基于比较诊断模型的超立方网络诊断算法

一个有效的诊断算法对多处理器系统而言极其重要。在多处理器系统中,识别所有故障节点的能力称为诊断系统的诊断度。在比较模型下,诊断 的执行是通过一个比较器处理器,给与之相邻的一对处理器发送相同的输入信号,并比较两者间的响应状态。为了提高超立方网络的诊断度,提出了一种新型的基于比较模型的超立方故障诊断算法,其利用超立方网络节点连接的特性生成一个拓扑图ES(k;n),最终得出一个3位二进制的诊断症候集,从而确定系统故障节点。该算法的诊断度最优能达到4n,大于传统超立方的诊断度n。     

T可诊断系统的测试图生生成算法

本文研究了系统级故障诊断中一步ｔ可诊断系统的测试图生成算法，由这些算法生成有的向图或无向图，均为ｔ－可诊断的，这些测试图为软件仿真研究提供了模型。     

一种高维聚类算法及在洗钱侦测中的应用

从技术的角度看,洗钱侦测问题实际上是一个数据分析问题。本文首先给出了一个可疑交易判定模型,并提出了一个基于超图模型的高维聚类算法,运用该算法从案例库中形成可疑交易模式,最后给出了可疑交易的判定方法。该基于超图的高维聚类算法具有以下特点：1）能处理大数据集;2）能适应高维数据;3）聚类结果是可理解、可解释和可用的。     

蜂窝下含D2D系统基于二部超图的资源分配

针对蜂窝下含D2D系统的资源分配问题,提出一种基于二部超图的资源分配算法。首先,以最大化系统和速率为目标,将该问题建模为一个整数规划问题。为求解该NP-hard问题,相继提出二部超图的概念、二部超图边的感知比较构造法则以及基于二部超图的链路匹配算法。仿真结果表明,与二部图算法相比,所提算法在同等条件下可将系统频谱效率提升40b/s/Hz左右,同时可将系统容量提升0.5倍左右。     

不完备文本信息系统的集对分析聚类算法

考虑到实验数据的大规模性及不完备性等特点,根据集对分析理论,提出一种新超图模型不完备文本系统的聚类算法,即在超图边的权重中引入了集对的同异反联系度和集对的相似联系度并建立了超图模型,最后应用超图分隔法进行聚类.该算法克服了传统聚类算法的缺陷,更有效地降低了文本空间的维数,提高了不完备文本信息系统聚类的精度和速度.最后的实例说明了该算法的可行性和有效性.     

面向时间感知的知识超图链接预测

知识超图是一种使用多元关系表示现实世界的异构图,但无论在通用领域还是垂直领域,现有的知识超图普遍存在不完整的情况.因此,如何通过知识超图中已有的链接推理缺失的链接是一个具有挑战性的问题.目前大多数研究使用基于多元关系的知识表示学习方法完成知识超图的链接预测任务,但这些方法仅从时间未知的超边中学习实体与关系的嵌入向量,没有考虑时间因素对事实动态演变的影响,导致在动态环境中的预测性能较差.首先,根据本文首次提出的时序知识超图定义,提出时序知识超图链接预测模型,同时从实体角色、位置和时序超边的时间戳中学习实体的静态表征和动态表征,以一定比例融合后作为实体嵌入向量用于链接预测任务,实现对超边时序信息的充分利用.同时,从理论上证明模型具有完全表达性和线性空间复杂度.此外,通过上市公司的公开经营数据构建时序知识超图数据集CB67,并在该数据集上进行了大量实验评估.实验结果表明:模型能够在时序知识超图数据集上有效地执行链接预测任务.     

CSCW环境下的CAI软件—一个通用的电子教室系统模型

本文基于ＣＳＣＷ的特性及方法，提出了一个通用的电子教室系统模型的设计方案，探讨异地教学的一种新途径，同时介绍了利用虚拟的Ｍｕｌｔｉｃａｓｔ通道实现通用的群体活动支持系统的方法。     

教学动画制作系统Movie-Maker

Ｍｏｖｉｅ－Ｍａｋｅｒ是一个集成化的适合于结构化图形的教学动画制作工具，它具有较强的功能和通用友好的用户界面，可望为ＣＡＩ的发展作出较大贡献。本文主要阐述该系统的设计思想和总体结构。     

