Results 51 to 60 of about 415,263 (295)
Advanced Engineering Materials, EarlyView.Low‐cycle fatigue damage in Mn–Mo–Ni reactor pressure vessel steel is examined using a combined electron backscatter diffraction and positron annihilation lifetime spectroscopy approach. The study correlates texture evolution, dislocation substructure development, and vacancy‐type defect formation across uniform, necked, and fracture regions, providing
Apu Sarkar +2 more
wiley +1 more source
A Kernel-Based Calculation of Information on a Metric Space
, 2013 Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space.
Houghton, Conor J., Tobin, R. Joshua
core +2 more sources
Advanced Engineering Materials, EarlyView.Creep experiments at 900°C on coarse‐grained steel‐ceramic composites containing recycled magnesia reveal that higher ceramic volume fractions significantly enhance the creep resistance. Detailed EBSD investigations identify subgrain formation in the steel matrix as the dominant deformation mechanism.
Moritz Müller +6 more
wiley +1 more source
Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution
Information, 2023 Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version.
Asif Hussain Khan +2 more
doaj +1 more source
The stochastic approximation method for the estimation of a multivariate probability density
, 2008 We apply the stochastic approximation method to construct a large class of recursive kernel estimators of a probability density, including the one introduced by Hall and Patil (1994).
Mokkadem, Abdelkader +2 more
core +5 more sources
Functional kernel estimators of conditional extreme quantiles [PDF]
, 2011 We address the estimation of "extreme" conditional quantiles i.e. when their order converges to one as the sample size increases. Conditions on the rate of convergence of their order to one are provided to obtain asymptotically Gaussian distributed ...
A Berlinet +14 more
core +8 more sources
Regularized nonparametric Volterra kernel estimation [PDF]
Automatica, 2017 In this paper, the regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modelled as Volterra series. The kernels of order higher than one, representing higher dimensional impulse responses in the series, are considered to be realizations of multidimensional ...
Georgios Birpoutsoukis +3 more
openaire +5 more sources
Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Estimation of Weighted Extropy with Focus on Its Use in Reliability Modeling
EntropyIn the literature, estimation of weighted extropy is infrequently considered. In this paper, some non-parametric estimators of weighted extropy are given.
Muhammed Rasheed Irshad +3 more
doaj +1 more source
Bivariate box plots based on quantile regression curves
Dependence Modeling, 2020 In this paper, we propose a procedure to build bivariate box plots (BBP). We first obtain the theoretical BBP for a random vector (X, Y). They are based on the univariate box plot of X and the conditional quantile curves of Y|X. They can be computed from
Navarro Jorge
doaj +1 more source