基于无线传输的智能小区门禁系统设计

设计了基于无线传输的智能小区门禁系统,介绍了系统的读卡模块、无线数据传输模块、液晶显示模块、随机密码键盘模块。设计了以大容量串行e-Flash MM36SB020为载体的16×16点阵字库。针对数据传输中易出现的网络冲突问题,在通信协议中提出了随机延时的解决方案。     

Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

We study the performance of recently developed linear regression models for interval data when it comes to forecasting the uncertainty surrounding future stock returns. These interval data models use easy‐to‐compute daily return intervals during the modeling, estimation and forecasting stage. They have to stand up to comparable point‐data models of the well‐known capital asset pricing model type—which employ single daily returns based on successive closing prices and might allow for GARCH effects—in a comprehensive out‐of‐sample forecasting competition. The latter comprises roughly 1000 daily observations on all 30 stocks that constitute the DAX, Germany's main stock index, for a period covering both the calm market phase before and the more turbulent times during the recent financial crisis. The interval data models clearly outperform simple random walk benchmarks as well as the point‐data competitors in the great majority of cases. This result does not only hold when one‐day‐ahead forecasts of the conditional variance are considered, but is even more evident when the focus is on forecasting the width or the exact location of the next day's return interval. Regression models based on interval arithmetic thus prove to be a promising alternative to established point‐data volatility forecasting tools. Copyright ©2015 John Wiley & Sons, Ltd.     

The Role of Survey Data in Nowcasting Euro Area GDP Growth

This paper evaluates the impact of new releases of financial, real activity and survey data on nowcasting euro area gross domestic product (GDP). We show that all three data categories positively impact on the accuracy of GDP nowcasts, whereby the effect is largest in the case of real activity data. When treating variables as if they were all published at the same time and without any time lag, financial series lose all their significance, while survey data remain an important ingredient for the nowcasting exercise. The subsequent analysis shows that the sectoral coverage of survey data, which is broader than that of timely available real activity data, as well as their information content stemming from questions focusing on agents' expectations, are the main sources of the ‘genuine’ predictive power of survey data. When the forecast period is restricted to the 2008–09 financial crisis, the main change is an enhanced forecasting role for financial data. Copyright © 2015 John Wiley & Sons, Ltd.     

基于小波变换和凸集投影的数据恢复和建模算法

在过程控制中,当参数仅能以低采样率获得时,数据恢复就在建模中显得非常重要.本文提出了基于小波变换和凸集投影的数据恢复和建模算法.待恢复数据的先验知识采用凸集表达,并利用向凸集迭代投影(POCSProjection onto Convex     

一种通用的分布式实时数据显控方法

提出了基于控制文件的通用分布式实时数据显控方法,给出了其总体逻辑结构及数据显控总流程,描述了显示数据与控制文件中显示控制说明项的对应关系,最后给出了一种高效、灵活、易扩充的控制文件实现方法。     

DMGSP:一种快速分布式全局序列模式挖掘算法

为了解决分布式环境下挖掘全局序列模式常产生过多候选序列,加大网络通信代价问题,提出了一种基于分布式环境下的快速挖掘全局序列模式算法--DMGSP.该算法将分布式环境下的各站点得到的局部序列模式压缩到一种语法序列树上, 避免了重复的序列前缀传输. 采用合并树中结点序列规则和项序扩展策略,对非频繁序列进行剪枝,有效地约简了候选序列,减少了网络传输量,从而快速生成全局序列模式.算法分析和实验结果表明,在大数据集环境下的DMGSP算法性能优越,能够有效地挖掘全局序列模式.     

一种高效挖掘高维数据的频繁闭合模式算法

为了克服传统高维数据挖掘频繁闭合模式算法迭代产生子表,引起算法执行时间长和存储开销大等问题,提出了一种高效挖掘高维数据的频繁闭合模式的算法EMHCP. EMHCP算法采用一种新型结构位图表来压缩存储数据,在仅扫描数据库一次后,建立位图转换表.根据位图转换表来构建混合树结构,采用深度优先的方式和有效的剪枝策略高效挖掘出所有的闭合模式.从而有效地缩小了搜索空间,加快了处理速度.通过在生物数据库应用的实验结果表明, EMHCP算法比已有的CARPENTER和TD-close等算法更为有效.     

大数据在基础教育管理与决策中的应用研究

进入大数据时代后,基础教育管理和运行迎来了更多的发展机遇,基于大数据的预测、分析将逐步融入基础教育管理和决策中。大数据技术和思维将影响基础教育管理与决策的各个环节,影响基础教育发展规划,改变基础教育教学评价体系,甚至在基础教育教学思维中产生深远的影响。基础教育管理工作者应主动研究和思考,以积极的态度迎接大数据时代的来临。     

Forecasting with Micro Panels: The Case of Health Care Costs

Micro panels characterized by large numbers of individuals observed over a short time period provide a rich source of information, but as yet there is only limited experience in using such data for forecasting. Existing simulation evidence supports the use of a fixed‐effects approach when forecasting but it is not based on a truly micro panel set‐up. In this study, we exploit the linkage of a representative survey of more than 250,000 Australians aged 45 and over to 4 years of hospital, medical and pharmaceutical records. The availability of panel health cost data allows the use of predictors based on fixed‐effects estimates designed to guard against possible omitted variable biases associated with unobservable individual specific effects. We demonstrate the preference towards fixed‐effects‐based predictors is unlikely to hold in many practical situations, including our models of health care costs. Simulation evidence with a micro panel set‐up adds support and additional insights to the results obtained in the application. These results are supportive of the use of the ordinary least squares predictor in a wide range of circumstances. Copyright © 2016 John Wiley & Sons, Ltd.     

一种含有中性指标的数据包络分析方法

在应用数据包络分析方法进行评价时,要求所有指标必须具有偏好性,即所有指标必须越大越好或者越小越好.然而,当评价指标体系中含有中性(即没有偏好性)指标时,传统的数据包络分析方法则不能解决该类问题.因此,本文以经济效率与产业结构调整为背景,从系统性的角度出发提出了一种用于评价含有中性指标的数据包络分析模型.该模型不仅能给出一个经济系统的效率大小,而且还能给出该系统应如何通过产业结构调整来提高经济系统的效率.最后,应用本文提出的方法分析了天津市经济结构调整的有效性问题.