多处理器系统实时调度的可预测性

在多处理器实时系统中,由于调度的不规则性,系统的可预测性判定问题尤为重要。针对多处理器系统中实时任务调度的可预测性问题,给出了不可预测的实时任务集反例,证明了一种可预测的实时任务集合。对于多处理器实时系统中常用的最早截止期零松弛调度算法(earliest deadline zero laxity,EDZL)的可预测性,利用EDZL算法的基本性质,用一种简捷的方法证明了EDZL算法是可预测的。通过仿真系统验证了证明的正确性,该方法可用于多处理器及分布式实时系统的设计和验证。     

教学动画制作系统Movie－Maker

Ｍｏｖｉｅ－Ｍａｋｅｒ是一个集成化的适合于结构化图形的教学动画制作工具，它具有较强的功能和通用友好的用户界面，可望为ＣＡＩ的发展作出较大贡献。本文主要阐述该系统的设计思想和总体结构。     

Conditional diagnosability and strong diagnosability of shuffle-cubes under the comparison model

The growing size of multiprocessor systems increases the vulnerability to component failures. It is crucial to locate and replace faulty processors to maintain the system's high reliability. Processor fault diagnosis is essential to the reliability of a multiprocessor system and the diagnosabilities of many well-known networks (such as hierarchical hypercubes and crossed cubes [S. Zhou, L. Lin and J.-M. Xu, Conditional fault diagnosis of hierarchical hypercubes, Int. J. Comput. Math. 89(16) (2012), pp. 2152–2164 and S. Zhou, The conditional diagnosability of crossed cubes under the comparison model, Int. J. Comput. Math. 87(15) (2010), pp. 3387–3396]) have been investigated in the literature. A system is t-diagnosable if all faulty nodes can be identified without replacement when the number of faults does not exceed t, where t is some positive integer. Furthermore, a system is strongly t-diagnosable if it is t-diagnosable and can achieve (t+1)-diagnosability except for the case where a node's neighbours are all faulty. In addition, conditional diagnosability has been widely accepted as a new measure of diagnosability by assuming that any fault-set cannot contain all neighbours of any node in a multiprocessor system. In this paper, we determine the conditional diagnosability and strong diagnosability of an n-dimensional shuffle-cube SQ_n, a variant of hypercube for multiprocessor systems, under the comparison model. We show that the conditional diagnosability of shuffle-cube SQ_n (n=4k+2 and k≥2) is 3n?9, and SQ_n is strongly n-diagnosable under the comparison model.     

基于比较模型的交换超立方网络的(t,k)诊断度研究

故障诊断问题已经被广泛讨论,许多互连网络的诊断度已被深入研究。(t,k)-诊断为最重要的系统级故障诊断策略之一,在故障节点不大于t的前提条件下,每次迭代均可以识别最少故障节点个数为k。针对如何提高交换超立方网络的诊断度的问题,进行了一个基于比较模型的(t,k)-诊断算法研究,根据连通图的特性对交换超立方网络进行连通分子的划分,并计算交换超立方连通图中连接边与节点间的量化关系,从而证明了交换超立方网络是(t,k)-可诊断的。最终表明,本算法下的诊断度,优于其传统精确诊断s 1。     

An evolutionary algorithm for generalized comparison-based self-diagnosis of multiprocessor systems

In this article, we consider the problem of self-diagnosis of multiprocessor and multicomputer systems under the generalized comparison model. In this approach, a system consists of a collection n independent heterogeneous processors (or units) interconnected via point-to-point communication links, and it is assumed that at most t of these processors are permanently faulty. For the purpose of diagnosis, system tasks are assigned to pairs of processors and the results are compared. The agreements and disagreements among units are the basis for identifying faulty processors. Such a system is said to be t-diagnosable if, given any complete collection of comparison results, the set of faulty processors can be unambiguously identified. We present an efficient fault identification method based on genetic algorithms. Analysis and simulations are provided, first, to evaluate the genetic parameters of the diagnosis algorithm; second, to show the efficiency of the genetic approach. The new strategy is shown to correctly identify the set of faulty processors, making it an attractive and viable addition or alternative to present fault diagnosis techniques.     

Adaptive diagnosis for torus systems under the comparison model

System level diagnosis is an important technique for fault detection and location in multiprocessor computing systems. Adaptive diagnosis, proposed by Nakajima, is one of many practical approach system level diagnostic schemes. As far as we know, the adaptive approach under the MM model has only been discussed in relation to a completely connected system. In this paper, we consider the problem of adaptive fault diagnosis for systems modelled by a cycle and a torus under the MM model. For cycles, we give some useful properties for identifying faulty vertices, show the minimum number of test rounds and provide some efficient test assignments. We also present two adaptive diagnosis algorithms for tori and show the minimum number of tests for these algorithms. Moreover, the two algorithms take linear time both for overall testing and syndrome decoding.     

