| 1 |
머신 러닝과 데이터 전처리를 활용한 증류탑 온도 예측
이예찬, 최영렬, 조형태, 김정환
Korean Chemical Engineering Research, 59(2), 191, 2021 |
| 2 |
Contrasting patterns and drivers of soil fungal communities in subtropical deciduous and evergreen broadleaved forests
Chen L, Xiang WH, Wu HL, Ouyang S, Lei PF, Hu YJ, Ge TD, Ye J, Kuzyakov Y
Applied Microbiology and Biotechnology, 103(13), 5421, 2019 |
| 3 |
A comparison of multiple methods for mapping local-scale mesquite tree aboveground biomass with remotely sensed data
Ku NW, Popescu SC
Biomass & Bioenergy, 122, 270, 2019 |
| 4 |
A data-centric predictive control approach for nonlinear chemical processes
Wang RG, Bao J, Yao YC
Chemical Engineering Research & Design, 142, 154, 2019 |
| 5 |
Ultra-high-aspect-ratio vertically aligned 2D MoS2-1D TiO2 nanobelt heterostructured forests for enhanced photoelectrochemical performance
Wei Z, Hsu CJ, Almakrami H, Lin GZ, Hu J, Jin XF, Agar E, Liu FQ
Electrochimica Acta, 316, 173, 2019 |
| 6 |
Long-term forecast of energy commodities price using machine learning
Herrera GP, Constantino M, Tabak BM, Pistori H, Su JJ, Naranpanawa A
Energy, 179, 214, 2019 |
| 7 |
TG-FTIR and Py-GC/MS analyses of pyrolysis behaviors and products of cattle manure in CO2 and N-2 atmospheres: Kinetic, thermodynamic, and machine-learning models
Zhang JH, Liu JY, Evrendilek F, Zhang XC, Buyukada M
Energy Conversion and Management, 195, 346, 2019 |
| 8 |
Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests
Assouline D, Mohajeri N, Scartezzini JL
Applied Energy, 217, 189, 2018 |
| 9 |
Data-driven model predictive control using random forests for building energy optimization and climate control
Smarra F, Jain A, de Rubeis T, Ambrosini D, D'Innocenzo A, Mangharam R
Applied Energy, 226, 1252, 2018 |
| 10 |
Random Forests for mapping and analysis of microkinetics models
Partopour B, Paffenroth RC, Dixon AG
Computers & Chemical Engineering, 115, 286, 2018 |
