INCREMENTAL AUGMENT ALGORITHM BASED ON REDUCED Q-MATRIX

Reduced Q-matrix （Qr matrix） plays an important role in the rule space model （RSM） and the attribute hierarchy method （AHM）. Based on the attribute hierarchy, a valid/invalid item is defined. The judgment method of the valid/invalid item is developed on the relation between reachability matrix and valid items. And valid items are explained from the perspective of graph theory. An incremental augment algorithm for constructing Qr matrix is proposed based on the idea of incremental forward regression, and its validity is theoretically considered. Results of empirical tests are given in order to compare the performance of the incremental augment algo-rithm and the Tatsuoka algorithm upon the running time. Empirical evidence shows that the algorithm outper-forms the Tatsuoka algorithm, and the analysis of the two algorithms also show linear growth with respect to the number of valid items. Mathematical models with 10 attributes are built for the two algorithms by the linear regression analysis.

A Comparison between the Reduced Differential Transform Method and Perturbation-Iteration Algorithm for Solving Two-Dimensional Unsteady Incompressible Navier-Stokes Equations

In this work, approximate analytical solutions to the lid-driven square cavity flow problem, which satisfied two-dimensional unsteady incompressible Navier-Stokes equations, are presented using the kinetically reduced local Navier-Stokes equations. Reduced differential transform method and perturbation-iteration algorithm are applied to solve this problem. The convergence analysis was discussed for both methods. The numerical results of both methods are given at some Reynolds numbers and low Mach numbers, and compared with results of earlier studies in the review of the literatures. These two methods are easy and fast to implement, and the results are close to each other and other numerical results, so it can be said that these methods are useful in finding approximate analytical solutions to the unsteady incompressible flow problems at low Mach numbers.

OPTIMIZATION OF A REDUCED CHEMICAL KINETIC MODEL FOR HCCI ENGINE SIMULATIONS BY MICRO-GENETIC ALGORITHM

A reduced chemical kinetic model （44 species and 72 reactions） for the homogeneous charge compression ignition （HCCI） combustion of n-heptane was optimized to improve its autoignition predictions under different engine operating conditions. The seven kinetic parameters of the optimized model were determined by using the combination of a micro-genetic algorithm optimization methodology and the SENKIN program of CHEMKIN chemical kinetics software package. The optimization was performed within the range of equivalence ratios 0.2-1.2, initial temperature 310- 375 K and initial pressure 0, 1-0.3 MPa, The engine simulations show that the optimized model agrees better with the detailed chemical kinetic model （544 species and 2 446 reactions） than the original model does.

A New Scheduling Algorithm for Reducing Data Aggregation Latency in Wireless Sensor Networks

Existing works on data aggregation in wireless sensor networks (WSNs) usually use a single channel which results in a long latency due to high interference, especially in high-density networks. Therefore, data aggre- gation is a fundamental yet time-consuming task in WSNs. We present an improved algorithm to reduce data aggregation latency. Our algorithm has a latency bound of 16R + Δ – 11, where Δ is the maximum degree and R is the network radius. We prove that our algorithm has smaller latency than the algorithm in [1]. The simulation results show that our algorithm has much better performance in practice than previous works.

MR-CLOPE: A Map Reduce based transactional clustering algorithm for DNS query log analysis

DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.

Reduced Detailed Mechanism for Methane Combustion

Simulated results from a detailed elementary reaction mechanism for methane-containing species in flames consisting of nitrogen (NOx), C1 or C2 fuels are presented, and compared with reduced mechanism;this mechanism have been constructed with the analysis of the rate sensitivity matrix f (PCAF method), and the computational singular perturbation (CSP). The analysis was performed on solutions of unstrained adiabatic premixed flames with detailed chemical kinetics described by GRI 3.0 for methane including NOx formation. A 9-step reduced mechanism for methane has been constructed which reproduces accurately laminar burning velocities, flame temperatures and mass fraction distributions of major species for the whole flammability range. Many steady-state species are also predicted satisfactorily. This mechanism is especially for lean flames. This mechanism is accurate for a wide range of the equivalence ratio (1, 0.9, 0.8, and 0.7) and for pressures as high as 40 atm to 60 atm. For both fuels, the CSP algorithm automatically pointed to the same steady-state species as those identified by laborious analysis or intuition in the literature and the global reactions were similar to well established previous methane-reduced mechanisms. This implies that the method is very well suited for the study of complex mechanisms for heavy hydrocarbon combustion.

