Publication

2024

21) T. H. Oh, J. W. Kim, Y. Kim, and J. M. Lee*, “Probability Density Function-based Stochastic Nonlinear Model Predictive Control using Fokker-Planck Equation,” Automatica (Under Review).

Before UNIST

20) T. H. Oh*, K. Kato, K. Sotowa, O. Tonomura “Bayesian Optimization with Search Space Movement for Cooling Crystallization Process”, Engineering Application in Artificial Intelligence (under review).

19) Y. Kim, and T. H. Oh*, “Model-based Safe Reinforcement Learning for Nonlinear Systems under Uncertainty with Constraints Tightening Approach,” Computers & Chemical Engineering, 2024.

18) T. H. Oh*, “Quantitative comparison of reinforcement learning and data-driven model predictive control for chemical and biological processes,” Computers & Chemical Engineering, vol. 181, pp. 108558, 2024.

17) X. Jiang, K. Sotowa*, S. Li, T. H. Oh, and O. Tonomura, “Continuous Preparation of Fe3O4 Nanoparticles with Narrow Size Distribution by Partial Oxidation Coprecipitation of F e2+ Ions in Microchannels,” Industrial & Engineering Chemistry Research, vol. 471, p. 144546, 2023.

16) X. Jiang, K. Sotowa*, O. Tonomura, and T. H. Oh, “Investigation of mass transfer in valve controlled gas–liquid segmented flow,” Chemical Engineering and Processing – Process Intensification, vol. 194, p. 109578, 2023.

15) X. Jiang, S. Li, K. Sotowa*, O. Tonomura, and T. H. Oh, “High throughput continuous synthesis of size-controlled nanoFe3O4 in segmented flow,” Chemical Engineering Journal, vol. 471, p. 144546, 2023.

14) T. H. Oh, S. Bae, J. W. Kim, Y. Kim, and J. M. Lee*, “Integrating Path Integral Control With Backstepping Control to Regulate Stochastic System,” International Journal of Control, Automation and Systems, vol. 21, pp. 2124–2138, 2023.

13) T. H. Oh, J. W. Kim, S. H. Son, D. H. Jeong, and J. M. Lee*, “Multi-strategy Control to Extend the Feasibility Region for Robust Model Predictive Control,” Journal of Process Control, vol. 116, pp. 25–33, 2022.

12) T. H. Oh, H. M. Park, J. W. Kim, and J. M. Lee*, “Integration of Reinforcement Learning and Model Predictive Control to Optimize Semi-batch Bioreactor,” AIChE Journal, vol. 68, no. 6, p. e17658, 2022.

11) J. W. Kim, T. H. Oh, S. H. Son, and J. M. Lee*, “Primal–dual Differential Dynamic Programming: A Model-based Reinforcement Learning for Constrained Dynamic Optimization,” Computers & Chemical Engineering, vol. 167, p. 108004, 2022.

10) S. H. Son, J. W. Kim, T. H. Oh, D. H. Jeong, and J. M. Lee*, “Learning of Model-plant Mis-match Map via Neural Network Modeling and Its Application to Offset-free Model Predictive Control,” Journal of Process Control, vol. 115, pp. 112-–122, 2022.

9) T. H. Oh, J. W. Kim, S. H. Son, H. Kim, K. Lee, and J. M. Lee*, “Automatic Control of Simulated Moving Bed Process with Deep Q-network,” Journal of Chromatography A, vol. 1647, p. 462073, 2021.

8) S. H. Son, B. J. Park, T. H. Oh, J. W. Kim, and J. M. Lee*, “Move Blocked Model Predictive Control with Guaranteed Stability and Improved Optimality using Linear Interpolation of Base Sequences,” International Journal of Control, vol. 94, no. 11, pp. 3213–3225, 2021.

7) J. W. Kim, B. J. Park, T. H. Oh, and J. M. Lee*, “Model-based Reinforcement Learning and Predictive Control for Two-stage Optimal Control of Fed-batch Bioreactor,” Computers & Chemical Engineering, vol. 154, p. 107465, 2021.

6) J. W. Kim, T. H. Oh, S. H. Son, D. H. Jeong, and J. M. Lee*, “Convergence Analysis of the Deep Neural Networks based Globalized Dual Heuristic Programming,” Automatica, vol. 122, p. 109222, 2020.

5) S. H. Son, T. H. Oh, J. W. Kim, and J. M. Lee*, “Move Blocked Model Predictive Control with Improved Optimality using Semi-explicit Approach for Applying Time-varying Blocking Structure,” Journal of Process Control, vol. 92, pp. 50-–61, 2020.

4) J. W. Kim, B. J. Park, H. Yoo, T. H. Oh, J. H. Lee, and J. M. Lee*, “A Model-based Deep Reinforcement Learning Method Applied to Finite-horizon Optimal Control of Nonlinear Control affine System,” Journal of Process Control, vol. 87, pp. 166–-178, 2020.

3) Y. Kim, T. H. Oh, T. Park, and J. M. Lee*, “Backstepping Control Integrated with Lyapunov-based Model Predictive Control,” Journal of Process Control, vol. 73, pp. 137-–146, 2019.

2) T. H. Oh, S.-K. Oh, H. Kim, K. Lee, and J. M. Lee*, “Transition Model for Simulated Moving Bed Under Nonideal Conditions,” Industrial & Engineering Chemistry Research, vol. 58, no. 47, pp. 21625–21640, 2019.

1) T. H. Oh, S.-K. Oh, H. Kim, K. Lee, and J. M. Lee*, “Conceptual Design of an Energy-efficient Process for Separating Aromatic Compounds from Naphtha with a High Concentration of Aromatic Compounds using 4-methyl-n-butylpyridinium Tetrafluoroborate Ionic Liquid,” Industrial & Engineering Chemistry Research, vol. 56, no. 25, pp. 7273–7284, 2017.