面向普适环境的上下文感知中间件研究

针对普适环境下上下文感知计算需求,引入广义模型化理论,建立了一种面向通用环境资源的上下文信息数据模型;在此基础上,提出了上下文感知中间件体系框架,并详细阐述了其构件化的实施方案。该中间件平台的上下文获取层能够封装各类感知器捕获的资源信息,中间处理层负责信息的管理、推理和聚合,基于门面模式的上下文访问层提供同步和异步相结合的上下文信息统一访问入口。通过实验测试了平台的时间损耗,表明该中间件可提供通用的上下文感知服务且具有较好的系统性能。     

基于上下文感知的普适计算辅助教学设计与实现

随着计算机技术的发展,普适计算作为一种新的计算模式孕育而生。基于普适计算的应用技术能为使用人群提供主动的,个性化的服务,这一特点可以极大地改善现有计算机辅助教学过程中一些固有缺陷。文中分析了普适计算在教学中的意义,研究了近年来国内外在上下文感知领域的探索,指出了普适计算技术在实验教学中的优越性,设计了一个基于上下文感知的普适计算辅助教学实验。     

普适计算中一种上下文集成方法

普适计算作为一种全新的计算模式,目的是根据用户需要提供随时随地的服务.普适计算环境中存在大量异构的数据源,不利于上下文信息的处理和访问.文中提出了一种基于XML的上下文集成方法,在保证上下文精度和新鲜度的条件下,尽量减少上下文信息存储量,并向上提供统一的上下文表达形式,有效屏蔽了上下文信息的异构.实验测试表明,采用的文件内存保留和分片转存方法,进一步加快上下文的集成,从而有效地支持普适服务.     

普适计算环境中的上下文感知技术研究

上下文感知技术是普适计算研究领域的的热点问题。该文探讨了上下文信息的获取、建模、管理及推理方法,重点介绍上下文感知系统的体系结构。并结合老年人健康监护推荐系统的设计,分析了上下文感知技术在系统中的应用。     

普适计算环境中上下文模型的建立

普适计算作为第三代计算方式已经成为科学研究的前沿,上下文作为普适计算中最为重要研究方向之一,对普适计算的研究和发展有着大力地推动作用.就建立普适计算环境上下文模型进行了分析和讨论,对上下文的数据和通信方式也进行了分析,为上下文的实际应用提供一些理论基础准备.     

基于本体的上下文感知中间件框架

上下文感知是普适计算的核心技术之一,而描述和理解上下文信息是上下文感知的前提.由于上下文信息种类繁多、感知方式迥异,目前开发面向特定应用的上下文感知系统缺乏统一的机制和通用的架构,增加了系统开发的成本.引入语义Web技术,利用本体对上下文信息进行建模,采用本体描述语言描述上下文模型,提供了一个公共的上下文本体以实现多个独立开发的上下文感知系统对知识的共享和推理,构建了通用的上下文感知中间件框架,从而实现对域内上下文知识的共同理解.     

基于本体的上下文感知中间件框架

上下文感知是普适计算的核心技术之一,而描述和理解上下文信息是上下文感知的前提。由于上下文信息种类繁多、感知方式迥异,目前开发面向特定应用的上下文感知系统缺乏统一的机制和通用的架构,增加了系统开发的成本。引入语义Web技术,利用本体对上下文信息进行建模,采用本体描述语言描述上下文模型,提供了一个公共的上下文本体以实现多个独立开发的上下文感知系统对知识的共享和推理,构建了通用的上下文感知中间件框架,从而实现对域内上下文知识的共同理解。     

基于本体的上下文感知系统

普适计算自20世纪90年代中期被提出以后得到了普遍的关注。上下文感知作为实现普适计算的关键技术已成为重要的研究课题。在分析上下文感知系统以及组成模块的基础上,借鉴软件方法学中的快速原型模型思想,通过本体标记语言OWL给出了系统中感应模块搭建的实例。     

具有上下文感知的智能校园系统

为提高校园教学活动的效率,通过delphi建立智能校园的教师机和学生机的软件系统,实现智能校园的软件系统,结合Aglet进行上下文感知,通过对学生机的上下文信息实时读取来进行智能反应。     

面向动作的上下文感知应用的规约与运行时验证

面向动作的上下文感知（AOCA）应用组织环境中的资源,为用户动作的顺利进行提供支持.为应对环境和动作相关需求的开放性,这类应用采用轻量级、增量式的开发方法进行开发.相比于在开发阶段描述全局信息的开发方法,AOCA应用的开发可能由不同开发者在不同时间共同参与,这可能会导致较多的不一致等问题,且难以在开发阶段被发现.我们围绕使用运行时验证手段提高AOCA应用可靠性这一目标展开了研究.本文给出了对于AOCA应用运行状态进行形式化规约、对于系统级和应用级性质进行描述的方法.进一步地,我们设计实现了AOCA应用监控器.最后,通过案例分析以及性能评估证实该方法的有效性.     

