Results 41 to 50 of about 41,711 (262)
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
On the effect of fixed-bandwidth kernel density estimation on the exponential distribution
Journal of Nigerian Society of Physical SciencesKernel density estimation (KDE) is widely used as a nonparametric smoothing operator in statistics. In this work, we study fixed-bandwidth KDE as a convolution operator applied to an exponential baseline distribution with rate parameter (beta > 0).
Anwar Bataihah
doaj +1 more source
Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
Journal of Statistical Software, 2007 Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing.
Tarn Duong
doaj
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2018 Data smoothing is often required within the environmental data analysis. A number of methods and algorithms that can be applied for data smoothing have been proposed.
Martina Čampulová
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Graph Dilated Network with Rejection Mechanism
Applied Sciences, 2020 Recently, graph neural networks (GNNs) have achieved great success in dealing with graph-based data. The basic idea of GNNs is iteratively aggregating the information from neighbors, which is a special form of Laplacian smoothing.
Bencheng Yan, Chaokun Wang, Gaoyang Guo
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Heat Kernel Smoothing on Unit Sphere [PDF]
3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., 2006 In brain imaging, cortical data such as the cortical thickness, cortical surface curvatures and surface coordinates have been mapped to a unit sphere for the purpose of visualization, surface registration and statistical analysis. Since the unit sphere provides a readily available parametrization and basis functions, cortical data can be easily ...
openaire +1 more source
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
New Bandwidth Selection for Kernel Quantile Estimators
Journal of Probability and Statistics, 2012 We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a ...
Ali Al-Kenani, Keming Yu
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Performing the Kernel Method of Test Equating with the Package kequate
Journal of Statistical Software, 2013 In standardized testing it is important to equate tests in order to ensure that the test takers, regardless of the test version given, obtain a fair test.
Björn Andersson +2 more
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