住带石灰质并含有化石,尤其在上部有富含Ditrupa sp之灰质的砂岩发现,所谓Ditrupa化石带是也。此外尚有下列之化石发现:     

阿吾拉勒山西段二叠纪火山岩组合与构造环境分析

阿吾拉勒山体西段发育了厚度巨大的下二叠统火山岩,这些火山岩均属碱性玄武岩系列,以钾质类型为主,钠质类型次之,岩浆演化呈现出跨越B型趋势。这种火山活动是在大陆地壳内部产生的,代表了陆内裂谷演化的早期阶段。在早二叠世末期,由于受到新源运动的影响,这种火山活动被终止了。     

The Geologic Time Scale and the Italian stratigraphic record

The construction of the Geologic Time Scale (GTS) is a titanic scientific challenge that has been under way for two centuries and will require much dedicated effort in the future. Italy preserves a paramount stratigraphic record of Mesozoic and Cenozoic marine sediments that have been significant in the development of the modern GTS. The Italian stratigraphic record has been histori-cally important in introducing and defining the standard Chronostratigraphic Units (CUs) of the Neogene and Quaternary. Pelagic successions from Northern Apen-nines and Southern Alps have been used in the seventies for integrating the late Cretaceous-Paleogene Geomag-netic Polarity Time Scale (GPTS) with planktonic microfossil biostratieraohv and standard CUs. This was a major contribution to the construction of a new gener-ation of GTS based on integrated magnetobiochronol-ogy. The middle Miocene to early Pleistocene marinerecord from Sicily and southern Italy has been funda-mental for establishing the recently developed Astro-nomical Time Scale (ATS). In prospect, there are many potentials still to be exploited in the Italian marine stratigraphic record for implementing the GTS by defin-ing GSSPs of various CUs, improving magneto-biochronology and extending downwards the ATS.     

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.     

分组密码的安全性与强化设计

分组密码是数据通讯中最常用的数据加密方式，以DES为例分析现有分组加密算法的安全隐患，并提出了可变密钥加密和变长密文输出两个新思路，可应用于所有现有分组加密算法以提高安全性，并就该方法的安全性、效率、具体应用做出了分析。