Liang Cao

Latest

A novel automated soft sensor design tool for industrial applications based on machine learning
Machine learning for real-time green carbon dioxide tracking in refinery processes
Comprehensive Analysis on Machine Learning Approaches for Interpretable and Stable Soft Sensors
Adaptive process monitoring for multimode industrial processes through machine learning
Green CO2 Emission Modelling with Artificial Intelligence During Co-Processing Biogenic Feedstocks
Data-driven battery capacity estimation using support vector regression and model bagging under fast-charging conditions
Stable Soft Sensor Modeling for Industrial Systems
Real-time tracking of renewable carbon content with AI-aided approaches during co-processing of biofeedstocks
Interpretable Industrial Soft Sensor Design Based on Informer and Shap
A Generalizable Method for Capacity Estimation and RUL Prediction in Lithium-Ion Batteries
Long short-term memory network with transfer learning for lithium-ion battery capacity fade and cycle life prediction
A GPU-Accelerated Moving-Horizon Algorithm for Training Deep Classification Trees on Large Datasets
Interpretable Soft Sensors using Extremely Randomized Trees and Shap
Large-scale process models using deep learning
Online capacity estimation of lithium-ion batteries by partial incremental capacity curve
Causal discovery based on observational data and process knowledge in industrial processes
Soft Sensor Change Point Detection and Root Causal Analysis
Tracking the green coke production when co-processing lipids at a commercial fluid catalytic cracker (FCC): combining isotope 14 C and causal discovery analysis
Data-driven dynamic inferential sensors based on causality analysis