On the Correctness of Goscinski′s Algorithm

In this paper, the correctness of the mutual exclusion algorithm proposed by Goscinski (J. Parallel Distribut. Comput.9(7), 77-82 (1990)), hereafter , is discussed and its features are compared with other token-based algorithms already published. In particular, we show that works correctly only using a communication system that guarantees a total ordering of messages, otherwise it is incorrect. We further give a modified version of , hereafter , and show that is actually a simple modification of the Suzuki-Kasami algorithm (ACM Trans. Comput. Systems3(5), 344-349 (1985)).     

A New Model of Learning

Abstract

Most learning machines are of a single type. Input patterns are transformed into some other patterns by a processor then classified by a discriminant function. Such machines are considered to offer poor information processing capability. This paper proposes a new and unusual model of learning. In conventional machines only the classification of input patterns is learned; however, the new model can memorize the input patterns themselves in the learning, that is, an input pattern appears as an output if it has been seen sufficiently often. Basic to the formulation of the new model are the multiplexing of local processing and the use of connections which operate on a manner similar to presynaptic inhibition.     

Extension of some MAP results to transient MAPs and Markovian binary trees

In this work we extend previous results on moment-based characterization and minimal representation of stationary Markovian arrival processes (MAPs) and rational arrival processes (RAPs) to transient Markovian arrival processes (TMAPs) and Markovian binary trees (MBTs).     

A Nondeterministic Extension over Finitely Recursive Process Model

This paper extends the Finitely Recursive Process framework introduced by Inan and Varaiya for modelling Discrete Event Systems to encompass nondeterministic processes. Nondeterminism has been captured as a set of possible deterministic futures instead of using the standard failure model of Communicating Sequential Processes. In the beginning a general structure of finitely recursive process space is provided with some important modifications. Next, the nondeterministic process space has been introduced as a special case of the general algebraic process space. A collection of operators has been defined over this nondeterministic process space that enables its characterisation in a finitely recursive manner. Finally, the advantages and disadvantages of the proposed model vis-a-vis other nondeterministic models of discrete event systems are discussed.     

KFUR:一个新型内核扩展安全模型

保障内核扩展的安全性对操作系统具有重要意义.当前存在大量针对内核函数使用规则的攻击,内核扩展中也存在大量违反内核函数使用规则的错误,因此针对内核函数使用规则的安全性检测十分必要.虽然存在多种提高内核扩展安全性的方法,但很少有方法对内核函数的使用规则进行安全性检测.文中设计了KFUR( Kernel Function Usage Rule)内核扩展安全模型系统,用于在运行时检测内核扩展调用内核函数是否遵守内核函数使用规则.如果内核扩展调用内核函数满足模型安全运行条件,则允许对该内核函数进行调用,否则将错误报告给操作系统内核并终止该内核扩展的运行.文中所述研究在Iinux操作系统上对KFUR安全模型系统进行实现,并将其运用于e1000网卡驱动、SATA硬盘驱动和HDA声卡驱动内核扩展.安全性评测表明安全模型系统能够对内核函数使用规则进行安全性检测,性能评测表明安全模型系统带来的开销很小.     

个性化的自主学习模型研究

自主学习系统是现代远程教育系统的重要内容。本文通过分析自主学习的特点,构造个性化的自主学习模型,并对学习材料的组织、推荐模型进行研究。目的是为学习者推荐有针对性的学习材料,提高学习者的学习效率。     

基于XPath数据模型的双时态扩展

XML作为Web上新的数据发布语言,将成为Web下一代"数据表达"和"数据交换"的统一标准.然而XML文档很少是静止的,它经常会被修改,引入"时态表达"后时态XML文档能够记录一系列的修改痕迹,提供了一种高效可行的XML版本管理方案.另一方面XML表达时态信息比关系模型更自然,时态数据库的研究人员开始将部分焦点转向研究如何利用XML实现时态信息处理.提出了一个双时态XML数据模型并给出了形式化定义,该模型将一个双时态XML文档建模成一个带有"双时态元素"边标记的有向树形图.还讨论了该模型映射到双时态XML文档的两种映射方法;并采用扩展DOM API的方法实现了模型以及映射.最后通过实验对比了扩展DOM实现模型和不扩展DOM实现模型两种实现方法,两种模型映射到双时态XML文档的映射方法.     

在不完备信息系统中的一种粗集扩充模型

对已有的基于不完备信息系统的粗集拓展模型进行分析比较,进而提出一种改进的限制容差关系模型。主要思想是考虑到人的主观要求,在容差类的划分中加入一个插值,再利用这些类得到上下近似集,最后通过实例说明该模型是符合客观实际的。     

A Neural Model for Context-dependent Sequence Learning

A novel neural network model is described that implements context-dependent learning of complex sequences. The model utilises leaky integrate-and-fire neurons to extract timing information from its input and modifies its weights using a learning rule with synaptic noise. Learning and recall phases are seamlessly integrated so that the network can gradually shift from learning to predicting its input. Experimental results using data from the real-world problem domain demonstrate that the use of context has three important benefits: (a) it prevents catastrophic interference during learning of multiple overlapping sequences, (b) it enables the completion of sequences from missing or noisy patterns, and (c) it provides a mechanism to selectively explore the space of learned sequences during free recall.     

