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R. Bhushan Gopaluni
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2020 – today
- 2024
- [j22]Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni:
Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior. Autom. 164: 111642 (2024) - [j21]Prodromos Daoutidis
, Jay H. Lee
Machine learning in process systems engineering: Challenges and opportunities. Comput. Chem. Eng. 181: 108523 (2024) - [j20]Yupeng Li
Drilling Process Monitoring Based on Operation Mode Recognition and Dynamic Feature Extraction. IEEE Trans. Ind. Electron. 71(7): 7876-7885 (2024) - [c25]Liang Cao, Yankai Cao, R. Bhushan Gopaluni:
Stable Soft Sensor Modeling for Industrial Systems. ICPS 2024: 1-6 - [c24]Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni:
Deep Hankel matrices with random elements. L4DC 2024: 1579-1591 - [i17]Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim, Aditya Tulsyan, Faraz Amjad, Kai Wang, Benoît Chachuat, Jong Min Lee, Biao Huang, R. Bhushan Gopaluni:
Machine learning for industrial sensing and control: A survey and practical perspective. CoRR abs/2401.13836 (2024) - [i16]Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni:
Deep Hankel matrices with random elements. CoRR abs/2404.15512 (2024) - [i15]Shuyuan Wang, Jingliang Duan, Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni, Lixian Zhang:
Guiding Reinforcement Learning with Incomplete System Dynamics. CoRR abs/2410.16821 (2024) - 2023
- [j19]Jingyi Wang
Simultaneous digital twin identification and signal-noise decomposition through modified generalized sparse identification of nonlinear dynamics. Comput. Chem. Eng. 177: 108294 (2023) - [j18]Atefeh Daemi
Process Monitoring Using Domain-Adversarial Probabilistic Principal Component Analysis: A Transfer Learning Framework. IEEE Trans. Ind. Informatics 19(2): 1436-1444 (2023) - [i14]Tobi Michael Alabi, Nathan P. Lawrence
Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system. CoRR abs/2301.07768 (2023) - [i13]Nathan P. Lawrence
A modular framework for stabilizing deep reinforcement learning control. CoRR abs/2304.03422 (2023) - [i12]Shuyuan Wang, Philip D. Loewen, Nathan P. Lawrence
Reinforcement Learning with Partial Parametric Model Knowledge. CoRR abs/2304.13223 (2023) - [i11]Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni:
Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior. CoRR abs/2310.14098 (2023) - 2022
- [c23]Jingyi Wang
Time-Variant Digital Twin Modeling through the Kalman-Generalized Sparse Identification of Nonlinear Dynamics. ACC 2022: 5217-5222 - [c22]Liang Cao
Soft Sensor Change Point Detection and Root Causal Analysis. SICE 2022: 242-247 - [i10]Daniel G. McClement, Nathan P. Lawrence, Johan U. Backström, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni:
Meta Reinforcement Learning for Adaptive Control: An Offline Approach. CoRR abs/2203.09661 (2022) - [i9]Daniel G. McClement, Nathan P. Lawrence
Meta-Reinforcement Learning for Adaptive Control of Second Order Systems. CoRR abs/2209.09301 (2022) - [i8]R. Bhushan Gopaluni, Aditya Tulsyan, Benoît Chachuat, Biao Huang, Jong Min Lee, Faraz Amjad, Seshu Kumar Damarla, Jong Woo Kim, Nathan P. Lawrence
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey. CoRR abs/2209.11123 (2022) - [i7]Lim C. Siang, Shams Elnawawi, Lee D. Rippon, Daniel L. O'Connor, R. Bhushan Gopaluni:
Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics. CoRR abs/2211.06440 (2022) - 2021
- [j17]Lee D. Rippon
Representation learning and predictive classification: Application with an electric arc furnace. Comput. Chem. Eng. 150: 107304 (2021) - [i6]Daniel G. McClement, Nathan P. Lawrence
