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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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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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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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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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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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
doaj +1 more source
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
doaj +1 more source
Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression [PDF]
, 2018 This is the pre-peer reviewed version of the following article: Parivash Ashrafi, Yi Sun, Neil Davey, Roderick G. Adams, Simon C. Wilkinson, and Gary Patrick Moss, ‘Model fitting for small skin permeability data sets: hyperparameter optimisation in ...
Adams, Roderick +5 more
core +3 more sources
Data-Driven Discovery of Quaternary Ammonium Interlayers for Efficient and Thermally Stable Perovskite Solar Cells. [PDF]
Adv MaterAn active learning framework, grounded in independently generated in‐house experimental data, enables reliable discovery of high‐performance interfacial materials for perovskite solar cells. Iterative model refinement autonomously converges toward structurally robust quaternary ammonium architectures, establishing a new design principle for interfacial
Kim J +8 more
europepmc +2 more sources
Prediction of Atomization Energy Using Graph Kernel and Active Learning [PDF]
, 2019 Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management.
de Jong, Wibe A., Tang, Yu-Hang
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