Results 41 to 50 of about 110,162 (274)
DEEP NEURAL NETWORK-BASED APPROACH FOR COMPUTING SINGULAR VALUES OF MATRICES
Science Journal of University of ZakhoMatrix factorization techniques, such as Singular Value Decomposition (SVD), Eigenvalue Decomposition (EVD), and QR decomposition, have long been pivotal in computational mathematics, particularly for applications in signal processing, machine learning,
Diyari A. Hassan
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FEBS Open Bio, EarlyView.Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei +3 more
wiley +1 more source
Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN
Sensors, 2018 Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was
Chang Liu +3 more
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A signal denoising method for full-waveform LiDAR data [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 The lack of noise reduction methods resistant to waveform distortion can hamper correct and accurate decomposition in the processing of full-waveform LiDAR data.
M. Azadbakht +6 more
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International Journal of Adaptive Control and Signal Processing, EarlyView.This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
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, 2018 In this paper, we propose new randomization based algorithms for large scale linear discrete ill-posed problems with general-form regularization: ${\min} \|Lx\|$ subject to ${\min} \|Ax - b\|$, where $L$ is a regularization matrix.
Jia, Zhongxiao, Yang, Yanfei
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Advanced Materials Interfaces, EarlyView.A near‐infrared photosensitizer that facilitates efficient solid‐state photon upconversion by recycling triplets formed within a fullerene‐based donor–acceptor bulk‐heterojunction system is demonstrated. Spectroscopic investigations reveal that the energy of photogenerated charge transfer states of triplet character (3CT) is subsequently transferred to
Maciej Klein +4 more
wiley +1 more source
Weak peak identification of gamma spectrum based on singular value decomposition
He jishuBackgroundWhen performing gamma-ray spectroscopy analysis of samples with low levels of radioactive nuclide content, the weak peaks are difficult to be identified.PurposeThis study aims to propose a new method for identifying peaks in γ spectra by ...
CHEN Feng, ZHOU Jianbin, LIU Yi
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New SVD based initialization strategy for Non-negative Matrix Factorization
, 2014 There are two problems need to be dealt with for Non-negative Matrix Factorization (NMF): choose a suitable rank of the factorization and provide a good initialization method for NMF algorithms.
Qiao, Hanli
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Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning
Advanced Optical Materials, EarlyView.Hyperspectral photoluminescence data from graded MoxW1 − xS2 alloys and monolayer MoS2–WS2 lateral heterostructures are analyzed using unsupervised machine learning. The combined use of PCA, t‐SNE, and DBSCAN uncovers distinct excitonic regions that trace how composition, strain, and defects modulate optical responses in these 2D materials.
Maninder Kaur +4 more
wiley +1 more source