FIRST: a flexible and interactive resampling tool for CFD simulation data

We introduce a flexible, variable resolution tool for interactive resampling of computational fluid dynamics (CFD) simulation data on versatile grids. The tool and coupled algorithm afford users precise control of glyph placement during vector field visualization via six interactive degrees of freedom. Other important characteristics of this method include: (1) an algorithm that resamples any unstructured grid onto any structured grid, (2) handles changes to underlying topology and geometry, (3) handles unstructured grids with holes and discontinuities, (4) does not rely on any pre-processing of the data, and (5) processes large numbers of unstructured grid cells efficiently. We believe this tool to be a valuable asset in the engineer's pursuit of understanding and visualizing the underlying flow field in CFD simulation results.     

联合模糊聚类和拟蒙特卡罗重采样的群目标跟踪算法

联合概率数据关联粒子滤波(joint probabilistic data association-particle filter,JPDA-PF)算法常被用来解决群目标跟踪中的数据关联和非线性滤波问题.针对算法的数据关联时间较长以及样本枯竭问题,本文阐述了一种利用模糊聚类和拟蒙特卡罗重采样的群目标跟踪算法.首先,在群演化网络模型的基础上,采用最大熵模糊聚类法来完成群内个体目标和量测之间的数据关联,利用模糊隶属度来构建互联概率矩阵.其次,在目标状态样本的重采样的过程中,利用随机化拟蒙特卡罗序列映射到拟复制样本的子空间上,提高样本的多样性,抑制样本枯竭的出现.仿真实验结果表明,与JPDA-PF算法相比,本文算法能有效估计群内目标状态和群结构,并具有更优的估计性能.     

基于分布式环境一种动态依赖关系发现的方法

本文提出了一种为分布式应用动态构造依赖性模型的方法。这个方法通过对系统进行主动干扰来获得建模的先验知识,然后基于贝叶斯网络构造方法,对分布式应用的组件间关系建立依赖性模型。和传统的被动建模技术不同的是,这种主动方法不需要事先对系统细节充分了解,它通过在运行环境中部署探针,捕捉和测量与部署的主动干扰相关的系统反馈,通过机器学习的方法识别分布式应用中构件间的动态调用的依赖关系,为分布式应用建立动态运行过程中的依赖性模型。动态建立的依赖性模型可用于分布式应用的运行时管理,用于分布式应用执行过程中的故障定位和恢复,对于分布式应用自主计算环境的实现,提供一种实用的方法。     

Mapping solar ultraviolet radiation from satellite data in a tropical environment

This paper presents a technique for mapping erythemally-weighted solar ultraviolet (EUV) radiation from satellite data in a tropical environment. A satellite-based EUV radiation model was formulated for calculating the EUV daily dose from satellite-derived earth-atmospheric albedo, total column ozone and other ground-based ancillary data. The earth-atmospheric albedo was obtained from a geostationary satellite (GMS5) while the total column ozone was retrieved from a polar orbiting satellite (EP/TOMS). The model was validated against the monthly average EUV daily dose from the measurements at four solar radiation monitoring stations located in the tropical environment of Thailand. The monthly average EUV daily dose calculated from the model was in reasonable agreement with that obtained from the measurement, with root mean square difference (RMSD) and mean bias difference (MBD) of 12.3% and 0.7%, respectively. After the validation, the model was used to calculate the monthly average EUV daily dose over Thailand employing an 8-year period of data from GMS5, EP/TOMS and other ancillary surface data. Values of the monthly average of EUV daily dose were presented as monthly and yearly maps. These maps reveal that the tropical monsoons have a strong influence on the EUV in this region.     

Development of a model for the estimation of photosynthetically active radiation from geostationary satellite data in a tropical environment

This paper presents a model for the estimation of photosynthetically active radiation (PAR) from geostationary satellite data. The model is aimed to estimate the monthly average hourly PAR in a tropical environment. This model represents a physical relation of PAR incident on the earth's surface and satellite-derived earth-atmospheric albedo together with the absorption and scattering coefficients of various atmospheric constituents. The earth-atmospheric albedo was obtained from the Multifunctional Transport Satellite-1R (MTSAT-1R). The absorption of PAR by water vapor, an important process for the tropics, was computed from the ambient temperature and relative humidity. The absorption of PAR by aerosols was estimated by using the visibility data and aerosol optical properties obtained from the Aerosol Robotic Network (AERONET) of NASA in this region. The total column ozone from the Ozone Monitoring Instrument onboard of AURA satellite (OMI/AURA) was used for the estimation of the absorption of PAR by ozone. The model was validated against the monthly average hourly PAR from measurements at four solar radiation measuring stations situated in the tropical environment of Thailand. The values of the monthly average hourly PAR estimated from the model and those obtained from the measurement were in good agreement, with the root mean square error (RMSE) and mean bias error (MBE) of 9.8% and 0.6%, respectively. After the validation, the model was employed to estimate the monthly average hourly PAR over Thailand using a 4-year period of data from MTSAT-1R and other ancillary surface data. Values of the monthly average hourly PAR were presented as maps showing the geographical distribution of PAR. These maps reveal the diurnal and seasonal variation of PAR over the country.     

