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發表論文：
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Y.-S.
Lee and
J. Chen, Boosting Quality Prediction with
Reinforced Cyber-Physical Ensemble Learning: A Case Study of
Fed-Batch Bioprocesses,
Appl. Soft Comput., 2026.
(Accepted)
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Y.-S.
Lee and
J. Chen, A Novel Reinforced Incomplete Cyber-Physics Ensemble with Error Compensation Learning for Within-Batch Quality Prediction,
Adv. Eng. Inform., 65,
103172, 2025.
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Y.-S.
Lee and
J. Chen, A Robust Semi-Supervised Learning Scheme for Development of Within-Batch Quality Prediction Soft-Sensors,
Eng. Appl. Artif.,
133, 107920, 2024.
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S. K.
Ooi, Y.-S.
Lee and
J. Chen, Developing a Dynamic Quality Prediction Model for Limited Samples Target Grade Based on Transfer Learning,
Measurement,
229, 114380,
2024.
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Y.-S.
Lee and
J. Chen,
Developing Semi-Supervised Latent Dynamic Variational Autoencoders to Enhance Prediction Performance of Product Quality,Chem.
Eng. Sci., 265,
118192, 2023..
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Y.-S.
Lee and
J. Chen,
Using Source Data to Aid and Build Variational State-Space Autoencoders
with Sparse Target Data for Process Monitoring,
Neural Networks,
154, 455-468, 2022.
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Y. Zhang,
Y.-S. Lee, H. Lin, and J. Chen, Establishing Convolutional Neural Network Kalman Recurrent Variational Autoencoder Using Infrared Imaging for Process Monitoring: An Application in Spinning Disc Processes
IEEE
Trans. Instrum. Meas.,71,
5001712, 2022.
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Y.-S.
Lee and
J. Chen,
Augmenting Deviation of Faults from the Normal Using Fault Assistant Gaussian Mixture Prior Variational Autoencoder,
J .Taiwan Inst. Chem. Eng.,
130, 103921, 2022.
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X. Wu,
J. Chen, L. Xie, Y.-S. Lee, C.-I Chen and H. Su, Application
of Convolutional Neural Networks for Multi-Stage
Semiconductor Processes,
J. Chem. Eng. Japan, 54(8), 449-455, 2021.
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Z. Li, Y.-S. Lee, Junghui Chen, Y. Qian, Variable Moving Window PLS Models for Long-term NOx Emission Prediction of Coal-fired Power Plants,
Fuel, 296, 120441, 2021
- Y.-S. Lee and J. Chen, Enhancing Monitoring Performance of Data Sparse Nonlinear Processes through Information Sharing among Different Grades Using Gaussian Mixture Prior Variational Autoencoders,
Chemometer
Intell. Lab.,
208, 104219, 2021.
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Y.-S.
Lee,
S.-K. Ooi
and J. Chen,
A Novel Multi-Step Prediction Model for Process Monitoring,
the 2023 6th International Symposium on Advanced Intelligent
Informatics,
Sept. 21-22, 2023, Indonesia.
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Y.-S. Lee
and J. Chen,
Enhancing Monitoring Performance of Pharmaceutical Processes Using Dual-Attention Latent Dynamic Conditional State-Space Model,
13th Asian Control Conference (ASCC 2022), May 3-7, 2022,
Jeju,
Korea.(Virtual)
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Y.-S. Lee, O. S. Kit, D. Tanny
and J. Chen,
Maintaining Soft-Sensor Models Using Latent Dynamic Variational Autoencoders,
11th International Symposium on Advanced Control of Chemical
Processes (ADCHEM), June 13-16, 2021, Venice,
Italy. (Virtual)
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Y.-S.
Lee and J. Chen,
Boosting Monitoring Performance for Nonlinear Processes with Limited Samples Using Gaussian Mixture Latent Distribution in Variational Autoencoders,
9th The
International Symposium on Design, Operation & Control of
Chemical Processes (PSE Asia 2019), Nov. 4-6, 2020,
Taipei. (Virtual)
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L. L. T. Chan, Y.-S. Lee and J. Chen, Economic Model Predictive Control of CO2 Capture Process Retrofitted with Membrane Units, 6th International Symposium on Process Intensification, Nov. 7-8, 2018, Taipei, Taiwan.
(Virtual)
- Y.-S. Lee, O.-S. Kit, D. Tanny, L. L. T. Chan and J. Chen, Reducing Energy Cost of Absorber-Stripper CO2 Capture Process through Economic Neural Network Model Predictive Control, 6th International Symposium on Process Intensification, Nov. 7-8, 2018, Taipei, Taiwan.
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