| 1 |
Pseudo-nuclear staining of cells by deep learning improves the accuracy of automated cell counting in a label-free cellular population
Tsuzuki Y, Sanami S, Sugimoto K, Fujita S
Journal of Bioscience and Bioengineering, 131(2), 213, 2021 |
| 2 |
Online deep neural network-based feedback control of a Lutein bioprocess
Natarajan P, Moghadam R, Jagannathan S
Journal of Process Control, 98, 41, 2021 |
| 3 |
A hybrid deep learning and mechanistic kinetics model for the prediction of fluid catalytic cracking performance
Yang F, Dai CN, Tang JQ, Xuan J, Cao J
Chemical Engineering Research & Design, 155, 202, 2020 |
| 4 |
Maldistribution and dynamic liquid holdup quantification of quadrilobe catalyst in a trickle bed reactor using gamma-ray computed tomography: Pseudo-3D modelling and empirical modelling using deep neural network
Qi BB, Farid O, Uribe S, Al-Dahhan M
Chemical Engineering Research & Design, 164, 195, 2020 |
| 5 |
기후 변화 적응을 위한 벡터매개질병의 생태 모델 및 심층 인공 신경망 기반 공간-시간적 발병 모델링 및 예측
김상윤, 남기전, 허성구, 이선정, 최지훈, 박준규, 유창규
Korean Chemical Engineering Research, 58(2), 197, 2020 |
| 6 |
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
Lu RZ, Hong SH
Applied Energy, 236, 937, 2019 |
| 7 |
Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks
Li F, Ren GR, Lee J
Energy Conversion and Management, 186, 306, 2019 |
| 8 |
An image understanding based model with ion current signals for predicting combustion information
Deng Y, Gao ZQ, Tomita E, Wen Y
Fuel, 253, 1080, 2019 |
| 9 |
Prediction of clathrate hydrate phase equilibria using gradient boosted regression trees and deep neural networks
Song YC, Zhou H, Wang PF, Yang MJ
Journal of Chemical Thermodynamics, 135, 86, 2019 |
| 10 |
Energy saving estimation for plug and lighting load using occupancy analysis
Anand P, Cheong D, Sekhar C, Santamouris M, Kondepudi S
Renewable Energy, 143, 1143, 2019 |