Numerical methods of higher order of accuracy for incompressible flows

The work deals with numerical solution of the Navier–Stokes equations for incompressible fluid using finite volume and finite difference methods. The first method is based on artificial compressibility where continuity equation is changed by adding pressure time derivative. The second method is based on solving momentum equations and the Poisson equation for pressure instead of continuity equation. The numerical solution using both methods is compared for backward facing step flows. The equations are discretized on orthogonal grids with second, fourth and sixth orders of accuracy as well as third order accurate upwind approximation for convective terms. Not only laminar but also turbulent regimes using two-equation turbulence models are presented.     

Asymptotic analysis of a heterogeneous multiprocessor system in arandomly changing environment

An asymptotic queuing theoretic approach is proposed to analyze the performance of an FCFS (first-come, first-served) heterogeneous multiprocessor computer system with a single bus operating in a randomly changing environment. All stochastic times in the system are considered to be exponentially distributed and independent of the random environment, while the access and service rates of the processors are subject to random fluctuations. It is shown under the assumption of `fast' arrivals that the busy period length of the bus converges weakly, under appropriate normalization, to an exponentially distributed random variable. As a consequence, main steady-state performance measures such as system throughput, mean delay time, expected waiting time, and mean number of active processors can be approximately determined. The reliability of the proposed method is validated by comparing the new approximations with known exact results     

P2P多数据库系统中的事务并发控制研究

P2P多数据库系统是一种分布式的数据库系统,它是P2P计算系统和传统数据库系统的结合体.P2P计算系统的动态性使得P2P多数据库系统的事务管理缺少全局控制机制.在事务传播过程不存在循环的前提下,文章提出了一种保证P2P多数据库系统中事务全局串行化的并发控制协议,它通过控制一系列全局事务传播过程中每个熟识关系的两级串行化来实现.     

云计算环境下基于数据关联度的海洋监测大数据布局策略

海洋监测数据是具有强数据关联的大数据,如何高效地进行数据布局,是制约其有效管理和应用的关键问题之一。在云计算环境下,针对海洋监测大数据的特点,提出了一种基于数据关联度的海洋监测大数据布局策略。在保证数据中心存储均衡的情况下,综合考虑了监测任务、监测点和监测数据之间的关联,建立了海洋监测点间的关联度、监测数据间的关联度和监测数据全局关联度,从三个角度对海洋监测大数据进行布局,使得同一数据中心内的数据具有较高的关联度。通过实验分析,该方法降低了用户访问海洋监测大数据的响应时间,为海洋监测大数据提供了一种有效的布局策略。     

CP methods of higher order for Sturm-Liouville and Schrödinger equations

The algorithm upon which the code SLCPM12, described in Computer Physics Communications 118 (1999) 259-277, is based, is extended to higher order. The implementation of the original algorithm, which was of order {12,10} (meaning order 12 at low energies and order 10 at high energies), was more efficient than the well-established codes SL02F, SLEDGE and SLEIGN. In the new algorithm the orders {14,12}, {16,14} and {18,16} are introduced. Besides regular Sturm-Liouville and one-dimensional Schrödinger problems also radial Schrödinger equations are considered with potentials of the form V(r)=S(r)/r+R(r), where S(r) and R(r) are well behaved functions which tend to some (not necessarily equal) constants when r→0 and r→∞. Numerical illustrations are given showing the accuracy, the robustness and the CPU-time gain of the proposed algorithms.     

Simple resampling methods of approximating the distribution of LAD estimators for doubly censored regression models

Recently, least absolute deviation (LAD) estimator for median regression models with doubly censored data was proposed and the asymptotic normality of the estimator was established, and the methods based on bootstrap and random weighting were proposed respectively to approximate the distribution of the LAD estimators. But the calculation of the estimators requires solving a non-convex and non-smooth minimization problem, resulting in high computational costs in implementing the bootstrap or random weighting method directly. In this paper, computationally simple resampling methods are proposed to approximate the distribution of the doubly censored LAD estimators. The objective functions in the resampling stage of the new methods are piece-wise linear and convex, and their minimizer can be obtained by the linear programming in the same way as that for the case of uncensored median regression.     

