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71.
It becomes challenging in order to represent,discover and exchange location information in a ubiquitous environment due to dynamic movement and interaction between mobile nodes inside.In this paper,a new method is presented in order to make location information context-aware so that organizing the format of location information and maintaining the communication between direct connected nodes in a ubiquitous environment is enabled.The structure of a contextual location information repository and a context information communication protocol is manipulated to implement the proposed features.According to the simulation results in network simulator version2(NS2),the new method has depicted good discovery success and consumed efficient service discovery bandwidth.Other network traffic,i.e.transmission control protocol,(TCP) has been simulated in the scenarios but the new location-aware method has shown its robustness with continuous context discovery process. 相似文献
72.
Business process execution language(BPEL)is a most recognized standard workflow language.However,it is difficult to be used in the ubiquitous system computing environment because it is difficult to describe the context information in the selection of the flow through the branch.To solve this problem,we propose a new BPEL workflow system with context-awareness by using aspect-oriented programming(AOP).This system is composed of a BPEL system module and a weaving module using AOP for context-aware.The BPEL system module generates a BPEL workflow program.And the weaving module converts a context-aware mark-up language(CAML)document to the aspect-oriented program that is applied to context-aware code without modification of the existing BPEL document.We also define a new document form that is called CAML,which provides a context-aware that is not available in BPEL.The system can generate a context-aware workflow program.It is developed in a way that inserts context information using AOP to provide context-aware services. 相似文献
73.
当前,开发灵活的适应环境变化的上下文感知应用较为复杂。在上下文感知应用开发过程中,很多未知情况无法事先被充分考虑到,实际的开发又需要处理很多意外情形,因此有效地表示和处理上下文尤为重要。谓词检测作为实现上下文感知的重要方法之一,能够有效对上下文信息进行建模,但在实际应用中对于真实物理设备的支持如何在很大程度上仍然是未知的。为了应对以上问题,通过构建一个简单的物理场景,将谓词检测方法应用于真实的机器小车上,支持其在一个既定轨道上完成运行任务。在该场景下对原始的上下文环境进行建模,将环境特性逐步规约成特定的快照谓词和序列谓词,并在案例研究中,对规约化的谓词进行检测,将谓词检测应用于真实的机器小车实验上。实验分析的结果表明,谓词检测的方法能有效检测小车的上下文特性并成功支撑小车完成既定的行走目标。 相似文献
74.
提出一种以Agent技术为基础的多层分布式的虚拟场景模型。先从装配体的视角构建装配信息模型,根据刻画特征对象的各项参数定义装配知识层;装配决策层将零件视为Agent,并定义Agent间传输状态参数的消息模板,同时根据虚拟装配任务的过程特点,建立决策规则;最后,定义用户控制层的运动控制算法,实现对用户的运动引导。实验表明该场景模型可在虚拟场景中有效呈现装配任务,并根据上下文情境准确理解用户意图。 相似文献
75.
Adapting the User Context in Realtime: Tailoring Online Machine Learning Algorithms to Ambient Computing 总被引:1,自引:1,他引:0
Johannes Schmitt Matthias Hollick Christoph Roos Ralf Steinmetz 《Mobile Networks and Applications》2008,13(6):583-598
Ambient systems weave computing and communication aspects into everyday life. To provide self-adaptive services, it is necessary to acquire context information using sensors and to leverage the collected information for reasoning and classification of situations. To enable self-learning systems, we propose to depart from static rule-based decisions and first-order logic to define situations from basic context, but to build on machine-learning techniques. However, existing learning algorithms show substantial weaknesses if applied in highly dynamic environments, where we expect accurate decisions in realtime while the user is in-the-loop to give feedback to the system’s recommendations. To address ambient and pervasive computing environments, we propose the FLORA—multiple classification (FLORA-MC) online learning algorithm. In particular, we enhance the FLORA algorithm to allow for (1) multiple classification and (2) numerical input values, while improving its concept drift handling capabilities; thus, making it an excellent choice for use in the area of ambient computing. The multiple classification allows context-aware systems to differentiate between multiple categories instead of taking binary decisions. Support for numerical input values enables the processing of arbitrary sensor inputs beyond nominal data. To provide the capability of concept drift handling, we propose the use of an advanced window adjustment heuristic, which allows FLORA-MC to continuously adapt to the user’s behavior, even if her/his preferences change abruptly over time. In combination with the inherent characteristics of online learning algorithms, our scheme is very well suited for realtime application in the area of ambient and pervasive computing. We describe the design and implementation of FLORA-MC and evaluate its performance vs. state-of-the-art learning algorithms. We are able to show the superior performance of our algorithm with respect to reaction time and concept drift handling, while maintaining an excellent accuracy. Our implementation is available to the research community as a WEKA module. 相似文献
76.
针对现有的序列化模型对中文隐式情感分析中特征信息提取不准确以及对篇章级的文本信息提取存在的梯度爆炸或者梯度消失的问题,提出了双向长短时神经网络和上下文感知的树形递归神经网络(context-aware tree recurrent neutral network,CA-TRNN)的并行混合模型.该模型分别利用双向循环长... 相似文献
77.
The Anatomy of a Context-Aware Application 总被引:9,自引:0,他引:9
We describe a sensor-driven, or sentient, platform for context-aware computing that enables applications to follow mobile users as they move around a building. The platform is particularly suitable for richly equipped, networked environments. The only item a user is required to carry is a small sensor tag, which identifies them to the system and locates them accurately in three dimensions. The platform builds a dynamic model of the environment using these location sensors and resource information gathered by telemetry software, and presents it in a form suitable for application programmers. Use of the platform is illustrated through a practical example, which allows a user's current working desktop to follow them as they move around the environment. 相似文献
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