基于TAP的上下文知晓系统体系结构

由于知识的异构性和动态性,在网上查找知识需要花费大量的时间和精力。为了提高效率和准确性,我们提出了面向语义网的上下文知晓模型。在分析上下文、上下文知晓和TAP的概念的基础上,构建了基于TAP的上下文知晓系统的体系结构,重点阐述了上下文知晓模型。     

Towards Context- Dependent Interpolation of Digital Elevation Models

The fractional Brownian motion (fBm) model has become increasingly popular in recent years as a mechanism for the synthesis of many natural phenomena. Unfortunately, the required interdependence of neighbouring points over an unbounded area (context dependence) renders valid approximations to fBm extremely inefficient. This paper describes a technique for the interpolation of digital elevation models to an arbitrarily high spatial resolution. This is achieved using an efficient recursive subdivision algorithm which has the ability to permit some degree of context dependence within, and across the boundaries of interpolation areas. The application of this technique to flight simulation is described and the technique is evaluated based on its ability to reduce run-time storage requirements.     

Driving Profile Modeling and Recognition Based on Soft Computing Approach

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.     

面向KDD的粒度计算建模研究

给出了全粒度空间的拓扑结构模型,进一步介绍了面向粒度计算的产生式决策逻辑语言GDL-language,然后定义了面向粒度描述的正基语言,阐明该语言公式的语义解释,给出了一种“全粒度空间＋正基语言”的粒度计算模型,并找到了正基语言系统、粒度空间和基本概念空间的关系定理。最后,把KDD任务归结为基于该模型的粒度计算问题,这样就可以把各种KDD任务统一到一个理论框架下,也便于比较和研究。这些工作无疑对令后知识发现的研究起着重要的作用．     

基于QoS的网格计算经济模型*

提出了基于经济模型的网格QoS控制机制,刻画了基于经济模型的网格QoS管理结构。用生产—消费模型给出基于多QoS指标的全局QoS优化模型,用整数规划中的背包问题形式化描述了以用户QoS效用函数为导向的资源分配问题。这种形式化描述将用户的QoS需求、资源稀缺以及服务成本等网格关键因素统一在一个框架中分析,提出用价格和契约来控制网格QoS的策略。     

多任务的边缘联邦学习:一种融合契约论和匹配博弈的激励机制

联邦学习（Federated Learning,FL）是一种新兴的分布式机器学习范式,它允许移动设备以分散的方式协作训练全局模型,同时保持训练数据在终端上面。然而,由于数千个异构分布式终端设备参与FL任务,所以FL面临的挑战是通信效率问题。为了解决上述问题,基于边缘计算的FL被提出来了,即边缘联邦学习。边缘计算利用终端设备附近的边缘节点执行模型参数的下发和聚合,进而降低通信时间。尽管有上述巨大的好处,多任务的边缘联邦学习的激励机制尚未得到很好的解决。因此,提出了一种融合契约论和匹配博弈的激励机制;然后,基于三个数据集的实验结果验证了该激励机制和匹配算法的有效性。     

云计算联盟体系结构建模研究

云计算联盟的体系结构对云计算联盟的设计、实现和资源管理效率产生重要的影响。由于云计算联盟的构成和运行过程具有典型的复杂系统特性,基于涌现理论和复杂网络理论对云计算联盟的体系结构进行建模,提出了一种基于域和复杂网络理论的云计算联盟体系结构模型,并对该模型中资源的发布和发现机制进行了详细的研究。     

Data Mining and Machine Oriented Modeling: A Granular Computing Approach

From the processing point of view, data mining is machine derivation of interesting properties (to human) from the stored data. Hence, the notion of machine oriented data modeling is explored: An attribute value, in a relational model, is a meaningful label (a property) of a set of entities (granule). A model using these granules themselves as attribute values (their bit patterns or lists of members) is called a machine oriented data model. The model provides a good database compaction and data mining environment. For moderate size databases, finding association rules, decision rules, and etc., can be reduced to easy computation of set theoretical operations of granules. In the second part, these notions are extended to real world objects, where the universe is granulated (clustered) into granules by binary relations. Data modeling and mining with such additional semantics are formulated and investigated. In such models, data mining is essentially a machine calculus of granules-granular computing.     

基于MDA的软件建模方法研究

模型驱动架构(MDA)是一个新的软件开发框架,它溶入了软件工程领域的新思想、新方法和新技术。本文研究了基于MDA的软件建模方法,探讨了模型在整个软件生命周期中起的关键作用,提出了一种从平台无关模型(PIM)到平台相关模型(PSM)的转换方法。     

Towards Developing Privacy-Preserved Data Security Approach (PP-DSA) in Cloud Computing Environment

In the present scenario of rapid growth in cloud computing models, several companies and users started to share their data on cloud servers. However, when the model is not completely trusted, the data owners face several security-related problems, such as user privacy breaches, data disclosure, data corruption, and so on, during the process of data outsourcing. For addressing and handling the security-related issues on Cloud, several models were proposed. With that concern, this paper develops a Privacy-Preserved Data Security Approach (PP-DSA) to provide the data security and data integrity for the outsourcing data in Cloud Environment. Privacy preservation is ensured in this work with the Efficient Authentication Technique (EAT) using the Group Signature method that is applied with Third-Party Auditor (TPA). The role of the auditor is to secure the data and guarantee shared data integrity. Additionally, the Cloud Service Provider (CSP) and Data User (DU) can also be the attackers that are to be handled with the EAT. Here, the major objective of the work is to enhance cloud security and thereby, increase Quality of Service (QoS). The results are evaluated based on the model effectiveness, security, and reliability and show that the proposed model provides better results than existing works.