汉语学习者依存句法树库构建

汉语学习者依存句法树库为非母语者语料提供依存句法分析,对第二语言教学与研究,以及面向第二语言的句法分析、语法改错等相关研究有重要意义.然而,现有的汉语学习者依存句法树库数量较少,且在标注方面仍存在一些问题.为此,该文提出一个依存句法标注规范,搭建在线标注平台,并开展汉语学习者依存句法标注.该文重点介绍了数据选取、标注流...     

并发试验环境下的设备排程研究

为了解决并发多产品试验下多设备申请冲突和闲置问题,提高设备利用率和试验效率,提出了一种基于并发多产品试验约束的试验设备排程模型。分析了产品试验可分阶段离散实施特性,着重分析了并发试验的各约束条件及可遵循的规则。通过分析试验中多试验设备并发使用特性和试验设备使用约束,给出了试验设备选择规则及多设备公有时间搜索规则。在此基础上以多试验完成时间最短和设备利用率最高为优化目标建立了试验设备排程模型。应用结果表明了该模型的有效性。     

网络环境下信息系统数据共享并发的控制方法

本文结合笔者开发管理信息系统的实践,探讨了网络环境下信息系统数据共享并发的控制方法,并给出了一个在Novell网络环境下,用FoxPro编写的处理网络数据共享并发的实用程序。     

依赖注入在Spring和EJB3.0中的应用与实现

讨论了POJO即纯JAVA简单旧式对象与持久对象即PO的区别和联系,详细介绍了在Spring和EJB3.0框架中依赖注入方式的实现机制,并由此比较了两种框架开发应用程序的过程中应用服务的方式及其复杂性。     

基于Retri网的市场需求预测模型研究

市场需求预测问题具有多因素、离散、动态和并发等特点，尤其是并发性始终影响预测结果的稳定性和精度。本文利用Ｐｅｔｒｉ网良好的离散事件动态表达和计算能力以及图形表示的直观性，建立市场需求预测问题的Ｐｅｔｒｉ网模型，并对模型的动态、并发问题进行分析     

Implementation of two projection methods on a shared memory multiprocessor: DEC VAX 6240

In this paper, we compare the relative performance of two iterative schemes, based on projection techniques, on a shared memory multiprocessor - VAX 6240. We consider the CG accelerated Block-SSOR method and the CG accelerated Symmetric-Kaczmarz method for the solution of large non-symmetric systems of linear equations. We show that the regular structure of many matrices can be exploited by the CG-accelerated Block-SSOR method to provide good speedup in a multiprocessing environment. However, the CG accelerated Symmetric-Kaczmarz method, while being a viable alternative on a scalar machine, is unable to benefit from multiprocessing.     

探析大数据在高职教育中的个性化应用

伴随着移动宽带、物联网、云计算的迅猛发展以及越来越多的移动终端、传感设备接入网络,现代社会正在以不可想象的速度产生海量数据,对传统教育模式产生广泛而深刻的影响。在数据量庞大、种类繁多、信息多样化的大数据背景下,高职院校教学服务和数据利用方式将发生显著变化,并因此带来新的机会.大数据技术的应用,使得高校可以对其数据资源采取完全数据筛选的方式来分析、挖掘隐藏在数据背后的规律,获得具有洞察力和新价值的东西依靠数据作出科学决策,让高校的信息化建设成果为办学所用,为素质教育所用。     

分布式环境下大规模资源描述框架数据划分方法综述

随着知识图谱的日益发展和在各个垂直领域的广泛应用,对于资源描述框架（RDF）数据的高效处理需求日益成为现代大数据管理领域中的新课题。RDF是W3C提出的用于描述知识图谱实体以及实体间关系的数据模型。为了有效地应对大规模RDF数据的存储和查询,很多学者考虑在分布式环境中管理RDF数据。RDF数据的分布式存储所面临的关键问题是数据的划分,而划分的结果很大程度上决定了SPARQL的查询性能。从数据划分的角度,主要围绕两类：基于图结构的RDF数据划分方法和基于语义的RDF数据划分方法展开深入阐述。前者包括多粒度层次划分、模板划分和聚类划分,适用于通用领域查询的语义范畴较为宽泛的场景;后者包括哈希划分、垂直划分和模式划分,更加适用于垂直领域查询的语义范畴相对固定的环境。此外,针对几种典型的划分方法进行对比与分析,为未来RDF数据划分方法的研究提供参考。最后,对未来RDF数据划分方法的发展方向进行了归纳总结。