What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Statistics
Shen Yin mainly focuses on Control theory, Data-driven, Fault, Fault detection and isolation and Control engineering.
His Control theory study frequently draws connections between adjacent fields such as Fuzzy logic.
His Data-driven research is multidisciplinary, incorporating elements of Data modeling, Computation and Residual.
His research investigates the connection between Fault and topics such as Benchmark that intersect with issues in Industrial engineering and Statistical hypothesis testing.
His research in Fault detection and isolation tackles topics such as Data mining which are related to areas like Latent variable, Structure, Maintenance actions and Reliability engineering.
The various areas that he examines in his Control engineering study include Fault tolerance, Trajectory, Realization and Systems engineering.
His most cited work include:
- A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process (831 citations)
- A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process (831 citations)
- A Review on Basic Data-Driven Approaches for Industrial Process Monitoring (830 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Control theory, Fault detection and isolation, Nonlinear system, Data-driven and Control engineering.
His research ties Fuzzy logic and Control theory together.
His Fault detection and isolation study introduces a deeper knowledge of Fault.
His Nonlinear system research is multidisciplinary, relying on both Data modeling, Compensation, Artificial intelligence, Particle filter and Machine learning.
His work carried out in the field of Data-driven brings together such families of science as Subspace topology, Linear system, Disturbance, Identification and Kernel.
He has included themes like Control system, Process control, Fault tolerance and Realization in his Control engineering study.
He most often published in these fields:
- Control theory (45.83%)
- Fault detection and isolation (29.17%)
- Nonlinear system (22.92%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (45.83%)
- Nonlinear system (22.92%)
- Data-driven (21.67%)
In recent papers he was focusing on the following fields of study:
Shen Yin spends much of his time researching Control theory, Nonlinear system, Data-driven, Fault and Fault detection and isolation.
His Control theory research focuses on State and how it relates to Tracking error.
His Nonlinear system study integrates concerns from other disciplines, such as Artificial neural network and Function.
His Data-driven study combines topics from a wide range of disciplines, such as Wind power, Data mining, Control engineering, Process control and Robustness.
His Fault research integrates issues from Adaptive observer, Marine engineering, State and Measure.
The concepts of his Fault detection and isolation study are interwoven with issues in Robust statistics and Orthogonalization, Algorithm, Residual.
Between 2019 and 2021, his most popular works were:
- A Data-Driven Realization of the Control-Performance-Oriented Process Monitoring System (21 citations)
- Actuator and Sensor Fault Estimation for Time-Delay Markov Jump Systems With Application to Wheeled Mobile Manipulators (18 citations)
- Adaptive Fuzzy Fault-Tolerant Control for Markov Jump Systems With Additive and Multiplicative Actuator Faults (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Statistics
His primary areas of investigation include Control theory, Residual, Nonlinear system, Artificial intelligence and Process control.
In general Control theory study, his work on Actuator and Control theory often relates to the realm of Mobile manipulator, thereby connecting several areas of interest.
Many of his studies involve connections with topics such as Fault detection and isolation and Residual.
His study explores the link between Fault detection and isolation and topics such as Linear system that cross with problems in Fault.
His work investigates the relationship between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Signal processing and Information extraction.
The study incorporates disciplines such as Control engineering, Data-driven, Robustness and Transfer function in addition to Process control.
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