基于PSR-WSVM模型的边坡位移预测

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。     

Ab Initio Prediction of ^29Si-NMR Chemical Shifts

The ability of several ab initio models to predict experimental 29Si-NMR chemical shift is examined. The shielding values of trimethylsilyl chloride (A), t-butyldimethylsilyl chloride (B) and allyltrimethylsilane (C) are calculated by GIAO, CSGT and IGAIM methods, using HF/6-31G^*, B3LYP/6-31G^* , HF/6-311 G^**, B3LYP/6-311 G^** and MPWlPW91/6-311 G^** models respectively. The 29Si chemical shifts calculated by GIAO method using HF/6-311 G^** model are highly in agreement with those obtained experimentally. All of the models above reproduce the trends of chemical shifts in all cases studied, suggesting that the models are of practical value.     

上海天文台激光测距站的时频系统

本叙述了上海天台人卫激光测距站的时频系统。随着测距精度的提高，时频系统也要不断地加以改进，包括频率标准和时间同步技术。     

用C#.Net实现软件系统的动态更新

为了便于大型软件系统的后续开发和用户软件系统的升级，介绍了软件系统在Internet/Intranet上更新功能或数据的原理和方法，并用C#．Net进行了编程实现。     

天文像复原迭代位移叠加法中初始信息的选择

给出了天文像复原“迭代位移叠加法”的第一步中确定位移基准点的方法-将目标像结构的初始信息与目标斑点图间的互相关性运算的极大值点作为基准点，按此基准点对目标斑点图进行位移叠加，第一步中初始信息的选择是展开迭代位移叠加过程的关键。讨论了目标初始信息的性质和提取方法。     

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.     

平凉隐伏岩溶水环境同位素研究

地下水资源可持续利用的一个急待解决的重要问题,是对地下水补给和更新能力的评价.利用环境同位素技术研究地下水的补给和可更新性是当前较为新颖的方法之一.在西北干旱、半干旱的隐伏岩溶地区,地下水埋藏条件复杂,常规的地质勘探方法所能提供的水文地质信息有限,环境同位素方法在研究地下水的补给及可更新能力方面发挥了优势,可对传统方法进行补充和验证.其结果表明,研究区隐伏岩溶水形成较早,且有大量现代水的混入,平均混入量为54%.说明区内隐伏岩溶水的补给和更新能力较好.环境同位素分析结果还显示,大岔河隐伏岩溶水为一相对独立、半开放的水文地质单元,其补给来源部分为流域内大气降水、地表水的补给,部分为东南部三道沟岩溶地下水的补给;根据环境同位素EPM模型计算,地下水的滞留时间为36 a.地下水储存量为1.314×108 m3; 储水系数为7.29×10-3.这一结果与传统勘探方法的计算结果基本吻合,说明环境同位素方法的实用性.     

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。