Results 1 to 10 of about 4,489 (189)

Evaluation and Prediction of Blast-Induced Ground Vibrations: A Gaussian Process Regression (GPR) Approach

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, Hajime Ikeda, Hisatoshi Toriya, Narihiro Owada, Tsuyoshi Adachi, Youhei Kawamura   +5 more
doaj   +4 more sources

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML). [PDF]

Smart Sustain Manuf Syst, 2017
ABSTRACT This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data.
Park J, Lechevalier D, Ak R, Ferguson M, Law KH, Lee YT, Rachuri S.   +6 more
europepmc   +5 more sources

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, Nasrin Heidarabadizadeh, Fahimeh Heravi   +2 more
doaj   +2 more sources

EVARS-GPR: EVent-Triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data [PDF]

Lecture Notes in Computer Science, 2021
Time series forecasting is a growing domain with diverse applications. However, changes of the system behavior over time due to internal or external influences are challenging. Therefore, predictions of a previously learned fore-casting model might not be useful anymore.
Dominik G Grimm, Florian Haßelbeck
exaly   +3 more sources

Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs. [PDF]

Sci Rep
AbstractSuspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications in building materials, textiles, and cooling systems. This study focuses on accurately predicting the dynamic viscosity, a critical thermophysical property, of suspensions containing MPCMs and
Hai T, Basem A, Alizadeh A, Sharma K, Jasim DJ, Rajab H, Ahmed M, Kassim M, Singh NSS, Maleki H.   +9 more
europepmc   +4 more sources

Gaussian process regression with physics-guided pseudo-sample augmentation for wear prediction under sparse measurements in milling [PDF]

Scientific Reports
Tool 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, Duc-Thuan Nguyen, Jong-Myon Kim   +2 more
doaj   +2 more sources

Prediction of Lubrication Oil Parameter Degradation to Extend the Oil Change Interval Based on Gaussian Process Regression (GPR)

Tribology Online, 2022
In this work, the degradation of selected lubrication oil parameters until the specified threshold is predicted based on Gaussian process regression (GPR) to extend the oil change interval. Kinematic viscosity (40°C) and total acid number (TAN) was selected based on Mahalanobis-Taguchi Gram-Schmidt (MTGS) analysis.
Ainul Akmar Mokhtar, Najat Mohammad Nazari   +1 more
exaly   +3 more sources

Drying temperature-dependent profile of bioactive compounds and prediction of antioxidant capacity of cashew apple pomace using coupled Gaussian Process Regression and Support Vector Regression (GPR–SVR) model

Heliyon, 2022
Crude extracts from cashew apple pomace (CAP) dried at different temperatures were used in High-Pressure Liquid Chromatography to quantify total alkaloids content (TAC), total flavanoids content (TFC), total saponin content (TSC) and total phenolics content (TPC).
Bobby Shekarau Luka
exaly   +4 more sources

Gaussian process regression (GPR) based non-invasive continuous blood pressure prediction method from cuff oscillometric signals

Applied Acoustics, 2020
Abstract Blood pressure measurement and continuous control are essential for heart and blood pressure patients. Therefore, continuous blood pressure measurement from these patients is required. In this paper, a novel hybrid prediction method combining Gaussian process regression (GPR) and feature extraction stage has been proposed and then applied to
Ahmed A Abd El-Latif, Abdulrahman A Alshdadi, Kemal Polat   +2 more
exaly   +3 more sources

Developing Gaussian process regression, Lasso regression, and Nu-support vector regression models for predicting solubility of exemestane in supercritical CO2 [PDF]

Scientific Reports
Precise 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
doaj   +2 more sources