认知无线电政策推理引擎发展概述

基于政策的频谱管理模式能够对认知无线电进行灵活、细化的频谱管理。政策推理引擎是实现该频谱管理模式的关键。本文概括了频谱管理政策的描述方法,总结了近年来政策推理引擎领域的研究进展,并介绍了基于二叉决策图的政策推理引擎的设计原理。     

应用案例推理技术的快速认知引擎

认知引擎必须根据外界无线环境的变化和用户需求,快速自适应调整无线电参数。本文选取一定比例粒子群个体按选定匹配案例的参数配置进行初始化,其余个体随机初始化,这样使粒子群优化算法的粒子在搜索初期就处于靠近最优解的解空间里,同时保持一定的种群多样性,得到一种基于案例推理粒子群优化算法;并由此算法以最大化数据速率、最小化发射功率及最小化误比特率为目标来优化无线电传输参数,得到一种比现有算法收敛速率快和寻优能力强的认知引擎。多载波系统的仿真结果表明了本算法的有效性。      

基于推理引擎的教师模型分析与设计

为了使教学效益达到最佳,首先根据基于本体和规则建立了基于推理引擎的教学策略推理机,引入了辅助规则和匹配规则;并建立教学规则库,根据学生的学习情况对其等级进行动态的调整并相应调整学习内容和教学方法,从而提高学习的效率和自主性.     

新鲜度敏感的上下文推理实时调度算法

由于普适计算中上下文具有时效性,上下文推理器必须支持推理任务的实时调度。针对上下文推理结果在一段时间内仍然保持新鲜的特点,本文提出推理结果重复利用效率及其计算公式。在此基础上提出一种新鲜度敏感的上下文推理实时调度算法FRSA,以推理结果重复利用效率作为判断依据结合任务的deadline进行调度,其目标是在推理器负载较重时达到较高吞吐量。实验表明,在推理器负载重时,FRSA的系统吞吐量比经典调度算法(SJF,EDF,LSF和FCFS)高出10%-30%。     

河南省电信公司电信综合业务数据驱动引擎解决方案

本文介绍了河南省电信公司在电信市场竞争日益加剧的环境中，对提高服务质量降低运营成本的探讨，以及提出的“电信综合业务数据驱动引擎”解决方案     

品质 创新——驱动麦拉菲尔公司竞争力的两个引擎

麦拉菲尔公司的历史可追溯到20世纪初。在早年，公司制造出了电缆绞线设备和成缆设备从而投身电缆机械工业。1941年，随着塑料开始应用于电缆，麦拉菲尔公司研制并生产了第一台直径为80mm、长径比为6的塑料挤出机。1961年麦拉菲尔先生发明了著名的BM螺杆并取得专利，使麦拉菲尔公司在塑料挤出行业确立了威望。     

基于数据驱动的工作流引擎的设计与实现

工作流是计算机化的业务流程.工作流引擎为工作流流程提供运行环境,在流程的执行过程中起着关键性作用.传统的工作流技术在处理复杂多变的流程时,缺乏良好的建模以及适应机制.本文提出一种基于数据驱动的工作流运行方式,实现了活动与活动之间的松散耦合,并以此为基础设计并实现支持这种数据驱动方式的工作流引擎.该引擎在应对复杂多变的业务流程时具有较强的处理能力,并能够实现流程的自动建模.     

NLP和推理引擎下电力基建现场风险区段识别

为识别电力基建现场风险区段,降低电力基建现场风险,提出了基于自然语言处理(Natural Language Processing, NLP)和推理引擎的电力基建现场风险区段识别方法。利用NLP技术深入挖掘电力基建现场报告文本,通过分词技术分析报告内相关内容,采用词频-逆文档频率(Term Frequency-Inverse Document Frequency, TF-IDF)算法统计现场报告内容词频,获取文本报告特征。将高权重特征项输入推理引擎内,利用推理引擎确定匹配度最高的事例,通过模拟退火思想优化推理引擎机制,实现电力基建现场风险区段识别。实验结果表明,所提方法风险识别误差均值约为3.5%,且根据所提方法识别结果进行有针对性优化后,应用对象内各区段风险均有不同程度下降。     

集成了三个独立的LED智能控制引擎的智能灯光驱动芯片

AS3661可控制多达9个LED，为移动设备带来精致的指示灯光效果，可独立于耗电的基带处理器工作。其支持基于命令的简单编程方式。3个独立的程序执行引擎形成了3个独立的可编程LED组。这意味着LED驱动器能根据功能进行分组，     