Adaptive Reduced Basis Methods Applied to Structural Dynamic Analysis

The reduced basis methods (RBM) have been demonstrated as a promising numerical technique for statics problems and are extended to structural dynamic problems in this paper. Direct step-by-step integration and mode superposition are the most widely used methods in the field of the finite element analysis of structural dynamic response and solid mechanics. Herein these two methods are both transformed into reduced forms according to the proposed reduced basis methods. To generate a reduced surrogate model with small size, a greedy algorithm is suggested to construct sample set and reduced basis space adaptively in a prescribed training parameter space. For mode superposition method, the reduced basis space comprises the truncated eigenvectors from generalized eigenvalue problem associated with selected sample parameters. The reduced generalized eigenvalue problem is obtained by the projection of original generalized eigenvalue problem onto the reduced basis space. In the situation of direct integration, the solutions of the original increment formulation corresponding to the sample set are extracted to construct the reduced basis space. The reduced increment formulation is formed by the same method as mode superposition method. Numerical example is given in Section 5 to validate the efficiency of the presented reduced basis methods for structural dynamic problems.

Stable Model Order Reduction Method for Fractional-Order Systems Based on Unsymmetric Lanczos Algorithm

This study explores a stable model order reduction method for fractional-order systems. Using the unsymmetric Lanczos algorithm, the reduced order system with a certain number of matched moments is generated. To obtain a stable reduced order system, the stable model order reduction procedure is discussed. By the revised operation on the tridiagonal matrix produced by the unsymmetric Lanczos algorithm, we propose a reduced order modeling method for a fractional-order system to achieve a satisfactory fitting effect with the original system by the matched moments in the frequency domain. Besides, the bound function of the order reduction error is offered. Two numerical examples are presented to illustrate the effectiveness of the proposed method.

Automatic differentiation for reduced sequential quadratic programming

In order to slove the large-scale nonlinear programming （NLP） problems efficiently, an efficient optimization algorithm based on reduced sequential quadratic programming （rSQP） and automatic differentiation （AD） is presented in this paper. With the characteristics of sparseness, relatively low degrees of freedom and equality constraints utilized, the nonlinear programming problem is solved by improved rSQP solver. In the solving process, AD technology is used to obtain accurate gradient information. The numerical results show that the combined algorithm, which is suitable for large-scale process optimization problems, can calculate more efficiently than rSQP itself.

CH_(4)/NO_(x) Reduced Mechanisms Used for Modeling Premixed Combustion

This study has identify useful reduced mechanisms that can be used in computational fluid dynamics (CFD) simulation of the flow field, combustion and emissions of gas turbine engine combustors. Reduced mechanisms lessen computational cost and possess the ability to accurately predict the overall flame structure, including gas temperature and species as CH4, CO and NOx. The S-STEP algorithm which based on computational singular perturbation method (CSP) is performed for reduced the detailed mechanism GRI-3.0. This algorithm required as input: the detailed mechanism, a numerical solution of the problem and the desired number of steps in the reduced mechanism. In this work, we present a 10-Step reduced mechanism obtained through S-STEP algorithm. The rate of each reaction in the reduced mechanism depends on all species, steady-state and non-steady state. The former are calculated from the solution of a system of steady-state algebraic relations with the point relaxation algorithm. Based on premixed code calculations, The numeric results which were obtained for 1 atm ≤ Pressure ≤ 30 atm and 1.4 ≤ ф ≤ 0.6 on the basis of the ten steps global mechanism, were compared with those computed on the basis of the detailed mechanism GRI-3.0. The 10-step reduced mechanism predicts with accuracy the similar results obtained by the full GRI-3.0 mechanism for both NOx and CH4 chemistry.

Hadoop平台中一种Reduce负载均衡贪心算法

MapReduce是目前广泛应用的并行计算框架,是Hadoop平台的重要组成部分。主要包括Map和Reduce函数,Map函数输出key-value键值对作为Reduce的输入。由于输入的动态性,不同主机上的Reduce处理的输入量存在不均衡性。如何解决Reduce的负载均衡是优化MapReduce的一个重要研究方向。对整体数据进行抽样,通过适量的样本分析数据,达到较小的代价获得可靠的key分布,提出贪心算法代替Hadoop平台默认的Hash算法来划分数据,实现Reduce负载均衡。提出的贪心算法主要思想是根据抽样数据,求取所有key频次的和对于Reduce节点数量的平均值,然后依次为每一个Reduce分配一个接近平均值的负载,从而达到整体的负载均衡。模拟实验表明,所提算法与默认的hash分区算法相比,运行时间节约10.6%,达到更好的负载均衡。

异构环境下自适应的Map-Reduce调度

本文针对Hadoop自带的Map-Reduce调度器和LATE调度器的不足,提出了自适应的Map-Reduce调度方式。通过历史信息动态调整Map和Reduce任务各阶段的时间比例,以找出真正需要启动备份任务的任务。并将节点进一步分类,在快速节点上启动落后任务的备份任务,从而减小响应时间,并尽量避免节点空载,以免浪费系统资源。最后通过具体实验验证了自适应的Map-Reduce调度的有效性,从历史信息中学习的方式在实验中能减少15%左右的响应时间,综合各种技术,能够减少25%左右的响应时间。