A Meta-Reinforcement Learning Approach to Process Control. CoRR abs/2103.14060 (2021) - [i5]Nathan P. Lawrence
Almost Surely Stable Deep Dynamics. CoRR abs/2103.14722 (2021) - [i4]Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen, Daniel G. McClement, Johan U. Backström, R. Bhushan Gopaluni:
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning. CoRR abs/2111.07171 (2021) - 2020
- [j16]Qiugang Lu, Michael G. Forbes, Philip D. Loewen
Support vector machine approach for model-plant mismatch detection. Comput. Chem. Eng. 133 (2020) - [j15]Hui Tian, Jagadeesan Prakash, Victor M. Zavala, James A. Olson, R. Bhushan Gopaluni:
A tractable approximation for stochastic MPC and application to mechanical pulping processes. Comput. Chem. Eng. 141: 106977 (2020) - [j14]Kai Wang
Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction. IEEE Trans. Ind. Informatics 16(12): 7233-7242 (2020) - [c21]Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni:
Almost Surely Stable Deep Dynamics. NeurIPS 2020 - [i3]Steven Spielberg, Aditya Tulsyan, Nathan P. Lawrence, Philip D. Loewen, R. Bhushan Gopaluni:
Deep Reinforcement Learning for Process Control: A Primer for Beginners. CoRR abs/2004.05490 (2020) - [i2]Nathan P. Lawrence
Reinforcement Learning based Design of Linear Fixed Structure Controllers. CoRR abs/2005.04537 (2020) - [i1]Nathan P. Lawrence
Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem. CoRR abs/2005.04539 (2020)
2010 – 2019
- 2019
- [j13]Kai Wang, Michael G. Forbes, R. Bhushan Gopaluni, Junghui Chen
Systematic Development of a New Variational Autoencoder Model Based on Uncertain Data for Monitoring Nonlinear Processes. IEEE Access 7: 22554-22565 (2019) - [j12]Qiugang Lu, Philip D. Loewen
Identification of symmetric noncausal processes. Autom. 103: 515-530 (2019) - [c20]Bryan Bohn, James A. Olson, R. Bhushan Gopaluni, Boris Stoeber:
Sensing Concept for Practical Performance-Monitoring of Centrifugal Pumps. IEEE SENSORS 2019: 1-4 - 2018
- [j11]Mahsa Sadeghassadi, Chris J. B. Macnab, R. Bhushan Gopaluni, David T. Westwick
Application of neural networks for optimal-setpoint design and MPC control in biological wastewater treatment. Comput. Chem. Eng. 115: 150-160 (2018) - [j10]Qiugang Lu
Locality preserving discriminative canonical variate analysis for fault diagnosis. Comput. Chem. Eng. 117: 309-319 (2018) - [j9]Aditya Tulsyan, Swanand R. Khare
A switching strategy for adaptive state estimation. Signal Process. 143: 371-380 (2018) - 2017
- [j8]David S. Zamar, R. Bhushan Gopaluni, Shahab Sokhansanj, Nathaniel K. Newlands
A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty. Comput. Chem. Eng. 97: 114-123 (2017) - [j7]Qiugang Lu, Michael G. Forbes, R. Bhushan Gopaluni, Philip D. Loewen
Performance Assessment of Cross-Directional Control for Paper Machines. IEEE Trans. Control. Syst. Technol. 25(1): 208-221 (2017) - [c19]Qiugang Lu, Lee D. Rippon
Noncausal modeling and closed-loop optimal input design for cross-directional processes of paper machines. ACC 2017: 2837-2842 - [c18]Koji Kitano, Manabu Kano
Fault identification with modified reconstruction-based contribution based on kernel principal component analysis. ASCC 2017: 1493-1498 - 2016
- [j6]Hong Jang, Kwang-Ki K. Kim, Richard D. Braatz
Regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical reaction networks. Comput. Chem. Eng. 90: 111-120 (2016) - [j5]Aditya Tulsyan, R. Bhushan Gopaluni, Swanand R. Khare
Particle filtering without tears: A primer for beginners. Comput. Chem. Eng. 95: 130-145 (2016) - [j4]O. Vahidi, K. E. Kwok, R. Bhushan Gopaluni, F. K. Knop
