Gaussian process regression with physics-guided pseudo-sample augmentation for wear prediction under sparse measurements in milling [PDF]
Scientific ReportsTool wear prediction is essential to ensure machining quality and sustainability. Hybrid physics-data Gaussian process regression (GPR) methods integrate domain knowledge with data-driven learning, but a fundamental challenge remains due to an inherent ...
Hai-Phong Nguyen +2 more
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Mining, 2023 Ground vibration is one of the most hazardous outcomes of blasting. It has a negative impact both on the environment and the human population near to the blasting area.
Yewuhalashet Fissha +5 more
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Developing Gaussian process regression, Lasso regression, and Nu-support vector regression models for predicting solubility of exemestane in supercritical CO2 [PDF]
Scientific ReportsPrecise estimation of pharmaceutical solubility in supercritical carbon dioxide (scCO2) is essential for optimizing pharmaceutical applications, including particle size reduction, the development of solid dispersions, and controlled-release formulations.
Jawza A. Almutairi, Thamir Malik
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Intelligence modeling of solubility of raloxifene and density of solvent for green supercritical processing of medicines for enhanced solubility [PDF]
Scientific ReportsIn this study, a dataset for solubility of raloxifene and CO2 density was analyzed using different regression models to reveal the correlation between inputs and drug solubility via supercritical processing.
Hashem O. Alsaab, Yusuf S. Althobaiti
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Gaussian Process Regression Model for Damage Localization in Plates Based on Modal Data [PDF]
Journal of Rehabilitation in Civil Engineering, 2022 The applications of plate like structures in different fields of engineering are increasing. In this paper, a new damage detection method investigated based on Gaussian process regression model (GPR).
Seyed Sina Kourehli
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Gaussian Process Regression (GPR) for Auto-Estimation of Resilient Modulus of Stabilized Base Materials [PDF]
Journal of Soft Computing in Civil Engineering, 2021 The resilient modulus of different pavement materials is one of the most important parameters for the pavement design using the mechanistic-empirical (M-E) method.
Ali Reza Ghanizadeh +2 more
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Yantu gongcheng xuebao, 2023 In order to establish an optimal model for estimating the uniaxial compressive strength (UCS) of rocks as well as its reasonable estimation, a fully Bayesian Gaussian process regression method (fB-GPR) is proposed by combining the Gaussian process ...
SONG Chao , ZHAO Tengyuan , XU Ling
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Soft sensor based on Gaussian process regression and its application in erythromycin fermentation process [PDF]
Chemical Industry and Chemical Engineering Quarterly, 2016 Erythromycin fermentation process is a typical microbial fermentation process. Soft sensors can be used to estimate biomass of Erythromycin fermentation process for their relative low cost, simple development, and ability to predict difficult-to-
Mei Congli +5 more
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Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression
Engineering Applications of Computational Fluid Mechanics, 2021 Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named ...
Shahab S. Band +7 more
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Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm
Applied Sciences, 2021 Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process.
Mahdi S. Alajmi, Abdullah M. Almeshal
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