基于聚类和ARMA时间序列的I/O区域预取

预取是提高存储系统性能的主要手段之一.但现有存储系统的设备层并不知道任何I/O访问的语义信息,因而不能充分利用I/O访问的语义来预取下一时刻要访问的数据,只能利用较简单的方式如I/O访问的局部性、顺序访问和循环访问等特性来实现简单的预测.为此,本文根据存储系统的特点提出了实用且高效的基于连续度的聚类算法来发现密集读请求访问的区域,并采用ARMA时间序列模型来预测密集读请求可能访问的区域及访问时刻,为正确的预取提供了准确的信息.为提高预取的准确性,并采用了动态参数估计的策略.通过大量实验的结果验证了这两种算法的正确性和预测的准确性,能较大的提高存储系统的预取效率.     

基于InfiniBand的存储系统目标模拟器

本文设计了一个支持SRP数据旁路加速和多I／O并发处理的存储系统目标模拟器I／O存取模型,通过主机通道GUID标识号与逻辑卷之间的动态映射关系实现存储系统逻辑存储资源的授权访问与屏蔽策略,并进一步提出了通过并发访问阵列设备来增加I／O带宽的方案。测试证明,目标器具有高带宽、低延时、可扩展、卷映射灵活等特点。     

一种改进大型存储系统离散小数据块读取性能的方法

在大型存储系统中，改善离散小数据块读操作的性能已成为提高整个存储系统I／O性能的关键因素。针对这种情况，本文设计并实现了一种系统CBSS（correlative blocks speedup system）。该系统采用一种启发式算法，综合考虑数据访问时间的局部性和全局性，在文件系统和存储设备之间挖掘数据块的相关性，并根据取得的结果进行预取和数据块布局的物理调整，使整个存储系统性能能够平滑地不间断改善。实验结果显示，CBSS能有效改进系统的I／O性能，且不需要改变文件系统和存储设备的数据结构，具有广泛的适应性。     

基于闪存的混合存储系统缓冲区管理算法

由于基于闪存的混合存储系统充分利用了闪存的高速随机读和磁盘的快速顺序写的特性,近年来已经成为了数据库管理系统的二级存储层的高效存储模式,但其I/O访问开销是一个继续提高存储性能的瓶颈.为了降低混合存储系统的I/O访问开销,提出了一种自适应缓冲区管理算法DLSB.该算法根据数据页的逻辑代价和物理代价进行自适应的数据域选择;并在选择的数据域中,比较闪存队列和磁盘队列容量的实际值与理想值来确定数据页的置换,达到了提高I/O访问效率的目的.实验结果表明,该算法有效且可行,显著降低了混合存储系统的I/O访问开销.     

RAID的并行I／O调度算法分析

由于越来越多的应用受限于I／O，存储系统正起着越来越重要的作用，磁盘阵列RAID是一种提供高性能I／O的最常见存储设备，本文分析了RAID并行I／O调度算法的I／O执行时间和磁盘利用率，为合理配置高性能阵列提供了依据。     

一种面向视频播放系统的RAID并行预取技术及实现

随着视频数字化技术的广泛应用，视频播放系统中的存在“瓶颈”也日益突出，在分析视频存储系统磁盘阵列的I／O调度算法和在实际应用中视频数据的特点的基础上，提出一种面向视频媒体服务的RAID并行预取实时调度算法，它利用未来数据的可行预测性，预先将其从磁盘取入缓冲区，同时优化任务调度，使主机数据的回送和从磁盘预取数据同步执行，进一步提高了阵列的I／O吞吐率，用I／Ometer测试结果证明，新算法具有很好的性能。     

基于QoS的数据迁移模型的设计

结合对象存储的特点,提出基于QoS的存储系统模型。该模型将迁移任务划分为细粒度的迁移请求,使对象存储设备在实现数据迁移的同时能响应I／O请求。元数据服务器按相同的标准给I／O请求和迁移请求分配相应的收益,使对象存储设备能采用收益最大算法调度I／O请求和迁移请求,从而提供更高的服务质量。通过收益预测和带宽预留实现在线最大收益调度算法。实验表明,最大收益算法与通常的迁移优先算法和固定平均迁移率算法相比,对系统的I／O性能影响最小。     

