基于树结构的分布式数据流水分发方法

针对网络带宽给数据分发带来的限制以及传统数据分发方法的不足,本文提出了一种基于树结构的分布式数据流水分发方法。该方法利用接收端的转发能力,采用流水式传输方式进行数据传输,以提高数据分发的整体效率。算法分析和实验数据表明,本文的方法是有效的。     

利用虚拟仪器构建的注水机组故障预测系统

大型注水机组故障预测系统由于系统复杂,采用传统的开发方式,开发周期长并且硬件系统庞大。虚拟仪器可以通过软件把计算机强大的计算处理能力和仪器硬件的测量、控制能力有机的结合起来。利用PC-DAQ与LabWindows/CVI构建大型旋转机组故障分析系统,减少了系统所需的硬件资源,加快了系统软件的开发速度,实现集成运行环境,使操作人员操作起来更加方便。     

小电流接地系统的新判据在检测工程应用分析

针对当前在小电流接地系统中,尤其是经消弧线圈接地的系统中,所装设的接地检测(选线)装置正确动作率不高的问题,根据系统的特点,对小电流接地系统的选线设备原理、采样误差等方面问题进行剖析,提出了减小综合测量误差,提高选线准确性、可靠性的措施。     

热轧加热炉DCS故障诊断专家系统的研究

热轧加热炉集散控制系统(DCS)从检测单元到执行机构的各个单元之间存在着错综复杂的关系,其肿大量的是因果关系。本文针对其中的因果关系,结合故障树分析法便于对事件进行分析、易于转化为专家系统中的规则知识、规则推理具有直观性和速度快的优点,从而建立故障树。然后以故障树来获取和分析知识,以产生式规则建立知识库,以规则匹配来进行推理,从而建立起步进式热轧加热炉DCS故障诊断专家系统。此方案充分利用了故障树和专家系统在故障诊断中的优势。     

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76-0.90 μm wavelength) and band 5 (1.55-1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R² of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation.     

A scalable algorithm for mining maximal frequent sequences using a sample

In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms. Congnan Luo received the B.E. degree in Computer Science from Tsinghua University, Beijing, P.R. China, in 1997, the M.S. degree in Computer Science from the Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China, in 2000, and the Ph.D. degree in Computer Science and Engineering from Wright State University, Dayton, OH, in 2006. Currently he is a technical staff at the Teradata division of NCR in San Diego, CA, and his research interests include data mining, machine learning, and databases. Soon M. Chung received the B.S. degree in Electronic Engineering from Seoul National University, Korea, in 1979, the M.S. degree in Electrical Engineering from Korea Advanced Institute of Science and Technology, Korea, in 1981, and the Ph.D. degree in Computer Engineering from Syracuse University, Syracuse, New York, in 1990. He is currently a Professor in the Department of Computer Science and Engineering at Wright State University, Dayton, OH. His research interests include database, data mining, Grid computing, text mining, XML, and parallel and distributed processing.     

An Online Algorithm for the Dynamic Maximal Dense Tree Problem

Abstract. The Online Maximal Dense Tree problem is as follows: given a weighted directed graph and a source node, users issue online requests for connection to the source node. A request can either be accepted or rejected (the admission control decision). If the connection request is accepted, it must be connected to the source or to a node previously connected to the source (the routing decision). The objective is to maximize the total number of connections while keeping the connection density , i.e. the ratio of accepted requests to the weight of the spanning tree, sufficiently high. The primary motivation for the Maximal Dense Tree problem is the Online Capacitated Multicast admission control and routing problem. In the Online Capacitated Multicast problem, we are given a communication network with limited link capacities and a set of signal source nodes. Users generate online requests for connection to the signal sources, and the network administrator has to make the admission control and routing decisions. The goal of the network administrator is to maximize the total number of users connected subject to the network capacity constraints. The Online Maximal Dense Tree problem is also faced by a cable TV operator who wishes to connect as many customers as possible while keeping down the amount of wiring per customer. Informally, the Online Maximal Dense Tree algorithm must ``gamble' on certain geographic areas, connecting nodes which are unprofitable to start with, in the hope that eventually enough requests will arrive in its vicinity to make the investment profitable. In this paper we present a randomized online algorithm for the Maximal Dense Tree problem that guarantees acceptance of a (1- ɛ) factor of the requests accepted by the optimum offline algorithm with the expectation of density being at most polylogarithmically lower than that of the offline algorithm. This yields an online capacitated multicast algorithm whose throughput is only poly-logarithmically lower than that of the optimum offline algorithm. Previous work on multicast routing and maximal dense tree problems either made probabilistic assumptions or resulted in linear performance gaps with the offline algorithm. Attempts to solve the Online Maximal Dense Tree problem have also lead to the development of the first polylogarithmic approximation algorithms for the k -MST and the Prize Collecting Salesman problems [AABV].     

A Randomized Linear-Work EREW PRAM Algorithm to Find a Minimum Spanning Forest

We present a randomized EREW PRAM algorithm to find a minimum spanning forest in a weighted undirected graph. On an n -vertex graph the algorithm runs in o(( log n) ^{1+ ɛ} ) expected time for any ɛ >0 and performs linear expected work. This is the first linear-work, polylog-time algorithm on the EREW PRAM for this problem. This also gives parallel algorithms that perform expected linear work on two general-purpose models of parallel computation—the QSM and the BSP.     

基于VxWorks的系统故障快速恢复设计

针对CDMA通讯系统基站侧的重要单板——基带信号处理单板的系统故障需要在极短时间内进行自恢复的要求,设计了一种基于VxWorks操作系统的故障快速恢复方法,包括操作系统的任务、消息队列、信号量等操作设计,同时对设计前后系统故障恢复情况进行了对比测试、验证。     

Computing all efficient solutions of the biobjective minimum spanning tree problem

A common way of computing all efficient (Pareto optimal) solutions for a biobjective combinatorial optimisation problem is to compute first the extreme efficient solutions and then the remaining, non-extreme solutions. The second phase, the computation of non-extreme solutions, can be based on a “k-best” algorithm for the single-objective version of the problem or on the branch-and-bound method. A k-best algorithm computes the k-best solutions in order of their objective values. We compare the performance of these two approaches applied to the biobjective minimum spanning tree problem. Our extensive computational experiments indicate the overwhelming superiority of the k-best approach. We propose heuristic enhancements to this approach which further improve its performance.