| 1 |
Soft Sensor Modeling for Identifying Significant Process Variables with Time Delays
Hikosaka T, Aoshima S, Miyao T, Funatsu K
Industrial & Engineering Chemistry Research, 59(26), 12156, 2020 |
| 2 |
Formulation of the excess absorption in infrared spectra by numerical decomposition for effective process monitoring
Shibayama S, Kaneko H, Funatsu K
Computers & Chemical Engineering, 113, 86, 2018 |
| 3 |
Selective Use of Adaptive Models Considering the Prediction Efficiencies
Yuge N, Tanaka K, Kaneko H, Funatsu K
Industrial & Engineering Chemistry Research, 57(42), 14286, 2018 |
| 4 |
Practical Models for Predicting the Emission Peak Wavelengths of Inorganic Phosphors Based on Stoichiometric Information
Nakano H, Tanaka K, Miyao T, Funatsu K, Shirasawa R, Tomiya S
Chemistry Letters, 46(10), 1482, 2017 |
| 5 |
On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification
Escobar MS, Kaneko H, Funatsu K
Computers & Chemical Engineering, 98, 113, 2017 |
| 6 |
Improvement of Process State Recognition Performance by Noise Reduction with Smoothing Methods
Kaneko H, Funatsu K
Journal of Chemical Engineering of Japan, 50(6), 422, 2017 |
| 7 |
Ensemble locally weighted partial least squares as a just-in-time modeling method
Kaneko H, Funatsu K
AIChE Journal, 62(3), 717, 2016 |
| 8 |
Development of an Adaptive Experimental Design Method Based on Probability of Achieving a Target Range through Parallel Experiments
Nakao A, Kaneko H, Funatsu K
Industrial & Engineering Chemistry Research, 55(19), 5726, 2016 |
| 9 |
Combined generative topographic mapping and graph theory unsupervised approach for nonlinear fault identification
Escobar MS, Kaneko H, Funatsu K
AIChE Journal, 61(5), 1559, 2015 |
| 10 |
Combined Generative Topographic Mapping and Graph Theory Unsupervised Approach for Nonlinear Fault Identification (vol 61, pg 1559, 2015)
Escobar MS, Kaneko H, Funatsu K
AIChE Journal, 61(7), 2372, 2015 |
