A Splitting Algorithm For Identifying Qualitative Variables Related To A Dichotomous Dependent Variable

A splitting algorithm based on Shannon measure of mutual information is presented. The algorithm is designed to select individual levels of qualitative variables that are significantly related to a skewed dichotomous dependent variable. The use of this technique in conjunction with a grouping algorithm based on similar principles is illustrated with an actual application.     

一种分段动态数据压缩算法

动态测试系统动态范围(DRN)宽、测试精度高、通道数多、数据冗余大,针对基于数字信号处理器的动态测试系统的特点提出了分段动态数据压缩算法及其类C语言描述和应用实例。该算法实现动态线性压缩,其压缩和重构速度快且开销极很少,用于实时多通道数据采集与分析系统,还可用于语音通信等领域。     

A Grouping Genetic Algorithm for the Pickup and Delivery Problem with Time Windows

The Pickup and Delivery Problem with Time Windows (PDPTW) is a generalization of the well studied Vehicle Routing Problem with Time Windows (VRPTW). Since it models several typical planning situations in operational transportation logistics and public transit, the PDPTW has attracted growing interest in recent years. This paper proposes a Grouping Genetic Algorithm (GGA) for solving the PDPTW which features a group-oriented genetic encoding in which each gene represents a group of requests instead of a single request. The GGA is subject to a comparative test on the basis of two publicly available benchmark problem sets that comprise 9 and 56 PDPTW instances, respectively. The results show that the proposed GGA is competitive.This complete issue was revised and published online in November 2004. The previous version contained a false date.Giselher Pankratz: The author wishes to thank the anonymous referees for their valuable comments     

A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors

Abstract

Computer simulations often involve both qualitative and numerical inputs. Existing Gaussian process (GP) methods for handling this mainly assume a different response surface for each combination of levels of the qualitative factors and relate them via a multiresponse cross-covariance matrix. We introduce a substantially different approach that maps each qualitative factor to underlying numerical latent variables (LVs), with the mapped values estimated similarly to the other correlation parameters, and then uses any standard GP covariance function for numerical variables. This provides a parsimonious GP parameterization that treats qualitative factors the same as numerical variables and views them as affecting the response via similar physical mechanisms. This has strong physical justification, as the effects of a qualitative factor in any physics-based simulation model must always be due to some underlying numerical variables. Even when the underlying variables are many, sufficient dimension reduction arguments imply that their effects can be represented by a low-dimensional LV. This conjecture is supported by the superior predictive performance observed across a variety of examples. Moreover, the mapped LVs provide substantial insight into the nature and effects of the qualitative factors. Supplementary materials for the article are available online.     

An Enhanced Grouping Genetic Algorithm for solving the cell formation problem

Cell formation is often the first step in solving facility layout design problems. The objective is to group part families and machines so that they can be assigned to manufacturing cells. The cell formation problem is a non-deterministic polynomial (NP) complete problem which means that the time taken to produce solutions increases exponentially with problem size. This paper presents the Enhanced Grouping Genetic Algorithm (EnGGA) that has been developed for solving the cell formation problem. The EnGGA replaces the replacement heuristic in a standard Grouping Genetic Algorithm with a Greedy Heuristic and employs a rank-based roulette–elitist strategy, which is a new mechanism for creating successive generations. The EnGGA was tested using well-known data sets from the literature. The quality of the solutions was compared with those produced by other methods using the grouping efficacy measure. The results show that the EnGGA is effective and outperforms or matches the other methods.     

进化论动态参量自适应消噪算法

由于以输出信号平均能量的倒数为适应度函数、以及定值克隆系数和定值匹配系数的因素,制约了传统进化论自适应消噪算法的收敛特性,影响了传统进化论自适应算法降噪的有效性。针对该问题,提出了基于动态适应度函数的进化论变系数自适应消噪算法。新算法中,匹配系数与进化代数相关联,克隆系数由进化代数和适应度值决定,并采用动态适应度函数,改善了滤波器的收敛特性和噪声抑制能力。模拟仿真分析表明,新算法较同类进化论算法有较快的收敛速度和良好的消噪效果。物理台架实验数据验证了该算法对故障信号的有效提取能力。     

