MapReduce:新型的分布式并行计算编程模型

MapReduce是Google提出的分布式并行计算编程模型,用于大规模数据的并行处理。Ma-pReduce模型受函数式编程语言的启发,将大规模数据处理作业拆分成若干个可独立运行的Map任务,分配到不同的机器上去执行,生成某种格式的中间文件,再由若干个Reduce任务合并这些中间文件获得最后的输出文件。用户在使用MapReduce模型进行大规模数据处理时,可以将主要精力放在如何编写Map和Reduce函数上,其它并行计算中的复杂问题诸如分布式文件系统、工作调度、容错、机器间通信等都交给MapReduce系统处理,在很大程度上降低了整个编程难度。MapReduce日益成为云计算平台的主流编程模型。Apache Hadoop项目提供开源的MapReduce系统还有待进一步完善。     

数字图书馆统一身份认证及综合应用研究

主要探讨利用数字证书技术实现数字图书馆的用户或读者全国统一身份认证,并在此基础上探讨数字图书资料在科研项目立项和研究成果水平鉴定,教师的教学水平和研究水平评估,启发与激励科研和教学人员及学生的创造力、创新能力等方面的综合性应用。     

荧光灯电子镇流器高频泄漏电流的自动化测试方法

本文概述了荧光灯电子镇流器高频泄漏电流的测试方法及原理。在指出传统方法的局限性基础上,提出了利用计算机实现电子镇流器高频泄漏电流的自动化测试,搭建了软件测试平台,从而提高了测试效率和结果的准确度。     

一种新型微机械陀螺仪结构方案设计与仿真

为了解决微机械陀螺仪驱动和检测模态耦合的问题,提出了一种新型的双质量块双线振动结构方案。根据微陀螺的结构和工作原理,对其驱动和检测模态进行了理论分析,给出了简化的动力学方程,并利ANSYS有限元分析软件进行了数值仿真,以验证设计思想的正确性。     

WebQuest教学模式在高校计算机教学中应用的探讨

本文提出了高校网络教学存在的问题,阐述了WebQuest教学模式基本理念及该模式在高校计算机教学中应用的实例。     

一种基于知网语义相似度计算的应用研究

随着常见问题集(FAQ)的积累,问句数量日益增大,逐页浏览的知识获取方式很难满足用户的实际需求,也浪费用户大量的精力和时间,而基于FAQ的自动问答系统是解决该问题的一种有效途径。本文设计了QuickFaq自动问答系统框架,采用基于知网的语义相似度计算模块,找出相似问句并返回答案。实验表明,该方法提高了问答系统的召回率和不匹配率。     

An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Snow is an important land cover on the earth's surface. It is characterized by its changing nature. Monitoring snow cover extent plays a significant role in dynamic studies and prevention of snow-caused disasters in pastoral areas. Using NASA EOS Terra/MODIS snow cover products and in situ observation data during the four snow seasons from November 1 to March 31 of year 2001 to 2005 in northern Xinjiang area, the accuracy of MODIS snow cover mapping algorithm under varied snow depth and land cover types was analyzed. The overall accuracy of MODIS daily snow cover mapping algorithm in clear sky condition is high at 98.5%; snow agreement reaches 98.2%, and ranges from 77.8% to 100% over the 4-year period for individual sites. Snow depth (SD) is one of the major factors affecting the accuracy of MODIS snow cover maps. MODIS does not identify any snow for SD less than 0.5 cm. The overall accuracy increases with snow depth if SD is equal to or greater than 3 cm, and decreases for SD below 3 cm. Land cover has an important influence in the accuracy of MODIS snow cover maps. The use of MOD10A1 snow cover products is severely affected by cloud cover. The 8-day composite products of MOD10A2 can effectively minimize the effect of cloud cover in most cases. Cloud cover in excess of 10% occurs on 99% of the MOD10A1 products and 14.7% of the MOD10A2 products analyzed during the four snow seasons. User-defined multiple day composite images based on MOD10A1, with flexibilities of selecting composite period, starting and ending date and composite sequence of MOD10A1 products, have an advantage in effectively monitoring snow cover extent for regional snow-caused disasters in pastoral areas.     

一种高性价比CAN总线适配卡的设计与实现

针对传统CAN总线设备存在的系统规模大、功耗高、成本高等问题,提出并实现了一种高性价比CAN适配卡系统。介绍了系统总体结构,阐述了软硬件组成及其功能,并给出硬件具体设计方案及驱动程序的实现方法。经实践证明,该适配卡系统完全可满足实际需要且结构简单、能耗低、稳定性能好。     

基于SIP家庭网关和Web Service的智能家居系统

提出了一种将 SIP 家庭网关技术和 Web Service 技术相结合的智能家居系统的设计和实现方案,详细介绍了系统各功能模块,给出了在 PDA 上调用基于 SIP 家庭网关的 Web 服务来监控家中各电器状态的实现结果。该技术可以向用户提供智能家居管理、远程监控等具体服务。     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.