Results 61 to 70 of about 88,517 (260)

Avoiding barren plateaus via Gaussian mixture model

New Journal of Physics
Variational quantum algorithms are among the most prominent methods in quantum computing, with applications in quantum machine learning, quantum simulation, and related fields.
Xiao Shi, Yun Shang
doaj   +1 more source

In Situ Micromechanical Study of Bimodal γ′–γ″ Precipitate Assemblies in Ni–Cr–Al–Nb Superalloy

Advanced Engineering Materials, EarlyView.
A Ni–Cr–Al–Nb superalloy with a bimodal γ′–γ″ precipitate distribution is developed. Composite precipitate assemblies form through heterogeneous nucleation, effectively impeding dislocation motion. Micropillar compression reveals high strength at room and elevated temperatures, governed by precipitate shearing, with coupled faulting mechanisms ...
Ujjval Bansal, Tae‐Hyeok Kang, Pyuck‐Pa Choi, Subin Lee, Christoph Kirchlechner   +4 more
wiley   +1 more source

Molecular Dynamics Studies of Shape Memory Polymers: From Bead–Spring Models to Atomistic Simulations

Advanced Engineering Materials, EarlyView.
Coarse‐grained (left) and atomistic (right) models of the shape memory polymer ESTANE ETE 75DT3 are shown schematically. The two representations bridge molecular detail and mesoscopic description. Both models capture shape memory behavior, linking segmental mobility and conformational relaxation of anisotropic chains to macroscopic recovery, and ...
Fathollah Varnik
wiley   +1 more source

Scale Mixture of Gaussian Modelling of Polarimetric SAR Data

EURASIP Journal on Advances in Signal Processing, 2010
This paper describes a flexible non-Gaussian statistical method used to model polarimetric synthetic aperture radar (POLSAR) data. We outline the theoretical basis of the well-know product model as described by the class of Scale Mixture models and ...
Anthony P. Doulgeris, Torbjørn Eltoft   +1 more
doaj   +1 more source

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

Advanced 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, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Triple Junctions as Dislocation‐Like Defects: The Role of Grain Boundary Crystallography Revealed by Experiment and Atomistic Simulation

Advanced Engineering Materials, EarlyView.
Grain boundary triple junctions are an essential ingredient of the microstructure of polycrystalline materials. In this study, a triple junction is observed using atomic‐resolution scanning transmission electron microscopy and characterized. Computer simulations reveal that the junction has a dislocation character that is determined by the joining ...
Tobias Brink, Saba Saood, Peter Schweizer, Jörg Neugebauer, Gerhard Dehm   +4 more
wiley   +1 more source

Product of Gaussian Mixture Diffusion Models

Journal of Mathematical Imaging and Vision
AbstractIn this work, we tackle the problem of estimating the density$$ f_X $$fXof a random variable$$ X $$Xby successive smoothing, such that the smoothed random variable$$ Y $$Yfulfills the diffusion partial differential equation$$ (\partial _t - \Delta _1)f_Y(\,\cdot \,, t) = 0 $$(∂t-Δ1)fY(·,t)=0with initial condition$$ f_Y(\,\cdot \,, 0) = f_X $$fY(
Zach, Martin, Kobler, Erich, Chambolle, Antonin, Pock, Thomas   +3 more
openaire   +3 more sources

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced 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, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Depth Data Reconstruction Based on Gaussian Mixture Model

Cybernetics and Information Technologies, 2016
Depth data is an effective tool to locate the intelligent agent in space because it accurately records the 3D geometry information on the surface of the scanned object, and is not affected by factors like shadow and light.
Li Zhe, Ma Chen, Zhang Tian-Fan
doaj   +1 more source

A Robust Gaussian Mixture Model for Mobile Robots’ Vision-based Pose Estimation [PDF]

Journal of Geodesy and Geoinformation Science, 2019
In dynamic environments, the moving landmarks can make the accuracy of traditional vision-based pose estimation worse or even failure. To solve this problem, a robust Gaussian mixture model for vision-based pose estimation is proposed.
Chuanqi CHENG,Xiangyang HAO,Jiansheng LI,Peng HU,Xu ZHANG
doaj   +1 more source