Graceful degradation in algorithm-based fault tolerantmultiprocessor systems

Algorithm-based fault tolerance (ABFT) is a technique which improves the reliability of a multiprocessor system by providing concurrent error detection and fault location capability to it. It encodes data at the system level and modifies the algorithm to operate on the encoded data in order to expose both transient and permanent faults in any processor. Work done till now in this area takes care of only the fault detection and location part of the problem. However, if spare processors are not available, then after a faulty processor has been located, the work initially assigned to it has to be mapped to some nonfaulty processors in the system in such a way that the fault tolerance capability of the system is still maintained with as small a degradation in performance as possible. In this paper, we propose an integrated deterministic solution to the above problem which combines concurrent error detection and fault location with graceful degradation. There exists no previous deterministic ABFT method for the design of general t-fault locating systems, even for the case of t=1. We propose a general method for designing one-fault locating/s-fault detecting systems. We use an extended model for representing ABFT systems. This model considers the processors computing the checks to be a part of the ABFT system, so that faults in the check computing processors can also be detected and located using a simple diagnosis algorithm, and the checks can be mapped to other nonfaulty processors in the system     

Strong diagnosability of regular networks under the comparison model

Diagnosability has played an important role in the reliability of multiprocessor systems. The strongly t-diagnosable system is (t+1) diagnosable except when all of the neighbors of a node are simultaneously faulty. In this paper, we discuss the in-depth properties of diagnosability for t-regular and t-connected networks under the comparison model. We show that a t-regular and t-connected multiprocessor system with at least 2t+6 nodes, for t?4, is strongly t-diagnosable under the comparison model if the following two conditions hold: (1) the system is triangle free, and (2) there are at most t−2 common neighbors for each pair of distinct nodes in the system.     

The conditional diagnosability of crossed cubes under the comparison model

The growing size of the multiprocessor systems increases their vulnerability to component failures. It is crucial to locate and replace the faulty processors to maintain the system's high reliability. The fault diagnosis is the process of identifying faulty processors in a system through testing. The conditional diagnosis requires that for each processor v in a system, all the processors that are directly connected to v do not fail simultaneously. In this paper, we show that the conditional diagnosability of the crossed cubes CQ _n under the comparison diagnosis model is 3n?5 when n≥7. Hence, the conditional diagnosability of CQ _n is three times larger than its classical diagnosability.     

A fast pessimistic diagnosis algorithm for generalized hypercube multicomputer systems

The reliability of processors is an important issue for designing a massively parallel processing system for which fault-tolerant computing is crucial. In order to achieve high system reliability and availability, a faulty processor (node) when found should be replaced by a fault-free processor. Within a multiprocessor system, the technique of identifying faulty nodes by constructing tests on the nodes and interpreting the test outcomes is known as system-level diagnosis. The topological structure of a multicomputer system can be modeled by a graph of which the vertices and edges correspond to nodes and links of the system, respectively. This work presents a system-level diagnosis algorithm for a generalized hypercube which is an attractive variance of a hypercube. The proposed algorithm is based on the PMC model and can isolate all faulty nodes to within a set which contains at most one fault-free node. If the total number of nodes to be diagnosed in a generalized hypercube is N, the proposed algorithm can run in O(Nlog?N) time, and being superior to Yang??s algorithm proposed in 2004, it can diagnose not only a hypercube but also a generalized hypercube.     

Efficient algorithms for system diagnosis with both processor andcomparator faults

For the comparison-based self-diagnosis of multiprocessor systems, an extended model that considers both processor and comparator faults is presented. It is shown that in this model the system diagnosability is t⩽Zδ/2Z, where δ is the minimum vertex degree of the system graph. However, if the number of faulty comparators is assumed not to exceed the number of faulty processors, the diagnosability of the model reaches t⩽δ. An optimal O(|E|) algorithm, where E is the set of comparators, is given for identifying all faulty processors and comparators, provided that the total number of faulty components does not exceed the system diagnosability, and an O(|E|)² algorithm for the case t⩽δ is also presented. These efficient algorithms determine the faulty processors by calculating each processor's weight, which is mainly defined by the number of adjacent relative tests stating `agreement'. After sorting the processors according to their weights, the algorithms determine all faulty components by separating the sorted processor list