Improving Imaging and Inversion Through Least-Squares Q-Kirchhoff APSDM: A Case Study from Offshore China

Least squares balancing methods have been applied for many years to reduce vibration levels of turbomachinery. This approach yields an optimal configuration of balancing weights to reduce a given cost function. However, in many situations, the cost function is not well-defined by the problem, and a more interactive method of determining the effects of balance weight placement is desirable. An interactive balancing procedure is outlined and implemented in an Excel spreadsheet. The usefulness of this interactive approach is highlighted in balancing case studies of a GE LM5000 gas turbine and an industrial fan. In each case study, attention is given to practical aspects of balancing such as sensor placement and balancing limitations.

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PEMODELAN REGRESI RIDGE ROBUST-MM DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi AKB di Jawa Tengah Tahun 2017)

Jurnal Gaussian ◽

10.14710/j.gauss.v8i1.26619 ◽

2019 ◽

Vol 8 (1) ◽

pp. 24-34

Author(s):

Eka Destiyani ◽

Rita Rahmawati ◽

Suparti Suparti

Keyword(s):

Least Squares ◽

Robust Regression ◽

Ordinary Least Squares ◽

Regression Method ◽

Predictor Variables ◽

Healthy Life ◽

Significance Level ◽

Central Java ◽

Regression Parameters

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linear regression parameters. If multicollinearity is exist within predictor variables especially coupled with the outliers, then regression analysis with OLS is no longer used. One method that can be used to solve a multicollinearity and outliers problems is Ridge Robust-MM Regression. Ridge Robust-MM Regression is a modification of the Ridge Regression method based on the MM-estimator of Robust Regression. The case study in this research is AKB in Central Java 2017 influenced by population dencity, the precentage of households behaving in a clean and healthy life, the number of low-weighted baby born, the number of babies who are given exclusive breastfeeding, the number of babies that receiving a neonatal visit once, and the number of babies who get health services. The result of estimation using OLS show that there is violation of multicollinearity and also the presence of outliers. Applied ridge robust-MM regression to case study proves ridge robust regression can improve parameter estimation. Based on t test at 5% significance level most of predictor variables have significant effect to variable AKB. The influence value of predictor variables to AKB is 47.68% and MSE value is 0.01538.Keywords: Ordinary Least Squares (OLS), Multicollinearity, Outliers, RidgeRegression, Robust Regression, AKB.

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INTERPRETATION OF MAGNETIC DATA USING BOUNDARY ANALYSIS AND INVERSION TECHNIQUES: A CASE STUDY FROM GÖLCÜK/ISPARTA (TURKEY) REGION

TURKISH JOURNAL OF EARTH SCIENCES ◽

10.3906/yer-2009-3 ◽

2021 ◽

Keyword(s):

Magnetic Data ◽

Boundary Analysis ◽

Inversion Techniques ◽

And Inversion

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Polymethyl Methacrylate Quality Modeling with Missing Data Using Subspace Based Model Identification

Processes ◽

10.3390/pr9101691 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1691

Author(s):

Nikesh Patel ◽

Kavitha Sivanathan ◽

Prashant Mhaskar

Keyword(s):

Missing Data ◽

Least Squares ◽

Partial Least Squares ◽

Polymethyl Methacrylate ◽

Model Identification ◽

Principal Component ◽

Quality Modeling ◽

The Impact ◽

Quality Measurements

This paper addresses the problem of quality modeling in polymethyl methacrylate (PMMA) production. The key challenge is handling the large amounts of missing quality measurements in each batch due to the time and cost sensitive nature of the measurements. To this end, a missing data subspace algorithm that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principal component analysis (PCA) is utilized to build a data driven dynamic model. The use of NIPALS algorithms allows for the correlation structure of the input–output data to minimize the impact of the large amounts of missing quality measurements. These techniques are utilized in a simulated case study to successfully model the PMMA process in particular, and demonstrate the efficacy of the algorithm to handle the quality prediction problem in general.

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