Results 31 to 40 of about 17,359 (256)

Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC

npj Computational Materials, 2023
Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.
Yu Xie, Jonathan Vandermause, Senja Ramakers, Nakib H. Protik, Anders Johansson, Boris Kozinsky   +5 more
doaj   +1 more source

Micro Doppler Reconstruction From Discontinuous Observations Based on Gapped SBL-FBTVAR Method for Spin Stabilized Object

IEEE Access, 2019
Micro Doppler analysis of spin stabilized objects is of a great significance for attitude estimation and recognition of space targets. In practice, the radar cannot dwell on one target in a long interval continuously.
Ling Hong, Fengzhou Dai, Xili Wang
doaj   +1 more source

Direction of arrival estimation under Class A modelled noise in shallow water using variational Bayesian inference method

IET Radar, Sonar & Navigation, 2022
The shallow water noise shows obvious impulsive property, which greatly degrades the direction of arrival (DOA) performance due to the conventional design concept based on the Gaussian assumption.
Xiao Feng, Xuebo Zhang, Ruiping Song, Junfeng Wang, Haixin Sun, Hamada Esmaiel   +5 more
doaj   +1 more source

EM-based parameter iterative approach for sparse Bayesian channel estimation of massive MIMO system

EURASIP Journal on Wireless Communications and Networking, 2017
One of the main challenges for a massive multi-input multi-output (MIMO) system is to obtain accurate channel state information despite the increasing number of antennas at the base station.
Sulin Mei, Yong Fang
doaj   +1 more source

Sparse Bayesian Learning via Stepwise Regression

CoRR, 2021
Sparse Bayesian Learning (SBL) is a powerful framework for attaining sparsity in probabilistic models. Herein, we propose a coordinate ascent algorithm for SBL termed Relevance Matching Pursuit (RMP) and show that, as its noise variance parameter goes to zero, RMP exhibits a surprising connection to Stepwise Regression.
Sebastian E. Ament, Carla P. Gomes
openaire   +3 more sources

Correlated Sparse Bayesian Learning for Recovery of Block Sparse Signals With Unknown Borders

IEEE Open Journal of Signal Processing
We consider the problem of recovering complex-valued block sparse signals with unknown borders. Such signals arise naturally in numerous applications. Several algorithms have been developed to solve the problem of unknown block partitions.
Didem Dogan, Geert Leus
doaj   +1 more source

Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)

Leida xuebao, 2016
Nowadays, high-speed sampling and transmission is a foremost challenge of radar system. In order to solve this problem, a compressive sensing approach is proposed for radar target signals in this study.
Zhong Jinrong, Wen Gongjian
doaj   +1 more source

Multi-emitters Direct Localization Method via Multi-dictionaries and Hierarchical Block Sparse Bayesian Framework

Leida xuebao, 2022
The direct position determination method based on compressed sensing depends on the accurate signal propagation model. With partially unknown propagation model parameters, its location performance will decline significantly.
Hongzhen YE, Haizhao GUO, Haoliang GUAN, Shunsheng ZHANG, Wenqin WANG   +4 more
doaj   +1 more source

Deep Bayesian Gaussian processes for uncertainty estimation in electronic health records

Scientific Reports, 2021
One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions.
Yikuan Li, Shishir Rao, Abdelaali Hassaine, Rema Ramakrishnan, Dexter Canoy, Gholamreza Salimi-Khorshidi, Mohammad Mamouei, Thomas Lukasiewicz, Kazem Rahimi   +8 more
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source