粗糙集的泛系化扩展模型

粗糙集与泛系理论相结合已成为一个新兴的研究领域,基于泛系理论中的泛权场/网等理论,对粗糙集理论的基本概念进行了基本的概括和扩展,将粗糙集理论泛系化扩展加以研究,进而构建了粗糙集的泛系化扩展模型,并通过实例给予解释,为粗糙集的进一步完善和扩展找到了一条新路。     

个性化的远程学习模型

随着互联网应用的发展,人们越来越多地把学习过程放到网络上来,而不再局限于传统的教室学习,因此基于Ｗｅｂ的远程学习得到了迅速推广。但是由于受教育对象之间存在着鲜明的个性特征差异,而目前存在的远程教育系统模式单一,学生只能被动地接受完全相同的学习内容,而不能主动地根据自己的需要选择、调度、控制自己的学习过程,这样,再丰富的教育资源也未能得到充分利用。为了从根本上解决这一矛盾,提高教育信息资源的利用度,使学生在学习过程中变被动为主动,实现因材施教,文章结合个性化服务的思想,提出个性化学习的概念,并系统地叙述了个性化的远程学习模型ＰＤＬＭ的原理和构成,给出了实现ＰＤＬＭ系统的具体思路和解决方案。个性化的远程学习将成为远程教育应用的新模式。     

WRAPLE: The Weighted Repair Assistance Program Learning Extension

The weighted repair assistance program learning extension, or WRAPLE, is a maintenance aid to help technicians isolate faults?a necessary step in system repair. We derived the fault isolation strategies used in WRAPLE from a knowledge-based information-theoretic approach that incorporates fixed experience. This approach contrasts with fault isolation strategies, which are developed from estimates and remain fixed. A fault isolation strategy that ?learned? would adapt to field conditions and change on the basis of actual failures and use of maintenance resources, permitting more efficient maintenance and reducing mean time to repair. In this article, we describe WRAPLE and the learning algorithms by which it modifies its output. We also present an example of a typical system to illustrate the technique.     

A structured filter for Markovian switching systems

In this work, a new methodology for the structuring of multiple model estimation schemas is developed. The proposed filter is applied to the estimation and detection of active mode in dynamic systems. The discrete-time Markovian switching systems represented by several linear models, associated with a particular operating mode, are studied. Therefore, the main idea of this work is the subdivision of the models set to some subsets in order to improve the detection and estimation performances. Each subset is associated with sub-estimators based on models of the subset. In order to compute the global estimate and subset probabilities, a global estimator is proposed. Theoretical developments based on a hierarchical decision, leading to more efficiency in detection and state estimation, are proposed. Naturally, these results can be used for fault detection and isolation, using the activation probabilities of operating modes. These results are applied to detect switches in the centre of gravity for vehicle roll dynamics.     

物元模型可拓控制的控制算法实现

可拓控制是最近几年发展起来的一种智能控制技术。本文在主要基于可拓集合理论发展起来的控制器模型基础上进行了扩充完善,主要是引入物元来表示控制中的各种信息流,并结合物元变换拓展及物元可拓展运算一起来实现控制算法,而使系统可实现多变量控制,同时因引入文献［４］中的可拓专家系统来实现上层控制器而使控制器的智能大大提高,真正具有创造性的辩证思维能力。     

一种MFC编程框架的.NET扩展模型

介绍了．NET框架的一些关键技术改进应用在基于MFC的软件系统开发中的方法，构建了一种新的MFC扩展模型，使得使用．NET技术对基于MFC框架的应用程序进行二次开发成为可能。     

基于扩展规则的模型计数与智能规划方法

提出命题扩展规则方法ER的一种高效实现.在此基础上,研究了扩展规则方法在3个领域的应用:提出一次性求解一系列相近SAT问题的快速算法nER;提出基于扩展规则的模型计数算法     

针对薄层介质的多极漫射模型扩展

多极( Multipole)漫射模型能较准确地模拟半透明介质的次表面散射现象,但并不适用于厚度接近或小于一个平均自由程( MFP)的薄层介质。为此,在Multipole漫射模型的基础上扩展该模型使之适用于薄层介质。根据Multipole漫射模型所依赖的假设条件,针对薄层介质加以修改,将光线分解为直接透射和漫射部分,并调整Multipole放置点光源的方式,使之更符合薄层介质中光传输的特征。使用修改后的光源计算漫射部分,而直接透射部分则采用狄拉克δ分布来近似。对扩展方法和传统Multipole以及蒙特卡罗方法进行分布数值和实际渲染效果的对比,在处理厚度接近或小于一个MFP的薄层介质时,扩展方法的结果均较传统Multipole更吻合蒙特卡罗方法的结果,可直接和现有Multipole模型实现集成。     

基于认知模型的通用多概念学习算法

文章在分析了幼儿认知活动的一般过程基础上,抽象出一个概念学习的认知模型,提出了一个面向通用问题的多概念学习算法。并分析了算法的复杂性,通过实验验证了该算法的优点。最后指出了算法良好的应用前景。