基于列生成的毫米波多连接链路配置调度算法

多连接技术允许用户同时建立和保持与多个小区/接入点的连接，通过网络元素之间的协调在吞吐量和可靠性方面大幅提高网络性能。针对毫米波通信中超高频段的链路中断问题，研究了多连接基于链路配置的调度算法，以提高链路调度效率，降低复杂度。首先，在系统模型中采用链路配置作为优化变量；其次，设计了多连接比例公平的调度准则；最后，提出一种基于列生成算法的链路配置调度优化算法，利用Dantzig-Wolfe分解将原问题分解为限制主问题和定价问题，并结合分支定界方法获得最优解。仿真结果表明，所提算法能够在数值上逼近全局最优，并且比现有的毫米波蜂窝网络链路调度方案增益平均提高40%以上。     

Chinese National Scenic Areas’ Tourism Efficiency: Multi-scale Fluctuation,Prediction and Optimization

The tourism efficiency in scenic areas is influenced by multi-factor joined effects, presenting multi-scale fluctuation characteristics in temporal terms and regional differences in spatial terms. This paper, by adopting the data envelopment analysis (DEA) method and Bootstrap-DEA model, calculates their tourism and decomposition efficiency. Then, based on methods of empirical mode decomposition, wavelet decomposition and Morlet wavelet multi-scale decomposition, the paper reveals the fluctuation characteristics, process and its spatial differentiation of tourism efficiency change in scenic areas from the perspective of the time–frequency domain, quantitatively investigating the important factors influencing its dynamic change, so as to summarize and refine the dynamic evolution models of tourism efficiency. Lastly, the mean generating function method is applied to conduct statistical prediction for scenic areas’ tourism efficiency in different regions, combined with the principal component method for regional integration, which provides an effective way for studying temporal–spatial differentiation characteristics of tourism efficiency change in scenic areas.     

古塔的变形情况及趋势研究

依据2013年全国大学生数学建模竞赛C题所给的古塔各层中观测点坐标的信息,运用基于最小二乘法的椭圆拟合算法结合MATLAB软件,列表给出各次测量的古塔各层中心坐标。利用古塔各层中心坐标,并将问题进行转化,采用初等数学模型研究古塔的倾斜程度、弯曲程度、扭曲程度,最后建立灰色预测模型GM(1,1),对上述引起古塔变形的三个因素进行拟合、预测,分析古塔的变形趋势。     

Validation and forecasting accuracy in models of climate change

Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster’s perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.     

Do high-frequency measures of volatility improve forecasts of return distributions?

Many finance questions require the predictive distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term structure of density forecasts is used to investigate the importance of: the intraday information embodied in the daily RV estimates; the functional form for log(RV)$log\left(RV\right)$ dynamics; the timing of information availability; and the assumed distributions of both return and log(RV)$log\left(RV\right)$ innovations. We find that a joint model of returns and volatility that features two components for log(RV)$log\left(RV\right)$ provides a good fit to S&P 500 and IBM data, and is a significant improvement over an EGARCH model estimated from daily returns.     

交通机动化爆炸性增长的模型分析

近年来我国城市交通出现较为严重的拥挤,其中一个重要的背景原因就是机动化进程不但呈现加速趋势,而且其增长具有爆炸性,而以往对机动化水平的预测明显偏低。基于交通规划预测中常用的逻辑斯蒂(Logistic)模型分析了爆炸性机动化的形成机制,并在建立收入增长的动态模型的基础上进行了数值模拟,进而提出了相关的政策建议。     

基于灰色系统理论的汽车报废量预测

随着汽车报废量快速提高,以及生产者责任制度的推行,报废汽车回收利用受到广泛关注。本文基于灰色系统理论,选取汽车全生命周期中的重要且白色信息指标,运用MGM(1,n)模型对汽车报废量进行预测,为建立汽车逆向物流系统及政府出台相关政策提供数量依据,促进报废汽车回收处理,达到保护环境、提高资源利用率和保障交通安全的目的。     

灰色预测在航材消耗预测上的应用

针对航材种类繁多、规格复杂、准确预测航材消耗比较困难的问题,提出利用灰色系统理论中的GM(1,1)模型,根据历年航材消耗数据,确定其预测模型。算例表明本模型具有较大的准确性和实用性。     

基于BP神经网络模型的国内旅游人数预测

旅游人数的分析和预测是旅游规划与管理的关键性、基础性工作。目前旅游人数预测主要采用基于传统研究方法的预测方法。提出了一种基于BP神经网络模型的国内旅游人数预测新方法,对国内旅游人数的变化趋势进行了综合分析与预测,结果表明该方法具有较高的精度,该模型在旅游人数预测中的应用是可行的。     

基于灰色系统理论的河南未来人口发展预测研究

以河南人口发展数为研究对象,根据相关统计资料应用年末总人口数值建立灰色动态预测模型,对未来10年河南省人口数值进行预测研究。结果表明,河南人口2010年底将超过1亿,2015年为1.03亿,到2020年将达到1.05亿,年均增加51.6万人,最后给出了一些相关建议。