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Found 15 papers, 2 papers with code
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Perspectives
This paper provides insights into deep reinforcement learning (DRL) for process control from the perspective of transfer learning.
Machine learning for industrial sensing and control: A survey and practical perspective
Soft sensing contains a wealth of industrial applications of statistical and machine learning methods.
Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control
The Organic Rankine Cycle (ORC) is widely used in industrial waste heat recovery due to its simple structure and easy maintenance.
Robust MPC with Zone Tracking
We propose a robust nonlinear model predictive control design with generalized zone tracking (ZMPC) in this work.
Estimation of minimum miscibility pressure (MMP) in impure/pure N2 based enhanced oil recovery process: A comparative study of statistical and machine learning algorithms
Most of the predictive models developed in this study exhibited superior performance over correlation and predictive models reported in literature.
Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging in vivo
The simulation results show that the acceleration-based method outperformed the non-acceleration-based method at different levels of acceleration and acquisition frame rates and achieved significant improvement in true positive rate (up to 10. 03%), false negative rate (up to 28. 61%) and correctly pairing fraction (up to 170. 14%).
Transthoracic super-resolution ultrasound localisation microscopy of myocardial vasculature in patients
Micro-vascular flow in the myocardium is of significant importance clinically but remains poorly understood.
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning.
A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation
A transferable multi-stage SOH estimation model is proposed to perform TL across batteries in the same stage, consisting of four steps.
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation
To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder.
A Generalized Probabilistic Monitoring Model with Both Random and Sequential Data
Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades.
Model predictive control of agro-hydrological systems based on a two-layer neural network modeling framework
To handle the tracking offset caused by the plant-model-mismatch of the proposed NN framework, a shrinking target zone is proposed for the ZMPC.
A comparative study of model approximation methods applied to economic MPC
Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for the next generation smart manufacturing.
Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control
Based on the distributed monitoring system, a two-level monitoring strategy is proposed to check different influences on process characteristics resulting from changes of the operating conditions and control action, and thus the two changes can be well distinguished from each other.
On-line Bayesian parameter estimation in general non-linear state-space models: A tutorial and new results
The simultaneous estimation is performed by filtering an extended vector of states and parameters using an adaptive sequential-importance-resampling (SIR) filter with a kernel density estimation method.
