RESTful风格地图瓦片服务的研究

综合当前主流的WebGIS产品和瓦片缓存技术,对地图瓦片服务的重要性和未来WebGIS产品应用的趋势进行了分析.详细阐述了OGC地图瓦片服务执行规范1.0.0版本的重点内容;并且介绍了RESTful风格服务的设计原则.充分利用现有的Web基础设施,从实现的角度给出了地图瓦片服务的体系架构,整体流程,按目录存储地图瓦片的具体结构,以及地图库访问引擎模块的功能接口的实现,完成基于RESTful风格的OGC地图瓦片服务的实现.     

基于Web Services的智能社区信息系统软件集成框架

当前智能社区建设成了一种趋势,智能社区信息系统软件集成有着广泛的需求。Web服务作为一种新的分布式计算系统具有许多优异的特性,如松散耦合性、集成的灵活性等。结合智能社区信息系统软件集成的需求和Web服务的特点,设计了一种智能社区信息系统软件集成框架,并且通过实例讨论了一些具有共性的问题。     

基于Web服务的局数据管理系统

由于移动通信网络的不断扩容和业务的不断扩展,网络上网元的类型和数量都在不断增加,这就对局数据的管理提出了更高的要求。如果采用Web服务技术和用正则表达式来描述数据解析规则的方法,设计出来的局数据管理系统可以通过简单的配置来动态地将新增的网元添加到系统中,并能够以通用的接口提供解析与核查服务。通过实际运行证明,该系统具有通用性、可扩展性和较高的效率,能够保障移动网络的稳定运行。     

A hybrid intelligent system to improve predictive accuracy for cache prefetching

Cache being the fastest medium in memory hierarchy has a vital role to play for fully exploiting available resources, concealing latencies in IO operations, languishing the impact of these latencies and hence in improving system response time. Despite plenty of efforts made, caches alone cannot comprehend larger storage requirements without prefetching. Cache prefetching is speculatively fetching data to restrain all delays. However, effective prefetching requires a strong prediction mechanism to load relevant data with higher degree of accuracy. In order to ameliorate the predictive performance of cache prefetching, we applied the hybrid of two AI approaches named case based reasoning (CBR) and artificial neural networks (ANN). CBR maintains the past experience and ANN are used in adaptation phase of CBR instead of employing static rule base. The novelty of technique in this domain is valued due to hybrid of two approaches as well as usage of suffix tree in populating the CBR’s case base. Suffix trees provide rich data patterns for populating case base and greatly enhanced the overall performance. A number of evaluations from different aspects with varying parameters are presented (along with some findings) where the efficacy of our technique is affirmed with improved predictive accuracy and reduced level of associated costs.     

基于神经网络算法的智能抗干扰系统设计

为了实现在复杂电磁环境中进行高效可靠的数据传输,达到实时抵抗干扰、提升通信系统频谱利用率的目的,对非连续正交频分复用技术、变换域通信系统、扩频技术以及神经网络算法进行了研究,首次将人工智能算法应用于切换通信波形以抵抗不同类型的干扰,以此为基础设计了一种基于径向基神经网络算法的智能抗干扰系统。对三种抗干扰模式分别进行了原理介绍及仿真,验证了其各自的抗干扰能力,以及通过切换波形提升频谱利用率的可行性,并对整个智能抗干扰系统进行了仿真,经分析该系统基本满足应对突发干扰情况下的波形选择要求,与传统通信系统相比,能够适用于更多通信场景。     

基于Web服务的ESB在电信网管中的应用研究

企业服务总线是在企业应用集成的基础上,为满足这一需求而提出的一个较好的解决思路.在介绍了基于Web服务的ESB技术之后,根据Web服务技术特点和ESB的基本功能要求,提出了该技术的一种嵌入式服务管理ESM（embedded setvices management）模型,并应用于电信网管系统中.阐述了基于该模型的网管系统的总体结构和业务系统之间的服务调用过程;最后通过实验,说明了该ESM模型应用于实际网管系统的可行性.     

Automated processing for map generalization using web services

In map generalization various operators are applied to the features of a map in order to maintain and improve the legibility of the map after the scale has been changed. These operators must be applied in the proper sequence and the quality of the results must be continuously evaluated. Cartographic constraints can be used to define the conditions that have to be met in order to make a map legible and compliant to the user needs. The combinatorial optimization approaches shown in this paper use cartographic constraints to control and restrict the selection and application of a variety of different independent generalization operators into an optimal sequence. Different optimization techniques including hill climbing, simulated annealing and genetic deep search are presented and evaluated experimentally by the example of the generalization of buildings in blocks. All algorithms used in this paper have been implemented in a web services framework. This allows the use of distributed and parallel processing in order to speed up the search for optimized generalization operator sequences.