变流量多蒸发器制冷系统仿真通用快速算法研究

针对目前变流量制冷系统仿真算法设计研究不多以及通用性不足,提出一种物理意义明确且通用的迭代算法（简称为ALG-I）及其变种算法（简称为ALG-II）。对迭代变量的选择,迭代判据的确定给出了操作准则和方法。对算法流程的关键步骤进行了详细阐述。ALG-I与ALG-II具有相似特性但需要的仿真时间不同。仿真结果表明,提出的算法能适用于任意数目蒸发器的变流量制冷系统仿真,且仿真时间未随蒸发器个数增大而急剧增加,表明算法具有与蒸发器个数无关的通用特性。从控制分析需要快速响应的要求来看,对于一拖一系统, ALG-II比ALG-I有优势,而对于一拖多系统,ALG-I则比ALG-II有优势。最后,系统对连续变化的控制变量（包括膨胀阀开度及压缩机转速）的合理响应表明提出的算法可以有效地用于VRF系统的能耗与控制仿真。     

求解考虑顺序相关调整时间的双边装配线平衡问题的变邻域搜索算法

为有效解决带有顺序相关调整时间的双边装配线平衡问题,提出了一种简单高效的变邻域搜索算法。该算法通过将优先关系约束融入到交换、插入、交叉、变异等算子中,分别得到4个不同的邻域结构来保证搜索过程中解的可行性,避免过多重复邻域解的生成。4个邻域结构的搜索空间依次变大,以增强算法搜索能力。同时,结合装配线的特点,提出基于作业序列的编码和解码方式,在解码过程中,优先选择空闲时间较多的边,引入启发式目标加快算法收敛。分配结束后,对装配线末端的工作站组进行局部调整。通过将该算法先后用于求解无/有顺序相关调整时间的双边装配线平衡第一类问题,并与已有的算法进行对比,验证了所提的变邻域搜索算法的优越性和有效性。     

A Stochastic EM Algorithm for Progressively Censored Data Analysis

Progressive censoring technique is useful in lifetime data analysis. Simple approaches to progressive data analysis are crucial for its widespread adoption by reliability engineers. This study develops an efficient yet easy‐to‐implement framework for analyzing progressively censored data by making use of the stochastic EM algorithm. On the basis of this framework, we develop specific stochastic EM procedures for several popular lifetime models. These procedures are shown to be very simple. We then demonstrate the applicability and efficiency of the stochastic EM algorithm by a fatigue life data set with proper modification and by a progressively censored data set from a life test on hard disk drives. Copyright © 2013 John Wiley & Sons, Ltd.     

线性回归模型的Boosting变量选择方法

针对线性回归模型的变量选择问题,本文基于遗传算法提出了一种新的Boosting学习方法.该方法对每一训练个体赋予权重,以遗传算法作为Boosting的基学习算法,将带有权重分布的训练集作为遗传算法的输入进行变量选择.同时,根据前一次变量选择效果的好坏更新训练集上的权重分布.重复上述步骤多次,最后以加权融合方式合并多次变量选择的结果.基于模拟和实际数据的试验结果表明,本文新提出的Boosting方法能显著提高传统遗传算法用于变量选择的质量,准确识别出与响应变量相关的协变量,这为线性回归模型的变量选择提供了一种有效的新方法.     

一种新型的大气数据测量方法

提出一种测量飞机大气数据的新方法。将声矢量传感器应用于嵌入式大气数据传感系统上,采用矢量传感器测得声波的质点速度,通过建立质点速度、音速和空速之间的数学模型,求出飞机的马赫数,利用马赫数求出飞机的动静压。此方法计算量小,实时性好,计算仿真验证了此方法的有效性。     

IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification

Medical data classification (MDC) refers to the application of classification methods on medical datasets. This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis. To gain experts’ trust, the prediction and the reasoning behind it are equally important. Accordingly, we confine our research to learn rule-based models because they are transparent and comprehensible. One approach to MDC involves the use of metaheuristic (MH) algorithms. Here we report on the development and testing of a novel MH algorithm: IWD-Miner. This algorithm can be viewed as a fusion of Intelligent Water Drops (IWDs) and AntMiner+. It was subjected to a four-stage sensitivity analysis to optimize its performance. For this purpose, 21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine. Interestingly, there were only limited differences in performance between IWD-Miner variants which is suggestive of its robustness. Finally, using the same 21 datasets, we compared the performance of the optimized IWD-Miner against two extant algorithms, AntMiner+ and J48. The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner, as confirmed by the Wilcoxon nonparametric statistical test. Results suggest that IWD-Miner is more efficient than AntMiner+ as measured by the average number of fitness evaluations to a solution (1,386,621.30 vs. 2,827,283.88 fitness evaluations, respectively). J48 exhibited higher accuracy on average than IWD-Miner (79.58 vs. 73.65, respectively) but produced larger models (32.82 leaves vs. 8.38 terms, respectively).     

面向跨媒体数字出版的可变数据印刷研究

深刻剖析了个性化印刷标记语言格式PPML的数据结构,并在制作一个可变数据印刷作业的实验基础上,利用Java语言设计和开发了PPML可变数据印刷作业的生成和读取模块。该模块可直接应用于可变数据印刷系统的研发,真正实现基于PPML标准的个性化数字印刷。     

一种变步长LMS自适应滤波算法研究

为了解决最小均方(LMS)算法的稳定性及收敛速度(自适应速度)和稳态误差(自适应滤波器的精度)之间的矛盾,本文提出了一种自适应变步长的LMS算法,它的权系数的调整取决于误差曲面在新权值点上的梯度.分析了新算法的收敛特性以及参数选择对算法性能的影响.该算法具有较快的收敛速度、鲁棒稳定性且运算小易于实现的特点.计算机仿真的结果证实了该算法的收敛性能优于标准的LMS算法并且具有较好的实用性.     

一种改进的一致性多传感器数据融合算法

采用阵列脉冲涡流技术对腐蚀缺陷进行检测,提出了改进的一致性多传感器数据融合算法.通过重新定义置信距离测度,保证了置信距离矩阵的对称性,最后采用加权平均法对通过了一致性数据检验的多传感器数据进行了融合,实验结果表明该算法具有较高的精度.     

纵向数据下部分线性EV模型的变量选择

本文考虑了纵向数据下部分线性EV模型的变量选择问题,采用SCAD惩罚方法提出了一个变量选择过程.通过选择适当的惩罚参数,证明了该变量选择过程可以相合地识别出真实模型,并且所得的正则估计具有Orcale性质.最后模拟研究了所提出方法的有限样本性质.     

一种应用于自适应降噪的变步长LMS算法

通过建立步长因子μ与误差信号е之间的非线性关系,提出一种改进的变步长LMS算法,并将其应用于通信降噪.该算法除了具有传统固定步长LMS算法计算量小、稳定性好、简单、易于实时处理等优点外,理论分析及计算机仿真结果表明,其收敛速度及稳定性优于SVSLMS算法,且不需要进行指数运算,计算复杂度低于SVSLMS算法,用于通信降噪取得了较好的效果.     

基于变维滤波算法的Kalman最佳平滑器在滑坡监测数据处理中的应用

本文研究如何对滑坡监测数据进行去噪.利用变维滤波算法能够很好描述滑坡变形的特性,建立了滑坡运动的数学模型,并引入变维滤波对滑坡监测数据进行降噪.为了提高滑坡预报精度,提出了一种基于变维滤波的Kalman最佳平滑算法.实例仿真结果表明利用该方法不仅预报精度高,而且也为寻找滑坡监测数据中的变点提供了新的思路.     

一种基于数据矩阵的快速ESPRIT实现

本文首先介绍了ESPRIT算法的基本原理,在此基础上提出了一种新的直接基于数据矩阵的D-ESPRIT方法,该方法利用高斯白噪声均值为零的特性来消除混合在有效信号中的噪声,通过构造新的矩阵束求解广义特征值进一步获取正弦组合信号的成分频率.与基本ESPRIT算法相比,该方法可以大大节省计算量,加快求解速度,而性能接近.计算机仿真证实了方法的有效性.