What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Jay Lee mainly investigates Prognostics, Reliability engineering, Control theory, Model predictive control and Control engineering.
His Prognostics study introduces a deeper knowledge of Data mining.
Jay Lee interconnects Reliability, Production and Electrical engineering in the investigation of issues within Reliability engineering.
His work in the fields of Control theory, such as Feed forward and Kalman filter, overlaps with other areas such as Economic optimization.
The Model predictive control study combines topics in areas such as Process control, Commercial software, Robust control and Optimal control.
His studies examine the connections between Control engineering and genetics, as well as such issues in Iterative learning control, with regards to Convergence.
His most cited work include:
- A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems (2035 citations)
- Model predictive control: past, present and future (1777 citations)
- Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment (871 citations)
What are the main themes of his work throughout his whole career to date?
Control theory, Artificial intelligence, Mathematical optimization, Prognostics and Reliability engineering are his primary areas of study.
His Control theory study integrates concerns from other disciplines, such as Control engineering, Model predictive control and System identification.
His study on Model predictive control is mostly dedicated to connecting different topics, such as Process control.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Data mining and Pattern recognition.
His Prognostics research includes themes of Systems engineering and Condition monitoring.
His study in Reliability engineering focuses on Predictive maintenance in particular.
He most often published in these fields:
- Control theory (13.44%)
- Artificial intelligence (11.32%)
- Mathematical optimization (11.17%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (11.32%)
- Deep learning (2.83%)
- Process engineering (8.06%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Deep learning, Process engineering, Fault and Mathematical optimization.
His work carried out in the field of Artificial intelligence brings together such families of science as Process and Pattern recognition.
His research on Process often connects related topics like Identification.
His Deep learning study combines topics from a wide range of disciplines, such as Domain, Domain adaptation, Test data and Condition monitoring.
Fault is closely attributed to Reliability engineering in his research.
Jay Lee does research in Mathematical optimization, focusing on Optimal control specifically.
Between 2018 and 2021, his most popular works were:
- A blockchain enabled Cyber-Physical System architecture for Industry 4.0 manufacturing systems (57 citations)
- Reinforcement Learning – Overview of recent progress and implications for process control (37 citations)
- Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Jay Lee mostly deals with Artificial intelligence, Deep learning, Convolutional neural network, Process engineering and Fault.
His Artificial intelligence study incorporates themes from Key and Pattern recognition.
His Convolutional neural network research integrates issues from Feature extraction, Real-time computing, Condition monitoring and Signal processing.
He combines subjects such as Prognostics, Benchmarking, Robustness and Quality monitoring with his study of Machine learning.
His work deals with themes such as Probability distribution, Kernel, Similarity, Probabilistic logic and Weibull distribution, which intersect with Prognostics.
His studies in Artificial neural network integrate themes in fields like Nonlinear control, Nonlinear system, Optimal control, Dynamic programming and State space.
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