An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction.
Sign Up to like & getrecommendations! Published in 2023 at "Journal of biophotonics"
DOI: 10.1002/jbio.202300031
Abstract: To alleviate the ill-posed of the inverse problem in Fluorescent molecular tomography (FMT), many regularization methods based on L2 or L1 norm have been proposed. Whereas, the quality of regularization parameters affect the performance of… read more here.
Keywords: regularization; parameter selection; parameter; reconstruction ... See more keywords
Parameter selection method for support vector machine based on adaptive fusion of multiple kernel functions and its application in fault diagnosis
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3792-7
Abstract: A new model parameter selection method for support vector machine based on adaptive fusion of multiple kernel functions is proposed in this paper. Characteristics of local kernels, global kernels, mixtures of kernels and multiple kernels… read more here.
Keywords: vector; multiple kernel; selection; fusion ... See more keywords
Consistent tuning parameter selection in high-dimensional group-penalized regression
Sign Up to like & getrecommendations! Published in 2019 at "Science China Mathematics"
DOI: 10.1007/s11425-017-9189-9
Abstract: Various forms of penalized estimators with good statistical and computational properties have been proposed for variable selection respecting the grouping structure in the variables. The attractive properties of these shrinkage and selection estimators, however, depend… read more here.
Keywords: parameter selection; consistent tuning; tuning parameter; selection ... See more keywords
Deep-Learning-Assisted Channel Estimation for Adaptive Parameter Selection in mMIMO-SEFDM
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2025.3554763
Abstract: This article introduces a massive multiple-input—multiple-output (mMIMO) system that utilizes spectrally efficient frequency division multiplexing (SEFDM) and incorporates a deep neural network (DNN) for enhanced SEFDM channel estimation. Unlike existing studies on DNN-based channel estimation,… read more here.
Keywords: sefdm; parameter selection; adaptive parameter; estimation ... See more keywords
A Control-Theoretic Approach to Analysis and Parameter Selection of Douglas–Rachford Splitting
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Control Systems Letters"
DOI: 10.1109/lcsys.2019.2922669
Abstract: Douglas–Rachford splitting and its equivalent dual formulation ADMM are widely used iterative methods in composite optimization problems arising in control and machine learning applications. The performance of these algorithms depends on the choice of step… read more here.
Keywords: tex math; parameter selection; analysis parameter; inline formula ... See more keywords
A Solution to the Parameter Selection and Current Static Error Issues With Frequency Shift Islanding Detection Methods
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Industrial Electronics"
DOI: 10.1109/tie.2020.2970684
Abstract: Frequency shift islanding detection methods have been widely used in inverter-based distributed generations. Two representatives of such methods, Sandia frequency shift (SFS) and reactive current perturbation (RCP) methods, are investigated in this article. The investigation… read more here.
Keywords: parameter selection; frequency shift; error; current static ... See more keywords
Optimum Parameter Selection for Accurate FDTD Simulations in Dispersive Media
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Magnetics"
DOI: 10.1109/tmag.2024.3446721
Abstract: This work aims at enhancing the accuracy of finite-difference time-domain (FDTD) models when dispersive media are involved. Focusing on the case of Lorentz dispersion, we propose the proper modification of the model’s constitutive parameters, so… read more here.
Keywords: accurate fdtd; dispersive media; parameter selection; optimum parameter ... See more keywords
LT-PEM Fuel Cells Diagnosis Based on EIS, Clustering, and Automatic Parameter Selection
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2023.3273084
Abstract: In the field of fuel cells, early detection of faulty conditions can significantly improve the lifetime. Then, signal analysis techniques such as electrochemical impedance spectroscopy combined with machine learning algorithms can generate a representation of… read more here.
Keywords: fuel; automatic parameter; fuel cells; parameter selection ... See more keywords
The modified generalized moveout approximation: a new parameter selection
Sign Up to like & getrecommendations! Published in 2017 at "Geophysical Prospecting"
DOI: 10.1111/1365-2478.12445
Abstract: ABSTRACT Non‐hyperbolic generalised moveout approximation is a powerful tool to approximate the travel‐time function by using information obtained from two rays. The standard approach for parameter selection is using three parameters defined from zero‐offset ray… read more here.
Keywords: ray; parameter; moveout approximation; parameter selection ... See more keywords
Defocusing parameter selection strategies based on PSF measurement for square-binary defocusing fringe projection profilometry.
Sign Up to like & getrecommendations! Published in 2018 at "Optics express"
DOI: 10.1364/oe.26.020351
Abstract: Three-dimensional (3D) shape measurement system with binary defocusing technique can perform high-speed and flexible measurements if binary fringe patterns are defocused by projector properly. However, the actual defocusing degree is difficult to set, and the… read more here.
Keywords: fringe; measurement; parameter selection; binary defocusing ... See more keywords
On the Adaptive Penalty Parameter Selection in ADMM
Sign Up to like & getrecommendations! Published in 2023 at "Algorithms"
DOI: 10.3390/a16060264
Abstract: Many data analysis problems can be modeled as a constrained optimization problem characterized by nonsmooth functionals, often because of the presence of ℓ1-regularization terms. One of the most effective ways to solve such problems is… read more here.
Keywords: penalty parameter; selection admm; parameter selection; parameter ... See more keywords