一种求解网损因子的新方法

从耗散功率转归理论所提出的支路功率损耗公式出发,分别针对考虑与不考虑节点注入无功功率对有功功率损耗的影响两种情况下,推导出了用于求解系统总的有功损耗、各节点应当分摊到的有功损耗、节点网损因子的新公式。与传统求解方法相比,该方法完全依靠支路的参数,物理含义清晰,更易于计算。     

Imputation techniques for multivariate missingness in software measurement data

The problem of missing values in software measurement data used in empirical analysis has led to the proposal of numerous potential solutions. Imputation procedures, for example, have been proposed to ‘fill-in’ the missing values with plausible alternatives. We present a comprehensive study of imputation techniques using real-world software measurement datasets. Two different datasets with dramatically different properties were utilized in this study, with the injection of missing values according to three different missingness mechanisms (MCAR, MAR, and NI). We consider the occurrence of missing values in multiple attributes, and compare three procedures, Bayesian multiple imputation, k Nearest Neighbor imputation, and Mean imputation. We also examine the relationship between noise in the dataset and the performance of the imputation techniques, which has not been addressed previously. Our comprehensive experiments demonstrate conclusively that Bayesian multiple imputation is an extremely effective imputation technique.

工业过程数据中缺失值处理方法的研究

针对工业生产中过程数据的缺失问题,首次提出了运用多重填补方法处理工业过程的缺失数据.阐述了常用的缺失数据处理方法,指出各方法的优缺点.在此基础上,通过建立回归模型,针对多变量工业数据中缺失值较少和较多时的两种情况,分别用删除含缺失值的个案,简单填补和多重填补(MI)3种方法对数据进行处理,利用处理后的新数据集进行数据挖掘,预测目标变量的值,并对预测结果进行分析比较.实验结果表明,多重填补方法的处理效果最好,为工业数据的缺失值处理提供了有用的策略.     

基于AP聚类的不完整大数据填充

不完整数据的分析与填充一直是大数据处理的热点研究课题,传统的分析方法无法对不完整数据直接聚类,大部分方法先填充缺失值,然后对数据聚类。这些方法一般利用整个数据集对缺失数据进行填充,使得填充值容易受到噪声的干扰,导致填充结果不精确,进而造成聚类精度很低。提出一种不完整数据聚类算法,对不完全信息系统的相似度公式进行重新定义,给出不完整数据对象间的相似度度量方式,进而直接对不完整数据聚类。根据聚类结果将同一类对象划分到相同的簇中,通过同一类对象的属性值对缺失值进行填充,避免噪声对填充值的干扰,提高填充结果的精确性。实验结果表明,提出的方法能够对不完整数据进行聚类,并有效提高缺失数据的填充精度。     

传感器网络中一种基于多元回归模型的缺失值估计算法

在无线传感器网络中,感知数据的缺失问题不可避免,并且给无线传感器网络的各种应用带来了巨大困难.解决该问题的最好办法是对缺失数据进行准确估计.提出了一种基于多元回归模型的缺失值估计算法.该算法首先依感知数据的时间相关性和空间相关性分别采用多元线性回归模型对缺失数据进行估计,然后根据回归模型的拟合优度对基于时间维和空间维求出的两个估计值分别赋予相应的权值系数,并将其加权平均值作为缺失数据的最后估计值.由于该算法在对缺失值进行估计时,同时考察多个邻居节点并联合地用其感知数据来共同估计缺失值,因此该算法具有可靠、稳定的估计性能.在两个真实的数据集合上对该算法进行了测试,实验结果表明提出的缺失值估计算法能够有效估计无线传感器网络中的缺失数据.     

Short communication: The role of genotypes from animals without phenotypes in single-step genomic evaluations

In a 2-step genomic system, genotypes of animals without phenotypes do not influence genomic prediction of other animals, but that might not be the case in single-step systems. We investigated the effects of including genotypes from culled bulls on the reliability of genomic predictions from single-step evaluations. Four scenarios with a constant amount of phenotypic information and increasing numbers of genotypes from culled bulls were simulated and compared with respect to prediction reliability. With increasing numbers of genotyped culled bulls, there was a corresponding increase in prediction reliability. For instance, in our simulation scenario the reliability for selection candidates was twice as large when all culled bulls from the last 4 generations were included in the analysis. Single-step evaluations imply the imputation of all nongenotyped animals in the pedigree. We showed that this imputation was increasingly more accurate as increasingly more genotypic information from the culled bulls was taken into account. This resulted in higher prediction reliabilities. The extent of the benefit from including genotypes from culled bulls might be more relevant for small populations with low levels of reliabilities.     

Design and evaluation of a decision support system for pain management based on data imputation and statistical models

The self-reporting of pain complaints is considered the most accurate pain assessment method and represents a valuable source of data to computerised clinical decision support systems (CCDSS) for pain management. However, the subjectivity and variability of pain conditions, combined with missing data, are constraints on the usefulness and accuracy of CCDSS. Based on data imputation principles, together with several mathematical models, this paper presents a CCDSS, the Patient Oriented Method of Pain Evaluation System (POMPES), that produces tailored alarms, reports, and clinical guidance based on collected patient-reported data. This system was tested using clinical data collected during a six-week randomised controlled trial involving thirty-two volunteers recruited from an ambulatory surgery department. The decisions resulting from the POMPES were fully accurate when compared with clinical advice, which proves the ability of the system to cope with missing data and detect either stability or changes in the self-reporting of pain.     

Moment adjusted imputation for multivariate measurement error data with applications to logistic regression

In clinical studies, covariates are often measured with error due to biological fluctuations, device error and other sources. Summary statistics and regression models that are based on mis-measured data will differ from the corresponding analysis based on the “true” covariate. Statistical analysis can be adjusted for measurement error, however various methods exhibit a tradeoff between convenience and performance. Moment Adjusted Imputation (MAI) is a measurement error in a scalar latent variable that is easy to implement and performs well in a variety of settings. In practice, multiple covariates may be similarly influenced by biological fluctuations, inducing correlated, multivariate measurement error. The extension of MAI to the setting of multivariate latent variables involves unique challenges. Alternative strategies are described, including a computationally feasible option that is shown to perform well.     

一种新型2-HRR并联机构及其数控系统实现

提出了一种新型的2-HRR构型的两杆两自由度并联平动机构,其具有运动建模简单、正反解计算容易、作业空间大、结构简单和易于控制等独特的优点.本论文对该并联实验平台进行了运动学分析,并结合其结构特点开发了一种开放式、模块化的数控系统,该数控系统是以“PC机 多轴运动控制器”为硬件平台,以“VisualC ”为软件平台构建的,这种构建方式可以实现系统“积木式”的集成.在此基础上,制作了2-HRR构型并联实验平台.通过联机调试,实现了正确的加工轨迹,验证了本文机构分析的正确性和数控系统的可行性.