A comprehensive compartmental model of blood glucose regulation for healthy and type 2 diabetic subjects. Medical Biol. Eng. Comput. 54(9): 1383-1398 (2016) - [c17]Hui Tian, Qiugang Lu, R. Bhushan Gopaluni, Victor M. Zavala, James A. Olson:
Economic nonlinear model predictive control for mechanical pulping processes. ACC 2016: 1796-1801 - [c16]Aditya Tulsyan, R. Bhushan Gopaluni:
Robust model-based delay timer alarm for non-linear processes. ACC 2016: 2989-2994 - [c15]Melissa Barazandegan, K. Ezra Kwok, R. Bhushan Gopaluni:
Model-based detection of organ dysfunction and faults in insulin infusion devices for type 2 diabetic patients. ACC 2016: 3994-3999 - [c14]Hui Tian, Qiugang Lu, R. Bhushan Gopaluni, Victor M. Zavala:
Multiobjective economic model predictive control of mechanical pulping processes. CDC 2016: 4040-4045 - 2015
- [c13]Marcello Torchio, Nicolas A. Wolff, Davide Martino Raimondo, Lalo Magni
Real-time model predictive control for the optimal charging of a lithium-ion battery. ACC 2015: 4536-4541 - [c12]Qiugang Lu, Lee D. Rippon
Cross-directional controller performance monitoring for paper machines. ACC 2015: 4970-4975 - [c11]Mahdi Yousefi
Detecting model-plant mismatch without external excitation. ACC 2015: 4976-4981 - 2014
- [c10]Mahdi Yousefi
Sensitivity of MIMO controller performance to model-plant mismatch, with applications to paper machine control. CCA 2014: 204-209 - [c9]Mahdi Yousefi
Sensitivity of controller performance indices to model-plant mismatch: An application to paper machine control. ACC 2014: 3506-3511 - [c8]Kwang Ki Kevin Kim, Hong Jang, R. Bhushan Gopaluni, Jay H. Lee
Sparse identification in chemical master equations for monomolecular reaction networks. ACC 2014: 3698-3703 - 2013
- [j3]Aditya Tulsyan, Biao Huang, R. Bhushan Gopaluni, J. Fraser Forbes:
A Particle Filter Approach to Approximate Posterior Cramer-Rao Lower Bound: The Case of Hidden States. IEEE Trans. Aerosp. Electron. Syst. 49(4): 2478-2495 (2013) - [c7]Vinay A. Bavdekar, Jagdeesan Prakash, Sirish L. Shah, R. Bhushan Gopaluni:
Constrained dual ensemble Kalman filter for state and parameter estimation. ACC 2013: 3093-3098 - [c6]Bharatkumar Suthar, Venkatasailanathan Ramadesigan, Paul W. C. Northrop, R. Bhushan Gopaluni, Shriram Santhanagopalan, Richard D. Braatz, Venkat R. Subramanian:
Optimal control and state estimation of lithium-ion batteries using reformulated models. ACC 2013: 5350-5355 - 2011
- [c5]O. Vahidi, R. Bhushan Gopaluni, K. E. Kwok:
Detection of organ dysfunction in type II diabetic patients. ACC 2011: 4769-4774 - 2010
- [c4]Devin J. Marshman, Terrance Chmelyk, M. S. Sidhu, R. Bhushan Gopaluni, Guy Albert Dumont:
Energy optimization in a pulp and paper mill cogeneration facility. ACC 2010: 1229-1234 - [c3]O. Vahidi, K. E. Kwok, R. Bhushan Gopaluni, L. Sun:
Development of a physiological model forpatients with type 2 diabetes mellitus. ACC 2010: 2027-2032
2000 – 2009
- 2008
- [j2]Rogemar S. Mamon
Adaptive signal processing of asset price dynamics with predictability analysis. Inf. Sci. 178(1): 203-219 (2008) - 2006
- [c2]R. Bhushan Gopaluni, Philip D. Loewen, Mohammed Ammar, Guy Albert Dumont, Michael S. Davies:
Identification of Symmetric Noncausal Processes: Cross-Directional Response Modelling of Paper Machines. CDC 2006: 6744-6749 - 2003
- [j1]R. Bhushan Gopaluni, R. S. Patwardhan, S. L. Shah:
The nature of data pre-filters in MPC relevant identification - open- and closed-loop issues. Autom. 39(9): 1617-1626 (2003) - 2002
- [c1]R. Bhushan Gopaluni, R. S. Patwardhan, Sirish L. Shah:
Experiment design for MPC relevant identification. ACC 2002: 2713-2718
Coauthor Index
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