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    Single-replicate longitudinal data analysis in the presence of multiple instrumental measurement errors

    , Article Computers and Industrial Engineering ; Volume 141 , 2020 Moazeni, M ; Behbahani, M ; Khedmati, M ; Akhavan Niaki, S. T ; Sharif University of Technology
    Elsevier Ltd  2020
    Abstract
    In this paper, a novel method as a combination of the expectation–maximization (EM) algorithm and Variogram is proposed to decompose the longitudinal measurement errors in the absence of replications and the presence of multiple instrumental measurement errors. In the proposed method, multiple measurements are considered where the units are observed by several distinct instruments (gauges). The approach decouples the observed variance of the measurement model into the process and measurement system variances. In addition, it decomposes the variance of multiple instruments into the process and instrument variances. In the end, the proposed model is validated and tested based on simulated... 

    A multiple-point statistics algorithm for 3D pore space reconstruction from 2D images

    , Article Advances in Water Resources ; Volume 34, Issue 10 , October , 2011 , Pages 1256-1267 ; 03091708 (ISSN) Hajizadeh, A ; Safekordi, A ; Farhadpour, F. A ; Sharif University of Technology
    2011
    Abstract
    Fluid flow behavior in a porous medium is a function of the geometry and topology of its pore space. The construction of a three dimensional pore space model of a porous medium is therefore an important first step in characterizing the medium and predicting its flow properties. A stochastic technique for reconstruction of the 3D pore structure of unstructured random porous media from a 2D thin section training image is presented. The proposed technique relies on successive 2D multiple point statistics simulations coupled to a multi-scale conditioning data extraction procedure. The Single Normal Equation Simulation Algorithm (SNESIM), originally developed as a tool for reproduction of...