无线传感器网络分布式调度方法研究

无线传感器网络(Wireless sensor network, WSN)是一个资源受限的网络系统.已提出了多种调度方法来提高网络性能. 本文归纳了WSN分布式调度方法的设计原则和分类方法,并按调度对象对调度方法进行了分类讨论. 详细论述了一些典型调度方法的内在机理,分析了每一类调度方法的特点. 对这些调度方法的设计目标和性能特点进行了对比.最后总结了WSN分布式调度方法的研究现状, 提出了该领域今后发展需要关注的重要因素.     

基于GQM的软件体系结构适应性度量方法研究*

基于GQM方法,结合当前的研究现状,根据实际需要,提出了一个新的面向过程的软件体系结构适应性度量方法.该方法在GQM方法的基础上,提出了面向过程的度量方法建模步骤,引入数据项分层和确认分层,增加了度量的结构化描述,定义了新的度量指标并提出了基于区间AHP和DEA的决策方法,对GQM方法进行了进一步的扩展和改进.     

代码自动检测方法研究

讨论了传统软件测试方法的缺点和局限性,进而提出了代码自动检测的方法,给出了此类方法可检测到的故障类型.对程序静态分析技术和方法进行了研究,依据该方法设计与开发了软件测试系统.最后给出了实验结果和对比分析.     

一种集成智能故障诊断方法研究

该文提出了一种新的故障诊断方法,该方法把基于观测器的诊断和基于知识的诊断方法相结合而成为一种集成智能故障诊断方法。在研究了基于多观测器故障诊断方法的基础上,对该集成诊断方法进行了较为详细的讨论。仿真例子表明了该方法的有效性和优越性。     

主题网络爬虫研究综述

首先给出了主题网络爬虫的定义和研究目标;然后系统分析了近年来国内外主题爬虫的研究方法和技术,包括基于文字内容的方法、基于超链分析的方法、基于分类器预测的方法以及其他主题爬行方法,并比较了各种方法优缺点;最后对未来的研究方向进行了展望.     

一种基于PB的图像动态显示方法

介绍了PB常见的图像显示方法,研究了用Modify函数动态显示图像的方法,该方法具有速度快、动态性强、显示方法灵活等特点,分析了PB的图像显示原理,比较了各种图像显示方法的性能。     

开放式计算环境中一个新的声誉建立方法

归纳了目前信任管理领域中声誉建立的方法,分析其不足之处并提出了一种新的声誉建立方法。该方法综合利用了拓扑信息和反馈信息,用于开放式计算环境中声誉的建立。将该方法与当前的方法进行了分析和对比。该方法可以有效地处理开放式计算环境中反馈信息少和动态性的问题,与当前方法相比,该方法更为有效。     

一种海量规则模式匹配方法

基于各种海量规则信息处理的需求,提出了一种海量规则模式匹配方法。设计了海量规则模式匹配方法的基本算法步骤,研究了各种规则节点的匹配处理方法。最后总结了海量规则模式匹配方法的特点。海量规则模式匹配算法部分拓展了现有规则匹配处理模式,提出了新的匹配处理方法。对比结果表明,该方法具有较好的效果。     

一种改进的可区分矩阵与求核方法

本文针对Hu方法存在的问题,提出了一种改进的可区分矩阵与求核方法,并证明了该方法的正确性。该方法不仅克服了Hu方法的不足,而且指出了Hu方法出错的根本原因。     

烟雾模拟方法的研究综述

计算机图形学的烟雾模拟,主要采用三大类方法:粒子系统方法、数学物理方法和纹理技术方法;其中粒子系统方法属于比较早期的简单方法,数学物理方法大部分是基于或围绕N-S方程的。介绍了烟雾模拟的主要方法与应用等,对烟雾模拟的方法进行了细致的分类;有早期的烟雾模型,也讨论了近年来出现的一些最新发展。讨论了各种模拟方法的优缺点;指出了目前烟雾方法中存在的一些问题;关注了未来的研究工作需要重点加以关切的几个方面;介绍了今后需要进一步努力和完善的一些工作思路。     

Derived components regression using the BACON algorithm

Partial least squares and principal components regression are commonly used regularized regression methods which use derived components instead of original predictors. The components are derived from the estimated variance-covariance matrix and regression is run using the least squares. Therefore, they are not robust and a few outliers may have drastic effects on the obtained results. These regression methods are robustified by using the BACON algorithm which provides robust measures for both dispersion and regression. The proposed methods are illustrated by examples and their properties are investigated using both real data and simulation experiments.     

基于Sas的时间序列缺失值处理方法比较

对于时间序列挖掘过程中的缺失值处理,目前有许多方法。在处理数据变量成一定的相关的数据集时,回归模型不失为较好的插补方法。利用均值插补、一元线性回归、多元线性回归、迭代回归方法对水文时间序列数据集的缺失数据进行处理,比较不同的皮氏相关系数下各方法的优劣及适用性。文中研究表明当数据集中存在与缺值变量相关度较大的变量时,一元线性回归的插补简单直观,且有较高的精度,结果接近真实;当数据集中不存在与缺值变量显著相关的自变量时,一元线性回归的结果变差,多元线性回归与多元迭代回归具有较好的结果,但多元迭代回归迭代次数难以确定,插补代价较大,多元线性回归为最佳选择;当缺值变量与其他自变量相关系数均较小时,回归插补的结果不理想,此时可考虑其他插补方法。     

