What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Zhu Liang Yu spends much of his time researching Artificial intelligence, Robustness, Control theory, Speech recognition and Brain–computer interface.
His Artificial intelligence research incorporates themes from Simulation and Pattern recognition.
His study focuses on the intersection of Robustness and fields such as Beamforming with connections in the field of Quadratic programming, Robust control and Constrained optimization.
His Control theory research is multidisciplinary, incorporating elements of Variational inequality, Rate of convergence and Signal-to-interference-plus-noise ratio.
His work carried out in the field of Speech recognition brings together such families of science as Mutual coherence and Signal reconstruction.
His study in Brain–computer interface is interdisciplinary in nature, drawing from both Neurofeedback and Computer vision.
His most cited work include:
- An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential (208 citations)
- A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control (198 citations)
- Adaptive noise cancelling microphone system (180 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Speech recognition, Algorithm and Robustness.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Brain–computer interface and Computer vision.
In the field of Brain–computer interface, his study on Motor imagery overlaps with subjects such as Graphical user interface.
His work focuses on many connections between Pattern recognition and other disciplines, such as Voxel, that overlap with his field of interest in Functional magnetic resonance imaging.
He interconnects Genetic algorithm, Mathematical optimization, Signal and Wideband in the investigation of issues within Algorithm.
Robustness is a subfield of Control theory that Zhu Liang Yu tackles.
He most often published in these fields:
- Artificial intelligence (40.00%)
- Pattern recognition (27.06%)
- Speech recognition (18.24%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (40.00%)
- Pattern recognition (27.06%)
- Brain–computer interface (11.76%)
In recent papers he was focusing on the following fields of study:
Zhu Liang Yu focuses on Artificial intelligence, Pattern recognition, Brain–computer interface, Feature extraction and Speech recognition.
His Artificial intelligence research focuses on Deep learning, Artificial neural network, Prior probability, Reinforcement learning and Sequence learning.
His studies deal with areas such as Supervised learning, Relevance and Selection as well as Pattern recognition.
Within one scientific family, Zhu Liang Yu focuses on topics pertaining to Event-related potential under Brain–computer interface, and may sometimes address concerns connected to Posterior probability.
His biological study spans a wide range of topics, including Segmentation, Feature, Detector, Magnetic resonance imaging and Discriminative model.
His research integrates issues of Object, Cognitive neuroscience of visual object recognition and Visualization in his study of Speech recognition.
Between 2018 and 2021, his most popular works were:
- MMAN: Multi-modality aggregation network for brain segmentation from MR images (16 citations)
- An Online Interactive Paradigm for P300 Brain–Computer Interface Speller (15 citations)
- An EOG-Based Human–Machine Interface to Control a Smart Home Environment for Patients With Severe Spinal Cord Injuries (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary scientific interests are in Artificial intelligence, Pattern recognition, Feature extraction, Robot and Machine learning.
His Artificial intelligence study integrates concerns from other disciplines, such as Control system, Automatic control and Optimal control.
His Pattern recognition study incorporates themes from Selection and Bayesian inference.
His Feature extraction research is multidisciplinary, relying on both Magnetic resonance imaging, Segmentation and Discriminative model.
His Robot research is multidisciplinary, incorporating perspectives in Recurrent neural network, Quadratic programming, Mathematical optimization, Control theory and Robustness.
The study incorporates disciplines such as QRS complex and Sequence learning in addition to Machine learning.
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