基于人工神经网络的智能化煤矿监测系统的路径规划

给出了一种煤矿监测、数据采集用智能化监测系统的设计方案,监测系统在运行的过程中可实现数据采集、循迹、避障、测距等功能.监测系统的路径规划采用了一种人工神经网络算法,该算法利用神经网络模型描述监测系统工作空间的动态环境信息,并建立起机器人动态避障与网络输出间的关系.仿真结果表明该算法具有较好的环境适应性和实时性,所用的动态路径规划方法是正确和有效的.     

A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network

One of the undesirable phenomena in the surface mines, which results in various hazards for human and facilities, is flyrock. It seems that the careful study of the subject and its effects on the environment can affect the control of flyrock hazards in the studied area. Therefore, the use of intelligent models and methods which are capable of predicting and simulating the risk of flyrock can be considered as an appropriate solution in this regard. The current research was conducted using nonlinear models and Monte Carlo (MC) simulation. The data used in this study consist of 260 samples of rock thrown from a mine in Malaysia. The parameters used in these models include hole’s diameter (D), hole’s depth (HD), burden to spacing (BS), stemming (ST), maximum charge per delay (MC), and powder factor (PF). At first, multiple regression analysis (MRA) and artificial neural network (ANN) models were used in order to develop a non-linear relationship between dependent and independent parameters. The ANN model was an appropriate predictor of flyrock in the mine. Then using the best implemented model of ANN, the flyrock environmental phenomenon was simulated using MC technique. MC simulation showed a proper level of accuracy of flyrock ranges in the mine. Using this simulation, it can be concluded with 90% accuracy that the Flyrock phenomenon does not exceed 331 m. Under these conditions, this simulation can be used for various areas requiring risk assessment. Finally, a sensitive analysis was carried out on data. This analysis showed MC has the greatest effect on flyrock.     

基于Web Services的图像处理系统

随着分布式应用的普及,Web Services作为一项新兴技术具有很好的发展前景。本论文首先介绍了Web Services的体系结构;随后通过一个图像缩放处理应用详细描述了构建Web Services的步骤和方法,并通过WSDL文件实现服务器端和客户端的信息交互。最后通过建立UDDI注册中心,完成Web Services的动态注册、发现和绑定,并有效地实现分布式环境下的资源共享和协同工作。     

基于元搜索引擎的专业式智能网络信息检索系统

随着网络信息资源的迅速增加和实时更新,如何及时准确地获取自己所需的有用信息是现代网络信息检索技术需要解决的主要问题。介绍了一种基于元搜索引擎的新型专业式智能网络信息检索系统。该系统建立专业词库和用户知识库,结合人工智能的Agent技术,能够很好地对用户的兴趣进行自适应学习,以实现个性化的信息检索,既保证了查全率,又提高了检索结果的查准率。     

神经网络在智能火灾预警系统的应用

提出了综合利用多种传感器准确判断火焰状态的算法.在神经网络算法下,根据一氧化碳传感器、烟雾传感器和温度传感器数据,对火焰状态进行分类.使用有限内存的拟牛顿(L-BFGS)优化算法对传统反向传播(BP)算法进行改进,加快了神经网络的收敛过程,有效提高了系统精度.实验验证了算法能够有效降低火灾预警系统的误报率,增强系统安全报警的灵敏度和可靠性,实现了实时有效的火灾预警.     

Key factors in web latency savings in an experimental prefetching system

Although Internet service providers and communications companies are continuously offering higher and higher bandwidths, users still complain about the high latency they perceive when downloading pages from the web. Therefore, latency can be considered as the main web performance metric from the user??s point of view. Many studies have demonstrated that web prefetching can be an interesting technique to reduce such latency at the expense of slightly increasing the network traffic. In this context, this paper presents an empirical study to investigate the maximum benefits that web users can expect from prefetching techniques in the current web. Unlike previous theoretical studies, this work considers a realistic prefetching architecture using real traces. In this way, the influence of real implementation constraints are considered and analyzed. The results obtained show that web prefetching could improve page latency up to 52% in the studied traces.     

