| 1 |
Mixture robust L1 probabilistic principal component regression and soft sensor application
Zhu PB, Yang XQ, Zhang H
Canadian Journal of Chemical Engineering, 98(8), 1741, 2020 |
| 2 |
기계학습 기반의 가스폭발위험범위 예측모델에 관한 연구
정용재, 이창준
Korean Chemical Engineering Research, 58(2), 248, 2020 |
| 3 |
Parallel quality-related dynamic principal component regression method for chemical process monitoring
Tao Y, Shi HB, Song B, Tan S
Journal of Process Control, 73, 33, 2019 |
| 4 |
Mass transfer study of water deoxygenation in a rotor-stator reactor based on principal component regression method
Zhao ZM, Wang JX, Sun BC, Arowo M, Shao L
Chemical Engineering Research & Design, 132, 677, 2018 |
| 5 |
Mixture semisupervised probabilistic principal component regression model with missing inputs
Sedghi S, Sadeghian A, Huang B
Computers & Chemical Engineering, 103, 176, 2017 |
| 6 |
Biomass Estimation in Pichia pastoris Cultures by Combined Single-Wavelength Fluorescence Measurements
Brunner V, Hussein M, Becker T
Biotechnology and Bioengineering, 113(11), 2394, 2016 |
| 7 |
Prediction of HVO content in HVO/diesel blends using FTIR and chemometric methods
Vrtiska D, Simacek P
Fuel, 174, 225, 2016 |
| 8 |
Mixture Semisupervised Principal Component Regression Model and Soft Sensor Application
Ge ZQ, Huang BA, Song ZH
AIChE Journal, 60(2), 533, 2014 |
| 9 |
Benchmarking energy performance of building envelopes through a selective residual-clustering approach using high dimensional dataset
Wang ED, Shen ZG, Grosskopf K
Energy and Buildings, 75, 10, 2014 |
| 10 |
The evaluation of sugar content and firmness of non-climacteric pears based on voltammetric electronic tongue
Wei ZB, Wang J
Journal of Food Engineering, 117(1), 158, 2013 |