一种高压LED光引擎的系统设计方案

从技术和市场经济的角度对高压LED光引擎的原理、基本结构、关键技术、应用技术、市场前景作分析论述。     

普适服务综述

随着技术和市场的发展,电信网和互联网正在走向融合,形成一体化网络,提供的服务也将逐步趋同。普适服务融合了各方面对未来服务的需求和期望,成为超3G、下一代网络（NGN）、下二代互联网（NGI）等研究领域的热点问题。普适服务的概念起源于普适计算和超3G领域,其特征包括普适性、移动性、透明性、质量可控性、个性化、自适应性、主动性、安全性、易用性、多样性、快捷性。3GPP、OMA等标准化组织以及欧盟的超3G研究项目都针对普适服务的不同特征进行了研究。当前普适服务的研究热点包括上下文感知、业务使能等技术。     

分布式计算技术概述

分布式计算已经广泛而又深入的参透到各行各业的科技应用中.本文对几种主要的分布式计算技术,如网格、云计算、普适计算等,做了分析和比较,同时对分布式计算目前所面临的问题做了总结.最后对分布式计算技术未来的发展方向做了展望与预测.     

演绎推理和模糊推理在潜艇攻击中的运用研究

介绍两种常见的推理方法——演绎推理和模糊推理,并研究其在潜艇攻击中的运用问题。对两种推理方法进行了对比评述,揭示了二者形式的相似性和本质的根本差异。以潜艇攻击为应用背景,说明两种推理方法在潜艇攻击理论中的重要地位。根据两种推理方法的特点,总结在潜艇攻击中"放心使用"严密演绎推理结论和"审慎使用"模糊推理结论的基本运用原则。     

Centaurus: An Infrastructure for Service Management in Ubiquitous Computing Environments

In the near future, we will see dramatic changes in computing and networking hardware. A large number of devices (e.g., phones, PDAs, even small household appliances) will become computationally enabled. Micro/nano sensors will be widely embedded in most engineered artifacts, from the clothes we wear to the roads we drive on. All of these devices will be (wirelessly) networked using Bluetooth, IEEE 802.15 or IEEE 802.11 for short range connectivity creating pervasive environments. In this age where a large number of wirelessly networked appliances and devices are becoming commonplace, there is a necessity for providing a standard interface to them that is easily accessible by any user. This paper outlines the design of Centaurus, an infrastructure for presenting services to heterogeneous mobile clients in a physical space via some short range wireless links. The infrastructure is communication medium independent; we have implemented the system over Bluetooth, CDPD and Infrared, three well-known wireless technologies. All the components in our model use a language based on Extensible Markup Language (XML) for communication, giving the system a uniform and easily adaptable interface. Centaurus defines a uniform infrastructure for heterogeneous services, both hardware and software, to be made available to diverse mobile users within a confined space.     

Adapting the User Context in Realtime: Tailoring Online Machine Learning Algorithms to Ambient Computing

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.     

一种基于置信规则的模糊推理算法

由于传统模糊推理不考虑前提属性相对于结论属性的重要程度,也不能处理包含分布式结论的规则,偏重解决模糊性而忽略了随机性,造成专家系统性能降低。针对这一局限性,提出了一种基于置信规则的模糊推理算法。该算法以置信规则为知识表达方式,规则前件论域不受连续性限制,它将单个前件匹配度依据其相对重要程度加权求和,并把置信规则的分布式结论按照置信度加权合并,最后通过规则计算得出结论;根据基于置信规则的模糊推理方法对FuzzyCLIPS进行扩展,并在鱼雷规避系统仿真中验证该算法的有效性。     

智能教学系统中的知识推理

知识推理主要应用于智能教学系统的教学策略、教学方法的产生和调整方面。给出了一种智能教学系统中的基于语义网络的模糊推理确定教学序列的方法和用基于产生式规则的推理确定教学方法，并给出了推理算法。     

A joint communication and computing resource management scheme for pervasive grid networks

The last years have been characterized by an increasing interest in the grid and cloud computing that allow the implementation of high performance computing structures in a distributed way by exploiting multiple processing resources. The presence of mobile terminals has extended the paradigm to the so called pervasive grid networks, where multiple heterogeneous devices are interconnected to form a distributed computing resource. In such a scenario, there is the need of efficient techniques for providing reliable wireless connections among network nodes. This paper deals with the proposal of a suitable resource management scheme relying on a routing algorithm able to perform jointly the resource discovery and task scheduling for implementing an efficient pervasive grid infrastructure in a wireless ad hoc scenario. The proposed solutions have been considered within two different parallelization processing schemes, and their effectiveness has been verified by resorting to computer simulations. Copyright © 2011 John Wiley & Sons, Ltd.     

一种基于缺省规则的推理算法

提出了一种将特定缺省规则转换成描述逻辑Abox实例的推理算法,该算法针对特定缺省规则的改变通常不影响Tbox的情况,将缺省规则映射成为Abox中实例的变化,简化了推理过程,同时保持描述逻辑推理的可判定性,具有较好的可行性,并通过推理实例验证了该算法的有效性.