机器学习在列车精确停车问题的应用

列车精确停车是实现轨道交通自动控制系统的关键技术之一。传统的精确停车技术需要依赖于复杂的物理模型及昂贵的传感设备,且难以达到较高的精度。从数据本身出发,利用机器学习中高斯过程回归和Boosting回归算法对列车精确停车问题进行了研究,并与线性回归方法进行了比较,实验表明,机器学习的方法对于解决列车精确停车问题是行之有效的。其中以高斯过程回归的性能最优,而基于梯度的Boosting回归方法在缺乏先验知识的条件下达到接近高斯过程回归的性能,在实际应用中具有更大的灵活性和适应性。     

Solve least absolute value regression problems using modified goal programming techniques

Least absolute value (LAV) regression methods have been widely applied in estimating regression equations. However, most of the current LAV methods are based on the original goal program developed over four decades. On the basis of a modified goal program, this study reformulates the LAV problem using a markedly lower number of deviational variables than used in the current LAV methods. Numerical results indicate that for the regression problems with hundreds of observations, this novel method can save more than 1/3 of the CPU time compared to current LAV methods.     

OWA Operators in Regression Problems

We consider an application of fuzzy logic connectives to statistical regression. We replace the standard least squares, least absolute deviation, and maximum likelihood criteria with an ordered weighted averaging (OWA) function of the residuals. Depending on the choice of the weights, we obtain the standard regression problems, high-breakdown robust methods (least median, least trimmed squares, and trimmed likelihood methods), as well as new formulations. We present various approaches to numerical solution of such regression problems. OWA-based regression is particularly useful in the presence of outliers, and we illustrate the performance of the new methods on several instances of linear regression problems with multiple outliers.      

多帧融合自适应核回归图像去噪

图像去噪是数字图像处理的重要内容,常用的传统方法包括空域中值滤波和维纳滤波,近年来基于小波变换、核回归等的去噪方法备受关注,基于单帧处理的实验发现核回归方法有更好的去噪效果。在理论上将核回归方法推广到多帧情况,并进行了对比实验,结果表明多帧处理能够进一步改进去噪效果。     

Reduction-based methods and metrics for selective regression testing

In corrective maintenance, modified software is regression tested using selected test cases in order to ensure that the modifications have not caused adverse effects. This activity of selective regression testing involves regression test selection, which refers to selecting test cases from the previously run test suite, and test-coverage identification. In this paper, we propose three test-selection methods and two coverage identification metrics. The three methods aim to reduce the number of selected test cases for retesting the modified software. The first method, referred to as modification-based reduction version 1 (MBR1), selects a reduced number of test cases based on the modification made and its effects in the software. The second method, referred to as modification-based reduction version 2 (MBR2) improves MBR1 by further omitting tests that do not cover the modification. The third method, referred to as precise reduction (PR), reduces the number of test cases selected by omitting non-modification-revealing tests from the initial test suite. The two coverage metrics are McCabe-based regression test metrics, which are referred to as the Reachability regression Test selection McCabe-based metric (RTM), and data-flow Slices regression Test McCabe-based metric (STM). These metrics aim to assist the regression tester in monitoring test-coverage adequacy, reveal any shortage or redundancy in the test suite, and assist in identifying, where additional tests may be required for regression testing.We empirically compare MBR1, MBR2, and PR with three reduction and precision-oriented methods on 60 test-problems. The results show that PR selects the least number of test cases most of the time and omits non-modification-revealing tests. We also demonstrate the applicability of our proposed methods to object-oriented regression testing at the class level. Further, we illustrate typical application of the RTM and STM metrics using the 60 test-problems and two coverage-oriented selective regression-testing methods.     

多重关联的静态回归测试用例集构建研究

静态回归测试用例集构建策略是依据程序间的调用关联,分析因代码更改而受影响的模块,进而构建回归测试用例集,该方法并没有考虑程序间的隐式数据关联,对同一数据库操作或者对公共对象数据操作的方法间存在隐式数据关联。针对代码更改不仅会对调用关联的方法产生影响,也会对隐式数据关联的方法产生影响进行研究,提出了一种多重关联的静态回归测试用例集构建策略,通过构建多重方法关联图分析方法间调用关联和隐式数据关联,进而依据关联关系构建因代码更改而受影响的回归测试用例集。通过对4个开源项目进行实验评估,实验结果表明本文提出的静态策略提高了回归测试的安全性和精确性。     

A new accelerated proximal technique for regression with high-dimensional datasets

We consider the problem of minimization of the sum of two convex functions, one of which is a smooth function, while another one may be a nonsmooth function. Many high-dimensional learning problems (classification/regression) can be designed using such frameworks, which can be efficiently solved with the help of first-order proximal-based methods. Due to slow convergence of traditional proximal methods, a recent trend is to introduce acceleration to such methods, which increases the speed of convergence. Such proximal gradient methods belong to a wider class of the forward–backward algorithms, which mathematically can be interpreted as fixed-point iterative schemes. In this paper, we design few new proximal gradient methods corresponding to few state-of-the-art fixed-point iterative schemes and compare their performances on the regression problem. In addition, we propose a new accelerated proximal gradient algorithm, which outperforms earlier traditional methods in terms of convergence speed and regression error. To demonstrate the applicability of our method, we conducted experiments for the problem of regression with several publicly available high-dimensional real datasets taken from different application domains. Empirical results exhibit that the proposed method outperforms the previous methods in terms of convergence, accuracy, and objective function values.     

Regression with residual neural network for vanishing point detection

This paper aims to propose a regression method with a residual neural network (ResNet) for vanishing point detection. The purpose of this study is to estimate the position of the vanishing point accurately. Our newly collected Naver Maps' Street View dataset is used for training regression ResNet-34 and for comparison with previous methods. It is concluded that the trained regression ResNet outperforms previous methods in terms of both computation time and accuracy.