一种自适应的基于预测的I／O性能优化方法

目前计算机的I／O性能已经成为严重制约计算机整体性能提升的瓶颈。在现有技术条件下，I／O性能的优化方法对于提升I／O性能显得尤为重要。本文提出了一种自适应的基于预测的I／O性能的优化方法，该方法采用了基于预测的数据预读策略，改进I／O读操作的响应速度；采用基于预测的缓存分配策略，改进了I／O写操作的响应速度，从而在整体上提高了计算机系统的I／O性能。从实验测试的结果可以看出，这种方法适用于多种不同的负载类型，能够大幅度提升计算机的I／O性能．是一种通用的、效果显著的I／O性能优化方法。     

进化存储系统中的物理进化

系统整体结构不能很好地适应系统磁盘的动态变化，这是现有存储系统中普遍存在的一个问题。而我们提出的进化存储系统ERAID（Evolving RAID system）是一个动态存储系统，能够在不停止系统I／O服务的前提下，采用DAA、HDAA、e-HDAA算法分别实现存储系统中同构磁盘的添加、异构磁盘的添加以及异构磁盘的替换,即实现存储系统的物理进化。仿真实验结果表明：在较小的系统开销下，DAA、HDAA、e-HDAA算法能逐渐吸收添加或替换到ERAID系统的（同构或异构）磁盘，使ERAID系统获得优化的存储客量和存储性能。     

并行I/O技术研究

从分析提高I／O性能的途径开始，对在分布主存的高性能计算机中利用存储系统并行性来完成数据访问的并行文件系统所涉及到的问题进行了分析和探讨，最后介绍了几个著名的并行文件系统。     

非线性时间序列预报的隐多分辨ARMA模型

研究一类用于非线性时间序列预报的隐多分辨自回归滑动平均(ARMA)模型,该模型以ARMA模型为初始细水平模型(即隐多分辨模型的基本块)．证明了模型的建模精度由水平问的方差决定．研究了新模型的自相关函数结构,给出了参数估计的Bayes方法和Metropolis-Hasting算法．进一步提出了一种可以直接用于不同基本块的隐多分辨模型的非线性时间序列预报方法,证明了其比其他的线性预报方法和隐多分辨模型预报方法降低了预报误差．最后通过数值模拟和实例验证了模型和预报方法,并和其他模型进行比较,结果表明新提出模型和预报方法能够更好地描述数据的特征,提高预报的精度．     

基于ARMA的销售预测方法与系统实现

为了提高销售预测准确性,为企业生产决策提供参考依据,建立一个基于自回归滑动平均模型ARMA的销售预测模型,实现产品销售预测。采用修正因子对输入序列进行影响因素权值调整（前处理）,再进行ARMA建模,并对预测结果再进行修正（后处理）,提高了销售预测的准确性。以IIS为应用服务器,Oracle为数据库服务器,采用B/S体系结构和ASP.NET四层架构设计,实现时序销量数据修正、模型的识别、定阶、参数估计和预测数据修正以及预测展示等功能,完成产品销量预测系统。     

基于MATLAB的时间序列建模与预测

该文介绍了MATLAB系统辨识工具箱在时间序列方面的应用。首先叙述了动态数据的分析与处理方法,时间序列模型阶次的判定及建模过程,然后给出了利用Matlab系统辨识工具箱对时间序列进行数据预处理,相关分析,ARMA模型参数估计,以及预报的方法和步骤,最后使用该方法对某水厂日供水量进行预测,结果说明该方法具有极强的实践意义。     

ARMA(p,q) type high order fuzzy time series forecast method based on fuzzy logic relations