基于卷积神经网络的层级化智能故障诊断算法

传统智能故障诊断算法需要依赖人工特征提取和专家知识,而旋转机械设备复杂的工作环境和工况使得传统算法在实际应用中缺乏良好的自适应性和泛化性.针对以上问题,提出基于卷积神经网络(Convolutional neural network,CNN)的层级化故障诊断算法(CNN based hierarchical fault diagnosis,CNN-HFD).首先,将原始振动信号进行分段预处理,以实现数据扩容;然后,分别根据故障类型和故障程度设计多个卷积神经网络,并将原始振动数据以某一时间步进行分割,作为卷积神经网络的输入进行训练;最后,将待识别信号送入CNN-HFD模型,经过分层故障诊断,在末端卷积神经网络输出相应故障类别和程度.通过滚动轴承振动数据库的实验表明,所提出的算法不仅具有高达99.5%以上的故障识别率,而且在负载发生变化时依然可以保持高达97%以上的故障识别率,具有较好的鲁棒性和泛化性能.     

Towards a web services and intelligent agents-based negotiation system for B2B eCommerce

With the explosive growth of the number of transactions conducted via electronic channels, there is a pressing need for the development of intelligent support tools to improve the degree and sophistication of automation for eCommerce. With reference to the BBT business model, negotiation is one of key steps for B2B eCommerce. Nevertheless, classical negotiation models are ineffective for supporting multi-agent multi-issue negotiations often encountered in eBusiness environment. The first contribution of this paper is the exploitation of Web services and intelligent agent techniques for the design and development of a distributed service discovery and negotiation system to streamline B2B eCommerce. In addition, an effective and efficient integrative negotiation mechanism is developed to conduct multi-party multi-issue negotiations for B2B eCommerce. Finally, an empirical study is conducted to evaluate our intelligent agents-based negotiation mechanism and to compare the negotiation performance of our software agents with that of their human counterparts. Our research work opens the door to the development of the next generation of intelligent system solutions to support B2B eCommerce.     

构建基于Web Services的视频点播系统

在分析目前网上视频点播系统缺陷的基础上,提出了利用WebServices技术实现网上视频点播的思路,并介绍了视频点播的实现原理、实现方法和实现过程;简要分析了WebServices的工作原理,利用System,IO对象模型成功地在ASP.NET中实现了视频点播功能.     

一种基于BP神经网络的多传感器系统信号降噪算法

传感器在对目标检测时,输出信号不可避免地含有白噪声.利用BP神经网络的非线性映射能力,提出一种基于BP网络的多传感器系统信号降噪压缩方法.多传感器含噪声的输出信号序列和目标真值作为样本,用于网络训练,用检验样本对训练后的网络进行检验,并与最优加权以及最优加权与递推最小二乘法相结合的滤波方法比较.MATLAB下的仿真结果表明:BP网络用于多传感器系统滤波有明显效果.     

基于Cookie的Web服务安全认证系统

Web服务是一种崭新的分布式计算模式,是下一代电子商务的框架。但Web服务能否顺利发展的关键是其安全问题,对Web服务请求者的身份进行认证是解决安全问题的重要途径。为克服HTTP协议的无状态性,在对基于cookie的认证机制的实现原理和过程进行分析和研究的基础上,提出了一种基于Cookie的认证系统,详细介绍了其工作流程。谊认证系统具备单点登录和适应所有浏览器等特点,比较适合于Web服务在单域内的安全认证。     

基于粗神经网络的仿人智能机器人的语音融合算法研究

将粗集合理论与神经网络相结合，提出一种基于粗神经网络的新的信息融合方法，用于仿人智能机器人的语音融合。该方法不仅可以接受定量输入，而且可以接受定性输入，即输入是一个范围，或在观测时间内输入是变化的。由于粗神经网络的误差传递函数不可微，所以采用遗传算法来训练粗神经网络。仿真实验结果表明，基于粗神经网络的信息融合方法有效地提高了语音的识别率。     

User behavior modeling and content based speculative web page prefetching

This paper provides a transparent and speculative algorithm for content based web page prefetching. The algorithm relies on a profile based on the Internet browsing habits of the user. It aims at reducing the perceived latency when the user requests a document by clicking on a hyperlink. The proposed user profile relies on the frequency of occurrence for selected elements forming the web pages visited by the user. These frequencies are employed in a mechanism for the prediction of the user’s future actions. For the anticipation of an adjacent action, the anchored text around each of the outbound links is used and weights are assigned to these links. Some of the linked documents are then prefetched and stored in a local cache according to the assigned weights. The proposed algorithm was tested against three different prefetching algorithms and yield improved cache–hit rates given a moderate bandwidth overhead. Furthermore, the precision of accurately inferring the user’s preference is evaluated through the recall–precision curves. Statistical evaluation testifies that the achieved recall–precision performance improvement is significant.