Suchergebnisse - "Gaussian Process Regression algorithm"

Predicting specific wear rate of laser powder bed fusion AlSi10Mg parts at elevated temperatures using machine learning regression algorithm: Unveiling of microstructural morphology analysis

Autoren: Vijaykumar S. Jatti R. Murali Krishnan A. Saiyathibrahim et al.

Quelle: Journal of Materials Research and Technology, Vol 33, Iss, Pp 3684-3695 (2024)

Schlagwörter: Gaussian Process Regression algorithm, Specific Wear Rate, Mining engineering. Metallurgy, Machine Learning Regression Algorithm, Laser powder bed fusion, Specific wear rate, TN1-997, Laser Powder Bed Fusion, Machine learning regression algorithm, Prediction, MAE, RMSE

Zugangs-URL: https://doaj.org/article/3a2c2dfb50b6409594a00b9327819c0b

Rational Design of High-Energy-Density Polymer Composites by Machine Learning Approach

Autoren: Ming-Xiao Zhu Qiu-Cheng Yu Heng-Gao Song et al.

Quelle: ACS Applied Energy Materials. 4:1449-1458

Schlagwörter: High-Energy-Density Polymer Composites, Machine Learning Approach Rationally, Chemical Sciences not elsewhere classified, Gaussian process regression algorithm, Biophysics, 02 engineering and technology, permittivity, ML, 01 natural sciences, ε r, dielectric properties, filler doping scheme, U e, 0103 physical sciences, nanofiller, E b, 0210 nano-technology, Physical Sciences not elsewhere classified, Biological Sciences not elsewhere classified, design polymer composites

Zugangs-URL: https://pubs.acs.org/doi/10.1021/acsaem.0c02647

Rational Design of High-Energy-Density Polymer Composites by Machine Learning Approach

Autoren: Ming-Xiao Zhu (10022305) Qiu-Cheng Yu (10022308) Heng-Gao Song (10022311) et al.

Schlagwörter: Biophysics, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Physical Sciences not elsewhere classified, dielectric properties, permittivity, High-Energy-Density Polymer Composites, design polymer composites, ML, nanofiller, E b, Gaussian process regression algorithm, ε r, Machine Learning Approach Rationally, filler doping scheme, U e

Relation: https://figshare.com/articles/journal_contribution/Rational_Design_of_High-Energy-Density_Polymer_Composites_by_Machine_Learning_Approach/13627555

Verfügbarkeit: https://doi.org/10.1021/acsaem.0c02647.s001

Implementation of Machine Learning and Internal Temperature Sensors in Nail Penetration Testing of Lithium-ion Batteries

Autoren: Casey M Jones

Schlagwörter: Aerospace materials, Aerospace structures, Electrical energy storage, Energy generation, conversion and storage (excl. chemical and electrical), lithium-ion batteries (LIB), Raman Spectra AnalysisIn, incremental capacity (IC or dQ/dV), nail penetration test, Dynamic impacts, Gaussian process regression algorithm, state of health (SOH), thermal runaway, Internal Temperature Monitoring, battery safety

Relation: https://figshare.com/articles/thesis/Implementation_of_Machine_Learning_and_Internal_Temperature_Sensors_in_Nail_Penetration_Testing_of_Lithium-ion_Batteries/23503464

Verfügbarkeit: https://doi.org/10.25394/pgs.23503464.v1

Implementation of Machine Learning and Internal Temperature Sensors in Nail Penetration Testing of Lithium-ion Batteries

Autoren: Casey M Jones (9607445)

Schlagwörter: Aerospace materials, Aerospace structures, Electrical energy storage, Energy generation, conversion and storage (excl. chemical and electrical), lithium-ion batteries (LIB), Raman Spectra AnalysisIn, incremental capacity (IC or dQ/dV), nail penetration test, Dynamic impacts, Gaussian process regression algorithm, state of health (SOH), thermal runaway, Internal Temperature Monitoring, battery safety

Relation: https://figshare.com/articles/thesis/Implementation_of_Machine_Learning_and_Internal_Temperature_Sensors_in_Nail_Penetration_Testing_of_Lithium-ion_Batteries/23503464