Within classic time series approaches, a time series model can be studied under 3 groups, namely AR (autoregressive model), MA (moving averages model) and ARMA (autoregressive moving averages model). On the other hand, solutions are based mostly on fuzzy AR time series models in the fuzzy time series literature. However, just a few fuzzy ARMA time series models have proposed until now. Fuzzy AR time series models have been divided into two groups named first order and high order models in the literature, highlighting the impact of model degree on forecast performance. However, model structure has been disregarded in these fuzzy AR models. Therefore, it is necessary to eliminate the model specification error arising from not utilizing of MA variables in the fuzzy time series approaches. For this reason, a new high order fuzzy ARMA(p,q) time series solution algorithm based on fuzzy logic group relations including fuzzy MA variables along with fuzzy AR variables has been proposed in this study. The main purpose of this article is to show that the forecast performance can be significantly improved when the deficiency of not utilizing MA variables. The other aim is also to show that the proposed method is better than the other fuzzy ARMA time series models in the literature from the point of forecast performance. Therefore, the new proposed method has been compared regarding forecast performance against some methods commonly used in literature by applying them on gold prices in Turkey, Istanbul Stock Exchange (IMKB) and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX).     

A two-level hybrid evolutionary algorithm for modelingone-dimensional dynamic systems by higher-order ODE models

This paper presents a new algorithm for modeling one-dimensional (1-D) dynamic systems by higher-order ordinary differential equation (HODE) models instead of the ARMA models as used in traditional time series analysis. A two-level hybrid evolutionary modeling algorithm (THEMA) is used to approach the modeling problem of HODE's for dynamic systems. The main idea of this modeling algorithm is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure of a model (the upper level), while a GA is employed to optimize the parameters of the model (the lower level). In the GA, we use a novel crossover operator based on a nonconvex linear combination of multiple parents which works efficiently and quickly in parameter optimization tasks. Two practical examples of time series are used to demonstrate the THEMA's effectiveness and advantages.     

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

This paper is concerned with parameter estimation of Wiener systems with measurement noises employing correlation analysis method and adaptive Kalman filter. The presented Wiener system consists of two series blocks, that is, a dynamic block represented by auto-regressive moving average (ARMA) model, and static nonlinear block established by neural fuzzy model. Aim at estimating separately the two blocks, the separable signals are introduced. First, applying the separable signals to decouple the identification of linear dynamic block from that of static nonlinear block, then ARMA model parameters are estimated employing correlation function-based least squares principle. Moreover, aiming at handle with error caused by colored measurement noise, adaptive Kalman filter technique and cluster method are introduced to estimate parameter of the nonlinear block and noises model, enhancing parameter estimation precision. The accuracy and applicability of estimated scheme presented are verified through numerical simulation and nonlinear process, the results demonstrate that it is feasible for estimating the Wiener systems in the presence of colored measurement noises.     

Estimation of Road Friction Coefficient and Vehicle States by 3‐DOF Dynamic Model and HSRI Model Based on Information Fusion

Vehicle states and the road friction coefficient in active safety control systems have become increasingly prominent. However, a low‐cost, high‐precision system in real‐time has yet to be achieved. The use of complex models has led to poor real‐time estimation, while variations in the road friction coefficient have often been neglected. This paper adopts information fusion technology by using DEKF theory for rapid simulation and estimation of these parameters. Using a vehicle dynamic model based on three degrees of freedom (3‐DOF) and the Highway Safety Research Institute tire model, DEKF recursive estimation models are established and verified. In the DEKF, two recursive state and parameter estimation models exist in parallel. The models are dependent upon each other and have real‐time interaction correction in order to forecast information, which quickly yields true value estimation in simulation. Experimental brake test results show that the DEKF estimator not only accurately estimates the vehicle state parameters, but also estimates the road friction coefficient in real‐time. This can reduce the cost of the vehicle sensor, and can estimate the status parameter, which is difficult to measure. The validity and feasibility of this algorithm have been verified by an HIL driving simulator, offering the possibility of future application in real cars.     

基于时间序列的航天器遥测数据预测算法

在航天器遥测数据预测领域,基于时间序列的预测方法有着广阔的应用前景;时间序列有一明显的特性就是记忆性,记忆性是指时间数列中的任一观测值的表现皆受到过去观测值影响;它的基本思想是根据观测数据的特点为数据建立尽可能合理的统计模型,利用模型的统计特性解释数据的统计规律,以期达到预报的目的;提出了采用模式识别和参数估计的方法,结合航天器遥测动态数据,建立关于航天器遥测数据的时序预测模型,对航天器遥测数据趋势进行检测和预报。