What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Programming language
His main research concerns Control theory, Observability, Supervisory control, Event and Control engineering.
His Observability study also includes
- Controllability which intersects with area such as Normality,
- Discrete event dynamic system and related Communications protocol.
Feng Lin merges many fields, such as Supervisory control and Supervisor, in his writings.
His Event research includes themes of Algorithm and Sublanguage.
His work carried out in the field of Algorithm brings together such families of science as Automaton and System identification.
His studies deal with areas such as Software system, Active Network Management, Feature and Automotive industry as well as Control engineering.
His most cited work include:
- On observability of discrete-event systems (676 citations)
- Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network (489 citations)
- Decentralized supervisory control of discrete-event systems (277 citations)
What are the main themes of his work throughout his whole career to date?
Feng Lin mainly focuses on Control theory, Artificial intelligence, Supervisory control, Control engineering and Event.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Computer vision and Pattern recognition.
His Supervisory control research is multidisciplinary, relying on both Automaton, Theoretical computer science, Observability and Decentralised system.
His Observability study combines topics from a wide range of disciplines, such as Event and Controllability.
The study incorporates disciplines such as Control system, Finite-state machine, State and Robustness in addition to Control engineering.
His research in Event intersects with topics in Control and Distributed computing.
He most often published in these fields:
- Control theory (22.87%)
- Artificial intelligence (17.05%)
- Supervisory control (17.88%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (17.05%)
- Real-time computing (9.15%)
- Computer vision (9.98%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Real-time computing, Computer vision, Event and Control theory.
His Real-time computing research is multidisciplinary, incorporating perspectives in Throughput and Motion planning.
His Event research incorporates themes from Automaton, Nondeterministic algorithm and Fuzzy logic.
His study in Control theory is interdisciplinary in nature, drawing from both Aerodynamics and Supervisory control.
The concepts of his Theoretical computer science study are interwoven with issues in Observability and Specification language.
His work deals with themes such as Control and Distributed computing, which intersect with Event.
Between 2016 and 2021, his most popular works were:
- Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network (489 citations)
- Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) (221 citations)
- Distributed Formation and Reconfiguration Control of VTOL UAVs (82 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Programming language
Feng Lin focuses on Real-time computing, Artificial intelligence, Observability, Supervisory control and Event.
His research on Real-time computing also deals with topics like
- Ultra-wideband that intertwine with fields like Latency,
- State together with Complex system, FIFO and Control system.
Feng Lin interconnects Deconvolution and Computer vision in the investigation of issues within Artificial intelligence.
His Observability study is associated with Control theory.
Feng Lin combines subjects such as Network partition, Power control, Overvoltage, Voltage regulation and Electronic engineering with his study of Control theory.
His study in Event is interdisciplinary in nature, drawing from both Control and Distributed computing.
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