As modern weapons and equipment undergo increasing levels of informatization,intelligence,and networking,the topology and traffic characteristics of battlefield data networks built with tactical data links are becomin...As modern weapons and equipment undergo increasing levels of informatization,intelligence,and networking,the topology and traffic characteristics of battlefield data networks built with tactical data links are becoming progressively complex.In this paper,we employ a traffic matrix to model the tactical data link network.We propose a method that utilizes the Maximum Variance Unfolding(MVU)algorithm to conduct nonlinear dimensionality reduction analysis on high-dimensional open network traffic matrix datasets.This approach introduces novel ideas and methods for future applications,including traffic prediction and anomaly analysis in real battlefield network environments.展开更多
WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted ma...WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from http：//agbu. une.edu.au/-kmeyer/wombat.html展开更多
提出一种以建筑信息模型(building information modeling,BIM)数据校正激光雷达误差的方法,用于解决建筑机器人室内精定位问题。首先,对雷达的采样数据进行概率建模,然后采用方差分析法研究影响机器人定位精度的不确定性变量显著性水平...提出一种以建筑信息模型(building information modeling,BIM)数据校正激光雷达误差的方法,用于解决建筑机器人室内精定位问题。首先,对雷达的采样数据进行概率建模,然后采用方差分析法研究影响机器人定位精度的不确定性变量显著性水平,确定敏感变量后引入BIM信息结合极大似然估计方法对雷达的数据进行修正。最后,对激光雷达扫描到的目标物位置精度进行评价,结果表明,不同种类别的材质误差差异在毫米级,所提出的方法具有较好的补偿效果。展开更多
Based on an extended Gauss-Markov model where the unknown parameters has the prior normal distribution, this paper derives the maximum posterior estimate formulas of the parameters which are proved to be unbiased,effi...Based on an extended Gauss-Markov model where the unknown parameters has the prior normal distribution, this paper derives the maximum posterior estimate formulas of the parameters which are proved to be unbiased,efficient, and of variance of unit weight which is biased. Finally, the marginal maximum posterior estimate formula of the variance with unbiased and efficient , properties is derived.展开更多
文摘As modern weapons and equipment undergo increasing levels of informatization,intelligence,and networking,the topology and traffic characteristics of battlefield data networks built with tactical data links are becoming progressively complex.In this paper,we employ a traffic matrix to model the tactical data link network.We propose a method that utilizes the Maximum Variance Unfolding(MVU)algorithm to conduct nonlinear dimensionality reduction analysis on high-dimensional open network traffic matrix datasets.This approach introduces novel ideas and methods for future applications,including traffic prediction and anomaly analysis in real battlefield network environments.
基金Project (No. BFGEN.100B) supported by the Meat and LivestockLtd., Australia (MLA)
文摘WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from http：//agbu. une.edu.au/-kmeyer/wombat.html
文摘提出一种以建筑信息模型(building information modeling,BIM)数据校正激光雷达误差的方法,用于解决建筑机器人室内精定位问题。首先,对雷达的采样数据进行概率建模,然后采用方差分析法研究影响机器人定位精度的不确定性变量显著性水平,确定敏感变量后引入BIM信息结合极大似然估计方法对雷达的数据进行修正。最后,对激光雷达扫描到的目标物位置精度进行评价,结果表明,不同种类别的材质误差差异在毫米级,所提出的方法具有较好的补偿效果。
文摘Based on an extended Gauss-Markov model where the unknown parameters has the prior normal distribution, this paper derives the maximum posterior estimate formulas of the parameters which are proved to be unbiased,efficient, and of variance of unit weight which is biased. Finally, the marginal maximum posterior estimate formula of the variance with unbiased and efficient , properties is derived.
