ENVI for ArcGIS Server(ENVIES)影像在线分析技术

基于B/S的在线影像分析是一个全新的理念,主要思想是将专业的遥感影像处理与分析功能、业务模型和影像数据部署在服务器端,客户端只需要用Web浏览器就可以获取影像处理与分析的结果,此服务模式将帮助更多的人一起分享影像的价值。使无遥感专业背景的人员在很少人工干预的情况下,能快速、高效     

遥感影像Web共享关键技术应用研究

研究海量遥感数据在Web 共享中所需要的网络支撑环境、数据自动入库、数据自动传输、影像高效查询等关键运行技术。在关键技术研究的基础上,详细分析与设计了针对海量遥感数据的可视化查询、浏览及下载功能,并基于 Web服务等相关计算机技术,实现了海量遥感数据共享原型系统,完成海量遥感数据的 Web 共享。
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基于OGCWPS服务链特性的遥感影像处理服务研究

遥感影像处理服务需求不断扩大,逐渐从单一的服务模式走向服务链的集合。因此,本文提出了基于Web服务技术、OGCWPS（WebProcessingService）、WCS（WebCoverageService）规范和可视化工作流技术．构建具有流程可视化定制功能的遥感影像在线处理服务链框架的方案．用于解决复杂的空间数据在线处理服务。并以一个遥感生态环境算法流程的定制、部署、在线处理为例说明该框架具有可行性、灵活性。     

基于ArcScene的遥感影像三维可视化技术研究与应用

分析遥感影像三维可视化的研究意义和现状;探讨基于ArcScene平台遥感影像三维可视化的实现方法和技术流程;以西藏自治区日喀则地区定日县的TM遥感影像和SRTM DEM数据,以及定日县遥感测站点经纬度坐标为数据实例,完成了定日县遥感影像三维地形可视化的构建,为该地区地形勘察、地理分析等提供了良好的三维环境基础资料。结合RS与GIS完成三维地形可视化构建的技术,为3S技术的结合应用做出了推广。     

分布式环境下的遥感影像组织与调度

通过研究遥感影像数据特点和分析应用现状,结合细节层次技术思想与影像金字塔模型,以优化的碎片式影像划分算法对影像进行分级分块的处理。按照共享文件目录的方式组织碎片文件,建立基于Web的分布式遥感影像数据发布框架,实现对遥感影像的快速组织与实时发布,为解决当前遥感影像数据共享问题提供了较好的解决思路。     

基于分布式的RS影像快速分发机理及应用

提出利用分布式技术来建立遥感数据个性化快速分发服务体系的整体策略,研究了Web服务构建影像处理平台的相关技术,开发了适应于遥感影像快速发布的遥感影像压缩及地理编码信息处理模块,实现了适应于当前网络环境下遥感影像个性化,实时分发方式。实验结果表明,遥感影像发布效率有很大提高,个性化需求得到满足。     

基于Web Service的在线水文模型服务研究

通过分析目前水文模型本地服务模式的不足,针对当前国内外Web Service技术在水文信息系统中的应用现状,设计并实现了一个基于Web Service技术的在线水文模型服务系统,示范了基于Web Service技术的网络数据获取、水文模型运行模拟等过程,说明基于Web Service技术的在线水文模型服务较好地实现了水文模型网络共享,支持异地模型访问。建议在线水文模型服务结构从网络数据库/数据中心获取数据,一定程度上解决了传统模式从本机上传海量数据等问题。这种在线水文模型服务模式节约了模型运行成本、降低了用户要求,在具体水文水资源管理事务中具有较好的应用和拓展前景。     

