Results 41 to 50 of about 14,298 (230)

Quantifying Dynamic Regulation in Metabolic Pathways with Nonparametric Flux Inference [PDF]

, 2019
One of the central tasks in systems biology is to understand how cells regulate their metabolism. Hierarchical regulation analysis is a powerful tool to study this regulation at the metabolic, gene-expression, and signaling levels.
He, Fei, Stumpf, Michael P H
core   +1 more source

A Comparative Study of Using Adaptive Neural Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), and SMRGT Models in Flow Coefficient Estimation

3C Tecnología_Glosas de innovación aplicadas a la pyme, 2023
Estimating the flow coefficient is a crucial hydrologic process that plays a significant role in flood forecasting, water resource planning, and flood control. Accurate prediction of the flow coefficient is essential to prevent flood-related losses, manage flood warning systems, and control water flow.
Ruya mehdi, Ayse Yeter GUNAL
openaire   +1 more source

A novel method for identifying geomechanical parameters of rock masses based on a PSO and improved GPR hybrid algorithm

Scientific Reports, 2022
In view of the shortcomings of existing artificial neural network (ANN) and support vector regression (SVR) in the application of three-dimensional displacement back analysis, Gaussian process regression (GPR) algorithm is introduced to make up for the ...
Hanghang Yan, Kaiyun Liu, Chong Xu, Wenbo Zheng   +3 more
doaj   +1 more source

State-of-Health Prediction For Lithium-Ion Batteries With Multiple Gaussian Process Regression Model

IEEE Access, 2019
State-of-health (SOH) prediction for lithium-ion batteries is a challenging and important topic in the modern industry. With the advent of cloud-connected devices, there are huge amounts of the battery degradation trend data available.
Xueying Zheng, Xiaogang Deng
doaj   +1 more source

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Tunnel geomechanical parameters prediction using Gaussian process regression

Machine Learning with Applications, 2021
The purpose of this study is to apply a modern intelligent method of Gaussian process regression (GPR) to predict the geological parameter of Rock Quality Designation (RQD) along the tunnel route. This method can also be used for any geological parameter
Arsalan Mahmoodzadeh, Mokhtar Mohammadi, Hawkar Hashim Ibrahim, Tarik Ahmed Rashid, Adil Hussain Mohammed Aldalwie, Hunar Farid Hama Ali, Ako Daraei   +6 more
doaj   +1 more source

Gaussian process single-index models as emulators for computer experiments

, 2011
A single-index model (SIM) provides for parsimonious multi-dimensional nonlinear regression by combining parametric (linear) projection with univariate nonparametric (non-linear) regression models.
Gramacy, Robert B., Lian, Heng
core   +1 more source

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

Power Prediction of Photovoltaic Power Generation Based on Improved Gaussian Process Regression Modeling

Taiyuan Ligong Daxue xuebao
Purposes The proportion of photovoltaic(PV) power generation has been increasing in China. PV power generation is greatly affected by meteorological factors and its output power shows strong intermittency and volatility because of the complexity and ...
XIAO Chun, HAO Junbo, YANG Xiaoxia, HAN Xiaoqing   +3 more
doaj   +1 more source

Non-Linear Dimensionality Reduction and Gaussian Process Based Classification Method for Smoke Detection

IEEE Access, 2017
To improve smoke detection accuracy, we combine local binary pattern (LBP) like features, kernel principal component analysis (KPCA), and Gaussian process regression (GPR) to propose a novel data processing pipeline for smoke detection.
Feiniu Yuan, Xue Xia, Jinting Shi, Hongdi Li, Gang Li   +4 more
doaj   +1 more source