Nonlinear Channel Equalization based on Gaussian Processes for Regression in Fiber Link
Guangtongxin yanjiu, 2022 In order to mitigate the effect of nonlinear noise nonlinear Channel Equalizer (CE) based on Gaussian Processes for Regression (GPR) is proposed and experimentally demonstrated in an intensity modulation and direct detection fiber link.
WU Biao, LI Jia-hao, ZHANG Zhao-cai
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Machine learning techniques for monitoring the sludge profile in a secondary settler tank
Applied Water Science, 2019 The aim of this paper is to evaluate and compare the performance of two machine learning methods, Gaussian process regression (GPR) and Gaussian mixture models (GMMs), as two possible methods for monitoring the sludge profile in a secondary settler tank (
Jesús Zambrano +2 more
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Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture. [PDF]
PLoS ONE, 2016 The remaining useful life (RUL) prediction of Lithium-ion batteries is closely related to the capacity degeneration trajectories. Due to the self-charging and the capacity regeneration, the trajectories have the property of multimodality.
Lingling Li +4 more
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mgpr: An R package for multivariate Gaussian process regression
SoftwareX, 2023 Gaussian process regression (GPR) is a non-parametric kernel-based machine learning method. GPR is based on Bayesian formalism, which enables the estimation of prediction uncertainty of the response variables.
Petri Varvia +2 more
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Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall
Engineering Applications of Computational Fluid Mechanics, 2020 In this study, two kernel-based models were used which include Support Vector Regression (SVR) and Gaussian Process Regression (GPR) and were compared with two tree-based models that are M5 and Random Forest (RF) for estimating missing monthly ...
Mohammad Taghi Sattari +4 more
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Rectangularization of Gaussian process regression for optimization of hyperparameters
Machine Learning with Applications, 2023 Gaussian process regression (GPR) is a powerful machine learning method which has recently enjoyed wider use, in particular in physical sciences. In its original formulation, GPR uses a square matrix of covariances among training data and can be viewed ...
Sergei Manzhos, Manabu Ihara
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Energy Reports, 2022 In order to optimize productin of biodiesel from waste cooking oil utilizing Fe-exchanged montmorillonite 12 K10 (Fe-MMT K10) heterogeneous catalyst was applied in this work.
Abdulaziz Ibrahim Almohana +7 more
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Nonlinear Channel Equalization Using Gaussian Processes Regression in IMDD Fiber Link
IEEE Photonics Journal, 2022 Gaussian processes regression (GPR)-aided nonlinear channel equalizer (CE) is experimentally demonstrated in a multi-level intensity modulation and direct detection fiber link.
Xiang Li +4 more
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On a Robust MaxEnt Process Regression Model with Sample-Selection
Entropy, 2018 In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt) process regression model that assumes a MaxEnt prior ...
Hea-Jung Kim, Mihyang Bae, Daehwa Jin
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Design of Amorphous Carbon Coatings Using Gaussian Processes and Advanced Data Visualization
Lubricants, 2022 In recent years, an increasing number of machine learning applications in tribology and coating design have been reported. Motivated by this, this contribution highlights the use of Gaussian processes for the prediction of the resulting coating ...
Christopher Sauer +5 more
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