基于Map-Reduce模型的云资源调度方法研究

为提高Map-Reduce模型资源调度问题的求解效能,分别考虑Map和Reduce阶段的调度过程,建立带服务质量(QoS)约束的多目标资源调度模型,并提出用于模型求解的混沌多目标粒子群算法。算法采用信息熵理论来维护非支配解集,以保持解的多样性和分布均匀性;在利用Sigma方法实现快速收敛的基础上,引入混沌扰动机制,以提高种群多样性和算法全局寻优能力,避免算法陷入局部最优。实验表明,算法求解所需的迭代次数少,得到的非支配解分布均匀。Map-Reduce资源调度问题的求解过程中,在收敛性和解集的多样性方面,所提算法均明显优于传统多目标粒子群算法。

基于Map/Reduce的朴素贝叶斯数据分类算法研究

针对目前机器学习研究领域中的数据分类问题,选择朴素贝叶斯算法作为研究对象。首先,通过对样本数据特征属性的权重进行加权调整,提高算法处理的准确率;然后,改进朴素贝叶斯算法分类器模型,使其能够利用Map/Reduce并行编程模型,采用多计算资源节点并行处理,进一步提高处理速度。最后,对UCI dataset数据库进行实验验证,结果表明:改进后的算法在海量数据分类处理中具有更好的性能表现。

基于关联规则的网络信息内容安全事件发现及其Map-Reduce实现

针对网络中信息内容安全事件的发现问题,该文提出一种基于关联规则的多维度用户行为特征关联分析法;对于存在的虚警问题,提出了基于邦弗朗尼校正的检验准则;为满足在海量数据中的应用需求,提出了一种Map-Reduce框架下的分布式幂集Apriori算法。实验结果表明,该文提出的方法及相应算法,并行运算能力强,在低虚警率和漏检率的情况下,具有较好的检测率,且运行时间短,收敛速度快。

基于MapReduce的Web日志挖掘

针对单一CPU节点的Web数据挖掘系统在挖掘Web海量数据源时存在的计算瓶颈问题,利用云计算的分布式处理和虚拟化技术优势以及蚁群算法并行性的优点,设计一种基于Map/Reduce架构的Web日志挖掘算法。为进一步验证该算法的高效性,通过搭建Hadoop平台,利用该算法挖掘Web日志中用户的偏爱访问路径。实验结果表明,充分利用了集群系统的分布式计算能力处理大量的Web日志文件,可以大大地提高Web数据挖掘的效率。

基于Map Reduce的Bagging贝叶斯文本分类

集中式系统框架难以进行海量文本数据分类。为此,提出一种基于Map Reduce的Bagging贝叶斯文本分类算法。介绍朴素贝叶斯文本分类算法,将其与Bagging算法结合,运用Map Reduce并行编程模型,在Hadoop平台上实现算法。实验结果表明,该算法分类准确率较高,运行时间较短,适用于大规模文本数据集的分类学习。

基于Map Reduce的序列模式挖掘算法

传统数据挖掘算法在处理海量数据集时计算能力有限。为解决该问题,提出一种基于Map Reduce的分布式序列模式挖掘算法MR PrefixSpan。在PrefixSpan算法的基础上,对模式挖掘任务进行分割,利用Map函数处理由不同前缀得到的序列模式,并行构造投影数据库,从而提高挖掘效率及简化搜索空间。采用Reduce函数对中间结果进行规约,得到全局序列模式。在Hadoop集群上的实验结果表明,MR PrefixSpan能减少数据库扫描时间,具有较高的并行加速比和较好的可扩展性。

一种基于MapReduce的短时交通流预测方法

非参数回归方法是短时交通流预测常用的方法,但现有非参数回归方法存在预测速度与精度之间的矛盾。为此,提出一种适用于海量历史数据、基于Map Reduce与遗传算法的非参数回归短时交通流预测方法。通过引入Map Reduce并行计算框架,加快K最近邻算法的搜索速度。在数据预处理阶段利用遗传算法优化关键参数的设置,并采用Map Reduce加速参数优化过程,以解决遗传算法迭代运算时间长的问题。实验结果表明,该方法在保证交通流预测精度的前提下,明显提高了预测速度,并且具有较好的可伸缩性。

云环境下基于MapReduce的用户聚类研究与实现

基于大数据背景下海量数据人们无法理解,聚类效率低下等问题,采用MapReduce编程模型将Canopy聚类算法和K-means聚类算法在云环境中相结合,使之能够充分利用Hadoop集群的计算和存储能力。以淘宝网上海量的购买用户聚类作为应用背景,通过使用Hadoop平台的数据挖掘组件Mahout对用户聚类进行了实例研究,并给出了使用Mahout进行挖掘的一般步骤。结果表明,基于MapReduce的聚类算法在大规模数据集上具有较好的聚类质量和运行速度。