洪水水文预报系统

水文预报系统是计算机算法应用于水文预报的一次实践 ,在从产汇流原理出发 ,详细阐述系统结构 ,工作原理及系统特征 ,还对软件的应用及今后开发前景进行了展望 ,随着社会发展和科技进步 ,实施计算机预报系统便于提高预报精度 ,增强水文预报的时效性 ,将使水文预报作用日益显著。     

张力减径过程快速仿真系统建模问题的研究

在空间解析曲面模型理论基础上,采用三维弹塑性大变形有限元方法,建立了复杂几何形体动态约束模型,解决了张力减径过程中的矩阵变换、模型转换和多机架全系统仿真的问题。     

中国铸造市场的气候与机遇

我国近几年铸件产量统计；国内工业用户需求和使用铸件数量；中国进出口状况，综述了我国铸造行业所采用的主要生产工艺，调查了部分具有典型代表性的工厂其生产成本构成，依据现有资料分析，旨出我国是铸件生产的大国，却不是铸件生产强国，提出了今后5年中国铸造市场展望，以及对铸件产量估计，市场展望和投资前景，抓住机遇，大展宏图。     

远程教育课件点播平台业务模式及构建方案

远程教育产业现在正处于蓬勃发展的上升态势之中。在着眼未来的互联网领域,教育产业是一个十分令人看好的投资方向。本文主要简述远程教育课件点播平台业务模式及构建方案,并通过投资预算及业务预测,计算其收益及业务拓展。     

计算机辅助电力设备故障模式与影响分析方法

以近年来我国电力企业设备检修实践为基础,从状态检修管理体制、人员素质和技术检测手段等几个方面阐述了在向状态检修模式迈进的历程中所采取的措施、设想和面临的困难及解决的思路,并论述了计算机技术在其中的应用。     

面向顾客选择的产品联合分析系统的研究与实现

运用联合分析原理建立数学模型,根据数学模型和项目情况进行系统分析设计,运用程序开发等相关技术实现在线产品分析系统,该系统提供给设计和销售人员进行产品调查和分析,从而提供产品各属性水平的效用值、重要性及产品潜在的市场占有率,以指导产品研发设计和产品进货选择.     

空间光通信中轨道短时预报

瞄准、捕获和跟踪(PAT)技术是星间光通信的关键技术之一,高精度卫星轨道短时预报能有效实现PAT.首先在EGM96地球引力场模型下建立了卫星状态动力方程和预报方程,然后改进基于数值算法的扩展卡尔曼滤波算法对卫星轨道短时预报,最后以champ卫星星载GPS实时定轨数据为卡尔曼滤波器观测数据进行仿真实验:预报卫星位置误差约亚米级,速度误差约0.05m/s;卫星位置和速度的均方差估计趋于稳定,在一定程度上能很好地克服离散误差和模型误差对轨道估计精度的影响.PAT瞄准精度约1.6 μrad.预报轨道精度能满足空间光通信PAT技术要求.     

建筑工程施工成本监测预警及协调控制

通过对施工成本影响因素的识别,建立了施工成本监测与预警信号模型,以及施工成本协调控制模型等。     

Comparison of forecasting methods with an application to predicting excess equity premium

This paper reviews various forecast methods including combination using theoretically optimal weights and those under model selection approaches. In addition, we suggest two modified simple averaging forecast combination methods—a mean corrected and a mean and scale corrected method. We conclude that due to the fact that real data is usually subject to structural breaks, rolling forecasting scheme has a better performance than fixed window and continuously updating scheme. In addition, methods that use less information appear to perform better than methods using all the sample information about the covariance structure of the available forecasts. The mean and scale corrected simple average approach yield smaller mean squared forecast error than the three widely used regression approaches suggested by Granger and Ramanathan [11].     

The behaviour of the multi-layer perceptron and the support vector regression learning methods in the prediction of NO and NO2 concentrations in Szeged, Hungary

The main aim of this paper is to predict NO and NO₂ concentrations 4 days in advance by comparing two artificial intelligence learning methods, namely, multi-layer perceptron and support vector machines, on two kinds of spatial embedding of the temporal time series. Hourly values of NO and NO₂ concentrations, as well as meteorological variables were recorded in a cross-road monitoring station with heavy traffic in Szeged, in order to build a model for predicting NO and NO₂ concentrations several hours in advance. The prediction of NO and NO₂ concentrations was performed partly on the basis of their past values, and partly on the basis of temperature, humidity and wind speed data. Since NO can be predicted more accurately, its values were considered primarily when forecasting NO₂. Time series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, namely, multi-layer perceptron and support vector regression are used to provide efficient non-linear models for NO and NO₂ time series predictions. Multi-layer perceptron is widely used to predict these time series, but support vector regression has not yet been applied for predicting NO and NO₂ concentrations. Three commonly used linear algorithms were considered as references: 1-day persistence, average of several day persistence and linear regression. Based on the good results of the average of several day persistence, a prediction scheme was introduced, which forms weighted averages instead of simple ones. The optimization of these weights was performed with linear regression in linear case and with the learning methods mentioned in non-linear case. Concerning the NO predictions, the non-linear learning methods give significantly better predictions than the reference linear methods. In the case of NO₂, the improvement of the prediction is considerable, however, it is less notable than for NO.