Results 31 to 40 of about 31,027 (249)

Split-and-Combine Singular Value Decomposition for Large-Scale Matrix

Journal of Applied Mathematics, 2013
The singular value decomposition (SVD) is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, high- dimensional data visualization, dimension reduction, data mining, latent semantic analysis,
Jengnan Tzeng
doaj   +1 more source

Feature extraction of vibration signal of roadheader based on singular value decompositio

Gong-kuang zidonghua, 2019
In view of difficulty of dynamic load identification of roadheader, feature extraction method of vibration signal of roadheader based on singular value decomposition was proposed.
ZHANG Linfeng, TIAN Muqin, SONG Jiancheng, HE Ying, FENG Junling, YANG Xiang   +5 more
doaj   +1 more source

Analysing the significance of small conformational changes and low occupancy states in serial crystallographic data

FEBS Open Bio, EarlyView.
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill, Yelyzaveta Pulnova, Elke De Zitter, Helen M. Ginn, Briony A. Yorke   +4 more
wiley   +1 more source

Singular Value Decomposition Wavelength-Multiplexing Ghost Imaging

Photonics
To enhance imaging quality, singular value decomposition (SVD) has been applied to single-wavelength ghost imaging (GI) or color GI. In this paper, we extend the application of SVD to wavelength-multiplexing ghost imaging (WMGI) for reducing the ...
Yingtao Zhang, Xueqian Zhang, Zongguo Li, Hongguo Li   +3 more
doaj   +1 more source

Singular Value Decomposition and Ligand Binding Analysis

Journal of Spectroscopy, 2013
Singular values decomposition (SVD) is one of the most important computations in linear algebra because of its vast application for data analysis. It is particularly useful for resolving problems involving least-squares minimization, the determination of
André Luiz Galo, Márcio Francisco Colombo   +1 more
doaj   +1 more source

Multidimensional Profiling of MRI‐Negative Temporal Lobe Epilepsy Uncovers Distinct Phenotypes

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Although hippocampal sclerosis (TLE‐HS) represents the most frequent cause of temporal lobe epilepsy (TLE), up to 30% of patients show no lesion on visual MRI inspection (TLE‐MRIneg). These cases pose diagnostic and therapeutic challenges and are underrepresented in surgical series.
Alice Ballerini, Alessia Casarini, Niccolò Biagioli, Laura Mirandola, Daniela Ballotta, Paul Summers, Simona Scolastico, Laura Madrassi, Maurilio Genovese, Marcella Malagoli, Gaetano Cantalupo, Giada Giovannini, Matteo Pugnaghi, Niccolò Orlandi, Laura Tassi, Valeria Cuccarini, Domenico Aquino, Elena Tartara, Fulvia Palesi, Giuseppe Didato, Paolo Vitali, the 3TLE Study Group, Roberta Di Giacomo, Fabio Doniselli, Federica Mazzi, Carlo Andrea Galimberti, Claudia A. M. Gandini Wheeler‐Kingshott, Stefano Meletti, Anna Elisabetta Vaudano   +28 more
wiley   +1 more source

Rafting of Ni‐Based Superalloys Under Multiaxial Load as Understood by Phase‐Field Simulations and Critical Experiments

Advanced Engineering Materials, EarlyView.
Phase‐field simulations coupled with dislocation‐density‐based crystal plasticity modeling reproduce γ′ rafting behavior in single‐crystal Ni‐based superalloys under varied loading conditions. The model captures both macroscopic creep and microscopic morphology evolution, with results matching high‐temperature creep experiments.
Micheal Younan, David Bürger, Gunther Eggeler, Oleg Shchyglo, Ingo Steinbach, Muhammad Adil Ali   +5 more
wiley   +1 more source

Differentiable Singular Value Decomposition

CoRR
Singular value decomposition is widely used in modal analysis, such as proper orthogonal decomposition and resolvent analysis, to extract key features from complex problems. SVD derivatives need to be computed efficiently to enable the large scale design optimization.
Rohit Kanchi, Sicheng He
openaire   +2 more sources

Additive Manufacturing of Continuous Fibre Reinforced Composites: Process, Characterisation, Modelling, and Sustainability

Advanced Engineering Materials, EarlyView.
Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley   +1 more source

Singular Value Decomposition

Theoretical and Natural Science
Singular Value Decomposition (SVD) is a very important matrix factorization technique in linear algebra which generalizes the eigenvalue decomposition to both non square and non symmetric matrices. This report explains the theoretical foundation of SVD by numerical examples and the comparison of SVD with eigenvalue decomposition on the basis of ...
Peter Zizler, Roberta La Haye
  +6 more sources