基于web挖掘的用户服务研究

数据丰富而知识贫乏导致了知识发现和数据挖掘领域的出现。基于Web的数据挖掘，是从Web海量的数据中自动、智能地抽取隐藏于这些数据中的知识，分析了Web挖掘技术的概念、特点、技术等。根据Web数据挖掘最流行的分类，可以分为Web内容挖掘、Web结构挖掘和Web使用记录挖掘。其中Web使用挖掘就是运用数据挖掘的思想来对服务器日志进行分析处理。该文根据Web数据挖掘的最近研究状况，主要论述了一个更新的频繁路径集的挖掘浏览模式在Web用户个性化服务中的应用，同时，还对发现的知识讨论了其在在线服务中的应用并给出了相应算法。     

基于QoS生命周期管理的服务组合在线优化方法

针对当前基于QoS的Web服务组合研究仅限于QoS生命周期的某一环节,提出了一个支持QoS生命周期管理的Web服务框架,基于该框架研究了服务组合在线优化的方法,提出了自动修正判断矩阵的层次分析法来实现基于QoS的在线局部优化策略。该方法针对因各种原因用户只能提供各QoS属性之间的相对重要性,而不能提供全局的QoS需求的应用场景,提供了一种在线局部优化解决方案。     

基于免疫遗传算法的多维多层关联规则挖掘

依据遥感影像的特点及WCS标准,研究面向服务的遥感数据挖掘模式及其基于工作流的分布式系统架构。在此基础上,采用.Net体系及WSBPEL流程建模语言,设计一个遥感影像纹理关联规则的挖掘系统,并研究对大数据集的访问、网络资源占用、程序的可伸缩性等方面的实现技术,为Internet环境下遥感影像的处理提供了依据。     

Revealing only the superficial me: Exploring categorical self-disclosure online

Despite previous research demonstrating that online self-disclosure occurs in an accelerated manner compared to offline interactions, little is known about the content of online disclosures. This study highlights a number of issues that arise when exploring the self-disclosure of different types of personal information in initial general online communications. Forty-eight students and 48 non-students completed a purpose-developed attitude towards online relationship formation questionnaire and Magno’s (2009) self-disclosure for beliefs, relationships, personal matters, interests and intimate feelings questionnaire. Findings suggest that people are more likely to self-disclose information online the more positive is their attitude towards forming relationships online. Moreover, this self-disclosure initially occurs only for superficial self-information relating to personal matters and interests, implying that it is the quantity of online exchanges that is enhanced rather than the quality thereof. These findings raise a number of issues relating to type of self-information disclosed online, as well as intent, Internet arena, social identity, privacy, trust, and general methodological issues that would benefit from further experimental exploration. Implications of the findings for future research to explore categorical self-disclosure online within an existing theoretical framework of self-categorisation and self-identity theory as well as the self-memory-system model of autobiographical memory are discussed.     

基于.NET的在线学习系统中的实时考勤方法研究

在线学习技术是现代教育发展的一个方向,如何有效地对学员进行管理也成为在线学习模式下一个亟待解决了问题。本文对在线学习模式下考勤管理进行了深入研究,针对在线学习中的学员流动频繁,考勤的实时性要求比较高的特点,以基于.NET的在线学习系统的实际开发为例,从考勤模块的总体设计到考勤信息的实时存取实现方法等方面进行了详细阐述。     

在线信誉系统中的信任模型有效性分析

从理论上证明,由于信任模型存在忽视评分用户信任度的缺陷,现有在线信誉系统难以激励卖者从事持续的诚实交易。针对此问题,构建了基于评分可信度的信任模型。理论分析表明,该模型可有效弥补现有信任模型的不足。     

我国教育网络游戏初探

随着我国网络游戏市场的不断发展,教育网络游戏也初步发展,如何将教育和网络游戏有效的结合一直是个争论的话题。通过对我国教育网络游戏的思考,教育网络游戏必须以当代教育思想为指导、网络游戏为基础、符合我国现在的教育现状的一款具有教育功能的网络游戏。     

Modified online Newton step based on elementwise multiplication

The second-order method using a Newton step is a suitable technique in online learning to guarantee a regret bound. The large data are a challenge in the Newton method to store second-order matrices such as the hessian. In this article, we have proposed a modified online Newton step that stores first- and second-order matrices of dimension m (classes) by d (features). We have used elementwise arithmetic operations to maintain the size of matrices. The modified second-order matrix size results in faster computations. Also, the mistake rate is on par with respect to popular methods in the literature. The experimental outcome indicates that proposed method could be helpful to handle large multiclass datasets on common desktop machines using second-order method as the Newton step.     

