High-Order Polynomial Response Surface with Optimal Selection of Interaction Terms

2006 ◽  
Author(s):  
Kikuo Fujita ◽  
Yusuke Kounoe
Keyword(s):  
Response Surface ◽  
High Order ◽  
Optimal Selection ◽  
Interaction Terms ◽  
Order Polynomial ◽  
Polynomial Response Surface ◽  
Selection Of

scholarly journals Novel Two-Stage Method for Low-Order Polynomial Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Cheng Yan ◽  
Xiuli Shen ◽  
Fushui Guo
Keyword(s):  
Response Surface ◽  
Surface Model ◽  
Two Stage ◽  
Nonlinear Simulation ◽  
Weakly Nonlinear ◽  
Second Stage ◽  
Sampling Points ◽  
Order Polynomial ◽  
Polynomial Response Surface ◽  
Low Order

One of the most popular statistical models is a low-order polynomial response surface model, i.e., a polynomial of first order or second order. These polynomials can be used for global metamodels in weakly nonlinear simulation to approximate their global tendency and local metamodels in response surface methodology (RSM), which has been studied in various applications in engineering design and analysis. The order of the selected polynomial determines the number of sampling points (input combinations) and the resulting accuracy (validity, adequacy). This paper derives a novel method to obtain an accurate high-order polynomial while requiring fewer sampling points. This method uses a two-stage procedure such that the second stage modifies the low-order polynomial estimated in the first stage; this second stage does not require new points. This paper evaluates the performance of the method numerically by using several test functions. These numerical results show that the proposed method can provide more accurate predictions than the traditional method.

Application of L6 Orthogonal Array for Optimal Selection of Some Process Parameters in GMAW Process

2012 ◽  
Vol 45 (4) ◽  
pp. 41 ◽  
Author(s):  
M. K. Saha ◽  
Santanu Das ◽  
A. Bandyopadhyay ◽  
S. Bandyopadhyay
Keyword(s):  
Process Parameters ◽  
Orthogonal Array ◽  
Optimal Selection ◽  
Selection Of

Application of L6 Orthogonal Array for Optimal Selection of Some Process Parameters in GMAW Process

2012 ◽  
Vol 45 (4) ◽  
pp. 41
Author(s):  
M. K. Saha ◽  
Santanu Das ◽  
A. Bandyopadhyay ◽  
S. Bandyopadhyay
Keyword(s):  
Process Parameters ◽  
Orthogonal Array ◽  
Optimal Selection ◽  
Selection Of

scholarly journals High-Order Polynomial Fitting Assistance for Fast Double-Peak Finding in Brillouin-Distributed Sensing

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 187
Author(s):  
Marcelo A. Soto ◽  
Alin Jderu ◽  
Dorel Dorobantu ◽  
Marius Enachescu ◽  
Dominik Ziegler
Keyword(s):  
Optical Fibers ◽  
High Order ◽  
Polynomial Fitting ◽  
Gaussian Fitting ◽  
Peak Finding ◽  
Fitting Method ◽  
Fold Reduction ◽  
Order Polynomial ◽  
Fitting In ◽  
Distributed Brillouin Sensor

A high-order polynomial fitting method is proposed to accelerate the computation of double-Gaussian fitting in the retrieval of the Brillouin frequency shifts (BFS) in optical fibers showing two local Brillouin peaks. The method is experimentally validated in a distributed Brillouin sensor under different signal-to noise ratios and realistic spectral scenarios. Results verify that a sixth-order polynomial fitting can provide a reliable initial estimation of the dual local BFS values, which can be subsequently used as initial parameters of a nonlinear double-Gaussian fitting. The method demonstrates a 4.9-fold reduction in the number of iterations required by double-Gaussian fitting and a 3.4-fold improvement in processing time.

scholarly journals Methodology for Determining the Location of River Ports on a Modernized Waterway Based on Non-Cost Criteria: A Case Study of the Odra River Waterway

2021 ◽  
Vol 13 (6) ◽  
pp. 3571
Author(s):  
Bogusz Wiśnicki ◽  
Dorota Dybkowska-Stefek ◽  
Justyna Relisko-Rybak ◽  
Łukasz Kolanda
Keyword(s):  
Large Scale ◽  
Multicriteria Decision Making ◽  
Research Process ◽  
Optimal Selection ◽  
Investment Projects ◽  
Odra River ◽  
Research Problems ◽  
Selection Of ◽  
Construction Works

The paper responds to research problems related to the implementation of large-scale investment projects in waterways in Europe. As part of design and construction works, it is necessary to indicate river ports that play a major role within the European transport network as intermodal nodes. This entails a number of challenges, the cardinal one being the optimal selection of port locations, taking into account the new transport, economic, and geopolitical situation that will be brought about by modernized waterways. The aim of the paper was to present an original methodology for determining port locations for modernized waterways based on non-cost criteria, as an extended multicriteria decision-making method (MCDM) and employing GIS (Geographic Information System)-based tools for spatial analysis. The methodology was designed to be applicable to the varying conditions of a river’s hydroengineering structures (free-flowing river, canalized river, and canals) and adjustable to the requirements posed by intermodal supply chains. The method was applied to study the Odra River Waterway, which allowed the formulation of recommendations regarding the application of the method in the case of different river sections at every stage of the research process.

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