Results 191 to 200 of about 14,298 (230)

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.
Najat Mohammad Nazari, Masdi Muhammad, Ainul Akmar Mokhtar   +2 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

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H-GPR: A Hybrid Strategy for Large-Scale Gaussian Process Regression

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
With the massive volume of data emerging from both scientific and industrial domains, it has become a desideratum to improve the scalability of Gaussian process regression (GPR). There are two major approaches to assuage its $\mathcal{O}\left( {{n^3}} \right)$ training complexity: the aggregation based methods and the sparse approximation methods. This
Naiqi Li, Yinghua Gao, Wenjie Li 0008, Yong Jiang 0001, Shu-Tao Xia   +4 more
openaire   +1 more source

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 S Alghamdi, Kemal Polat, Abdulrahman A Alshdadi   +2 more
exaly   +3 more sources

Determining kinetic parameters of cellulose and lignin pyrolysis by Gaussian process regression (GPR) method

2022
The ignition and flame-spread processes in the forest and urban fires involve the pyrolysis reactions of biomass materials. One of the most common methods for estimating the fire performance of a material is the evaluation of kinetic parameters, i.e., activation energy (𝐸), pre-exponential factor (𝐴), and reaction model (𝑓(𝛼)), from thermogravimetric ...
Viriya-amornkij, Pichayaporn, Kuwana, Kazunori   +1 more
openaire   +1 more source

ASS-GPR: Adaptive Sequential Sampling Method Based on Gaussian Process Regression for Reliability Analysis of Complex Geotechnical Engineering

International Journal of Geomechanics, 2021
Abstract Reliability analysis of complex geotechnical engineering is time-consuming since its performance function is highly nonlinear and implicit.
Mengyao Li, Gang Wang, Long Qian, Xiangpeng Li, Zhenyue Ma   +4 more
openaire   +1 more source

Determination of Friction Capacity of Driven Pile in Clay Using Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR)

Geotechnical and Geological Engineering, 2019
Friction capacity (fs) of driven pile in clay is key parameter for designing pile foundation. This study employs Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR) for determination of fs of driven piles in clay. GPR is a Bayesian nonparametric regression model. MPMR is a probabilistic model.
Pijush Samui
exaly   +2 more sources

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models

Environmental Science and Pollution Research, 2023
Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future ...
Ahmed Elbeltagi, Chaitanya B. Pande, Manish Kumar, Abebe Debele Tolche, Sudhir Kumar Singh, Akshay Kumar, Dinesh Kumar Vishwakarma   +6 more
openaire   +2 more sources

Drought Forecasting Using Gaussian Process Regression (GPR) and Empirical Wavelet Transform (EWT)-GPR in Gua Musang

2019
Drought forecasting is important in preparing for drought and its mitigation plan. This study focuses on the investigating the performance of Gaussian Process Regression (GPR) and Empirical Wavelet Transform-Gaussian Process Regression (EWT-GPR) in forecasting drought using Standard Precipitation Index (SPI).
Muhammad Akram Shaari, Ruhaidah Samsudin, Ani Shabri Ilman, Abdulsamad E. Yahya   +3 more
openaire   +1 more source

Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models

Journal of Cleaner Production, 2018
Abstract The objective of this study was to predict the irrigated and rainfed wheat output energy with three soft computing models include Artificial Neural Network (MLP and RBF models) and Gaussian Process Regression (GPR) for the first time, in Shahreza city, Isfahan province, Iran.
Morteza Taki, Abbas Rohani, Farshad Soheili-Fard, Abbas Abdeshahi   +3 more
openaire   +1 more source