Results 41 to 50 of about 2,027,139 (291)

Diffusion Means and Heat Kernel on Manifolds [PDF]

open access: yes, 2021
8 pages, 1 figure, conference paper submitted to GSI ...
Hansen, Pernille   +4 more
openaire   +4 more sources

The Segal–Bargmann Transform for Odd-Dimensional Hyperbolic Spaces

open access: yesMathematics, 2015
We develop isometry and inversion formulas for the Segal–Bargmann transform on odd-dimensional hyperbolic spaces that are as parallel as possible to the dual case of odd-dimensional spheres.
Brian C. Hall, Jeffrey J. Mitchell
doaj   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli   +3 more
wiley   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

open access: yesAdvanced 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

Effects of the Broken Kernel on Heat and Moisture Transfer in Fixed-Bed Corn Drying Using Particle-Resolved CFD Model

open access: yesAgriculture, 2023
To investigate the pore structure distribution and the coupled heat and moisture transfer during the drying process of the grains, this study focuses on fixed-bed corn drying with varying levels of broken kernel rate.
Wenlei Liu   +4 more
doaj   +1 more source

Heat Kernels, Bergman Kernels, and Cusp Forms [PDF]

open access: yes, 2017
This article is a slightly elaborate version of the article arXiv:1506.08497, where certain errors have been corrected on this ...
openaire   +2 more sources

Microstructural Evolution and Vacancy Defect Formation in Mn–Mo–Ni RPV Steel Under Low Cycle Fatigue: Insights From EBSD and PALS

open access: yesAdvanced 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

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

open access: yesAdvanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran   +6 more
wiley   +1 more source

An integrated exploration of heat kernel invariant feature and manifolding technique for 3D object recognition system

open access: yesActa Scientiarum: Technology, 2023
Spectral Graph theory has been utilized frequently in the field of Computer Vision and Pattern Recognition to address challenges in the field of Image Segmentation and Image Classification.
Subramaniam Usha   +3 more
doaj   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

open access: yesAdvanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle   +5 more
wiley   +1 more source

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