检索结果-内蒙古大学图书馆

Extension of rebmix algorithm to von Mises parametric family for modeling joint distribution of wind speed and direction

引用

ENGINEERING STRUCTURES 2019年 183卷 1134-1145页

作者： Ye, X. W. Xi, P. S. Nagode, Marko Zhejiang Univ Dept Civil Engn Hangzhou 310058 Zhejiang Peoples R China Univ Ljubljana Fac Mech Engn Askerceva 6 SI-1000 Ljubljana Slovenia

A statistical analysis of the wind speed and wind direction serves as a solid foundation for the wind-induced vibration analysis. The probabilistic modeling of wind speed and direction can effectively characterize the stochastic properties of wind field. The joint distribution model of wind speed and direction involves a circular distribution and has a multimodal characteristic. In this paper, the finite mixture distribution model is introduced and used to represent the joint distribution model that is comprised of the mixture Weibull distributions and von Mises distributions. An extended parameters estimation algorithm for multivariate and multimodal circular distributions is proposed to construct the joint distribution model. The proposed algorithm estimates the component parameters, mixture weight of each component and the number of components successively by an iterative process. The major improvement is accomplished by adding a circular distribution model. The effectiveness of the proposed algorithm is verified with numerical simulations and one-year field monitoring data and compared with the expectation maximization algorithm-based angular-linear approach in terms of the Akaike's information criterion and computing time. The results indicate that the finite mixture model represents the joint distribution model of wind speed and direction well and that the proposed algorithm has a good and time-saving performance in parameter estimation for multivariate and multimodal models.

关键词： Structural health monitoring Wind field characteristics Joint probability density function Mixture estimation rebmix algorithm

来源：评论

学校读者我要写书评

暂无评论

Fault detection and classification with the rebmix R package

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2023年 185卷

作者： Nagode, Marko Panic, Branislav Klemenc, Jernej Oman, Simon Univ Ljubljana Fac Mech Engn Askerčeva U 6l Ljubljana 1000 Slovenia

Fault detection and classification is an important part of assessing the structural and system health status. The classification and detection of faults and faulty units is mostly done with statistical methods. After the data are measured and collected, the use of statistical software is necessary. Currently, many statistical software packages are being developed for the R programming language, as a result of R implementation being open source and free to use. This paper focuses on the rebmix R package, which concentrates on mixture model estimation. Mixture models, in particular Gaussian mixture models, are the main driver for many practical applications, such as clustering and classification. Hence, in this paper, we have expanded the rebmix for the estimation of the Gaussian mixtures. The results acquired on three different fault classification datasets were promising. Additionally, the process of obtaining those results is shown in detail, giving the researchers in the fault classification field useful resources for their research.

关键词： Fault classification Fault detection Mixture models rebmix algorithm EM algorithm

来源：评论

学校读者我要写书评

暂无评论

Probability density function of the equivalent stress amplitude using statistical transformation

引用

RELIABILITY ENGINEERING & SYSTEM SAFETY 2015年第Feb.期134卷 118-125页

作者： Franko, Mitja Nagode, Marko Univ Ljubljana Fac Mech Engn Ljubljana 1000 Slovenia

The shape of a rainflow matrix is complex and cannot be approximated by a simple distribution function. In this paper, the Weibull-normal mixture distribution is used, for which the number of components and unknown parameters are required to be estimated. The scope of the paper is to estimate the number of components and unknown parameters using the FlexMix and rebmix algorithms, and compare their results. The results are then used in Goodman and Walker mean stress correction methods. This correction is not made as a point-to-point transformation, where the information about the distribution function of the rainflow matrix is lost. Instead, the used distribution function of the rainflow matrix with estimated parameters is transformed in accordance with Goodman and Walker mean stress correction methods. With this procedure, the probability density of the equivalent stress amplitude is immediately obtained, and the information about the distribution function of the rainflow matrix is not lost. (C) 2014 Published by Elsevier Ltd.

关键词： EM algorithm rebmix algorithm FlexMix algorithm Weibull-normal mixture Mean stress correction

来源：评论

学校读者我要写书评

暂无评论

Finite Mixture Estimation algorithm for Arbitrary Function Approximation

引用

STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2012年第2期58卷 115-124页

作者： Volk, Matej Nagode, Marko Fajdiga, Matija Univ Ljubljana Fac Mech Engn Ljubljana 1000 Slovenia

The paper considers a new prospect of the arbitrary continuous function approximation from a limited set of input data with the rebmix algorithm, developed for the finite mixture density estimation. Since the rebmix estimates the unknown parameters with the unique semi-parametric method, it is assumed that it could be used also for the estimation of the unknown parameters in the fields that are not directly connected to density function estimation. For the approximation of the arbitrary continuous function with the rebmix algorithm, the required procedure is developed in the paper. The results gained by the proposed procedure and by the radial basis function network for three different datasets are compared by calculating the RMSE values between estimated and test output values. The adequacy of the proposed procedure is estimated by using both univariate and bivariate datasets. It can be concluded that with the developed procedure, the rebmix algorithm can be applied successfully for the continuous function approximation.

关键词： rebmix algorithm function approximations finite mixtures RBF networks parameter estimation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：