Using Prezi to Structure Online Videos for Information Literacy Instruction: A Case Study

This case study describes how librarians at Radford University use Prezi, a free, cloud-based, online presentation software, as a platform to structure the content of short online videos focused on various research skills. By utilizing Prezi's flexible formatting, templates, and panning and zooming features, librarians are able to visually illustrate the relationships between varying concepts. This article discusses the online video creation process, best practices, and lessons learned from delivering structured information literacy instruction in the video medium.     

网络广告营销对汽车行业的影响

汽车网络广告营销作为高速发展的汽车产业和互联网产业结合的产物,备受广大汽车制造商的青睐,在市场上获得了巨大的成功。主要阐述了在互联网时代,网络广告在汽车行业中的作用,还分别从发展现状、存在的问题和发展对策三方面对汽车行业网络广告营销进行了分析。     

基于在线学习的自动修正组合投资选择算法

提出了一个基于在线学习的自动修正组合投资选择算法(AU)。AU算法不基于任何股票市场模型,能够取得接近最佳固定重平衡组合投资(BCRP)的投资收益,并且超过了在线多阶段组合投资算法的投资收益,在同类型的在线组合投资选择算法上有明显的优势。     

培养提高网络教育学院学生的素质

网络教育作为一种新型的教育模式，如要蓬勃发展，离不开素质教育的全面推进。目前网络教育虽发展迅速，但对应的素质教育严重滞后这两者之间的矛盾已日益凸显。文章立足实践，对提高网络教育学院学生的素质教育提出了四个观点，以期能对网络教育的发展起到借鉴作用。     

How to Better Use Expert Advice

This work is concerned with online learning from expert advice. Extensive work on this problem generated numerous expert advice algorithms whose total loss is provably bounded above in terms of the loss incurred by the best expert in hindsight. Such algorithms were devised for various problem variants corresponding to various loss functions. For some loss functions, such as the square, Hellinger and entropy losses, optimal algorithms are known. However, for two of the most widely used loss functions, namely the 0/1 and absolute loss, there are still gaps between the known lower and upper bounds.In this paper we present two new expert advice algorithms and prove for them the best known 0/1 and absolute loss bounds. Given an expert advice algorithm ALG, the goal is to form an upper bound on the regret L _ALG – L* of ALG, where L _ALG is the loss of ALG and L* is the loss of the best expert in hindsight. Typically, regret bounds of a canonical form C · are sought where N is the number of experts and C is a constant. So far, the best known constant for the absolute loss function is C = 2.83, which is achieved by the recent IAWM algorithm of Auer et al. (2002). For the 0/1 loss function no bounds of this canonical form are known and the best known regret bound is , where C ₁ = e – 2 and C ₂ = 2 . This bound is achieved by a P-norm algorithm of Gentile and Littlestone (1999). Our first algorithm is a randomized extension of the guess and double algorithm of Cesa-Bianchi et al. (1997). While the guess and double algorithm achieves a canonical regret bound with C = 3.32, the expected regret of our randomized algorithm is canonically bounded with C = 2.49 for the absolute loss function. The algorithm utilizes one random choice at the start of the game. Like the deterministic guess and double algorithm, a deficiency of our algorithm is that it occasionally restarts itself and therefore forgets what it learned. Our second algorithm does not forget and enjoys the best known asymptotic performance guarantees for both the absolute and 0/1 loss functions. Specifically, in the case of the absolute loss, our algorithm is canonically bounded with C approaching and in the case of the 0/1 loss, with C approaching 3/ . In the 0/1 loss case the algorithm is randomized and the bound is on the expected regret.