What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Control theory
Control theory, Fault detection and isolation, Algorithm, Filter and Nonlinear system are his primary areas of study.
His Control theory research incorporates elements of Stability and Network packet.
He has researched Fault detection and isolation in several fields, including Reliability engineering, Principal component analysis, Artificial intelligence and Residual.
His Algorithm study combines topics in areas such as Prognostics and Expectation–maximization algorithm.
The Prognostics study combines topics in areas such as Data-driven and Econometrics.
The study incorporates disciplines such as Kalman filter, Covariance, Discrete time and continuous time and Probabilistic logic in addition to Filter.
His most cited work include:
- Remaining useful life estimation - A review on the statistical data driven approaches (1062 citations)
- Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process (315 citations)
- Total projection to latent structures for process monitoring (274 citations)
What are the main themes of his work throughout his whole career to date?
Donghua Zhou mainly focuses on Control theory, Fault detection and isolation, Nonlinear system, Algorithm and Reliability engineering.
His Control theory research incorporates themes from Control engineering and Network packet.
His Network packet research integrates issues from Model predictive control and Stability.
The Fault detection and isolation study which covers Artificial intelligence that intersects with Machine learning.
The Algorithm study combines topics in areas such as Covariance and Filter.
His work deals with themes such as Iterative learning control and Actuator, which intersect with Control theory.
He most often published in these fields:
- Control theory (58.40%)
- Fault detection and isolation (39.28%)
- Nonlinear system (16.02%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (58.40%)
- Fault detection and isolation (39.28%)
- Algorithm (17.31%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Control theory, Fault detection and isolation, Algorithm, Discrete time and continuous time and Data mining.
The concepts of his Control theory study are interwoven with issues in Estimator and Intermittent fault.
The study incorporates disciplines such as Reliability engineering, Control limits and Air brake in addition to Fault detection and isolation.
His Algorithm research includes elements of Key and Markov chain.
His Discrete time and continuous time study also includes fields such as
- Lyapunov function and related Synchronization, Exponential stability and Stability,
- Complex network together with Actuator fault, Linear matrix, Node, Filtering problem and Filter,
- Topology which connect with Distributed filtering and Wireless sensor network.
The various areas that Donghua Zhou examines in his Nonlinear system study include State and Explained sum of squares.
Between 2019 and 2021, his most popular works were:
- Quasi-Synchronization of Discrete-Time Lur’e-Type Switched Systems With Parameter Mismatches and Relaxed PDT Constraints (65 citations)
- Moving Horizon Estimation With Unknown Inputs Under Dynamic Quantization Effects (48 citations)
- Moving Horizon Estimation of Networked Nonlinear Systems With Random Access Protocol (31 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Control theory
Donghua Zhou mostly deals with Control theory, Fault detection and isolation, Estimator, Algorithm and Linear system.
In his works, Donghua Zhou undertakes multidisciplinary study on Control theory and Symmetric matrix.
His research in Fault detection and isolation intersects with topics in Reliability engineering and Air brake.
Donghua Zhou combines subjects such as Scheduling, Bounded function and Moving horizon estimation with his study of Estimator.
His study in Moving horizon estimation is interdisciplinary in nature, drawing from both Artificial neural network, Nonlinear system, Markov chain and Random access.
His work in the fields of Algorithm, such as Singular value decomposition, intersects with other areas such as Sensor node.
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