What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Control theory
Control theory, Artificial intelligence, Fault detection and isolation, Algorithm and Fault are his primary areas of study.
The Control theory research he does as part of his general Control theory study is frequently linked to other disciplines of science, such as Process control, therefore creating a link between diverse domains of science.
His studies in Artificial intelligence integrate themes in fields like Data mining and Pattern recognition.
Weihua Gui has included themes like Filter, Constant false alarm rate, Residual and Canonical correlation in his Fault detection and isolation study.
His work carried out in the field of Fault brings together such families of science as Recursive computation, Principal component analysis, Process and Sensitivity.
While the research belongs to areas of Deep learning, Weihua Gui spends his time largely on the problem of Feature extraction, intersecting his research to questions surrounding Restricted Boltzmann machine, Deep belief network, Raw data and Classifier.
His most cited work include:
- Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE (189 citations)
- Set stability and set stabilization of Boolean control networks based on invariant subsets (125 citations)
- Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms (123 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Control theory, Artificial intelligence, Mathematical optimization, Control theory and Pattern recognition.
His studies in Nonlinear system, Linear matrix inequality, Stability theory, Control system and Robust control are all subfields of Control theory research.
His work on Feature extraction, Deep learning and Feature as part of general Artificial intelligence study is frequently linked to Soft sensor, therefore connecting diverse disciplines of science.
His Deep learning study focuses on Autoencoder in particular.
Weihua Gui is studying Optimization problem, which is a component of Mathematical optimization.
His studies deal with areas such as Control and Bounded function as well as Control theory.
He most often published in these fields:
- Control theory (34.40%)
- Artificial intelligence (16.40%)
- Mathematical optimization (14.81%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (16.40%)
- Control theory (34.40%)
- Pattern recognition (10.02%)
In recent papers he was focusing on the following fields of study:
Weihua Gui mainly focuses on Artificial intelligence, Control theory, Pattern recognition, Nonlinear system and Mathematical optimization.
His work on Deep learning, Feature extraction, Autoencoder and Feature as part of general Artificial intelligence study is frequently linked to Soft sensor, bridging the gap between disciplines.
Control theory is often connected to Dynamic programming in his work.
In his research, Froth flotation is intimately related to Artificial neural network, which falls under the overarching field of Pattern recognition.
Weihua Gui works mostly in the field of Nonlinear system, limiting it down to topics relating to Algorithm and, in certain cases, Blast furnace, as a part of the same area of interest.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Multi-agent system, Reduction and Fuzzy logic.
Between 2018 and 2021, his most popular works were:
- A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network. (107 citations)
- Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy (55 citations)
- Stability and Set Stability in Distribution of Probabilistic Boolean Networks (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Control theory
Weihua Gui mainly investigates Artificial intelligence, Deep learning, Pattern recognition, Fault detection and isolation and Soft sensor.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Context, Machine learning and Computer vision.
His work in the fields of Autoencoder overlaps with other areas such as Component.
He works mostly in the field of Pattern recognition, limiting it down to topics relating to Feature and, in certain cases, Support vector machine.
His Fault detection and isolation research includes themes of Data mining and Canonical correlation.
He focuses mostly in the field of Compensation, narrowing it down to topics relating to Kiln and, in certain cases, Control theory.
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