activity
20212024
most citedDetermination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

30 citations · 34 across the 11 of their papers we have counts for

collaborators

11 papers

eess.SY2024

ReLU Surrogates in Mixed-Integer MPC for Irrigation Scheduling

Bernard T. Agyeman, Jinfeng Liu, Sirish L. Shah

Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effecti…

cs.LG202430 cited

Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

Vishnu Jayaprakash, Jae Bem You, Chiranjeevi Kanike +3

Accurate detection and analysis of traces of persistent organic pollutants in water is important in many areas, including environmental monitoring and food quality control, due to…

stat.AP2023

State estimation for one-dimensional agro-hydrological processes with model mismatch

Zhuangyu Liu, Jinfeng Liu, Shunyi Zhao +2

The importance of accurate soil moisture data for the development of modern closed-loop irrigation systems cannot be overstated. Due to the diversity of soil, it is difficult to ob…

eess.SY2023

Robust MPC with Zone Tracking

Zhiyinan Huang, Jinfeng Liu, Biao Huang

We propose a robust nonlinear model predictive control design with generalized zone tracking (ZMPC) in this work. The proposed ZMPC has guaranteed convergence into the target zone…

eess.SY2023

State estimation of a carbon capture process through POD model reduction and neural network approximation

Siyu Liu, Xunyuan Yin, Jinfeng Liu

This paper presents an efficient approach for state estimation of post-combustion CO2 capture plants (PCCPs) by using reduced-order neural network models. The method involves extra…

eess.SY2023

Control invariant set enhanced reinforcement learning for process control: improved sampling efficiency and guaranteed stability

Song Bo, Xunyuan Yin, Jinfeng Liu

Reinforcement learning (RL) is an area of significant research interest, and safe RL in particular is attracting attention due to its ability to handle safety-driven constraints th…