What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Electrical engineering
- Statistics
Hongnian Yu mainly focuses on Artificial intelligence, Activity recognition, Machine learning, Control theory and Heuristic.
In the subject of general Artificial intelligence, his work in Feature extraction is often linked to Accelerometer, Home based and Achilles tendon, thereby combining diverse domains of study.
His Activity recognition research includes themes of Wearable computer, Data mining and Human–computer interaction.
He regularly ties together related areas like Control engineering in his Control theory studies.
His Control engineering research is multidisciplinary, relying on both Control, Bottleneck, Robotics and Degrees of freedom.
His Heuristic study incorporates themes from Best-first search, Beam search, Scheduling and Petri net.
His most cited work include:
- Elderly activities recognition and classification for applications in assisted living (215 citations)
- A Survey of Underactuated Mechanical Systems (150 citations)
- Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond (99 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Control theory, Robot, Control engineering, Artificial intelligence and Simulation.
Hongnian Yu combines subjects such as Control and Mobile robot with his study of Control theory.
His studies deal with areas such as Mechatronics, Mechanism and Haptic technology as well as Robot.
His Control engineering study combines topics in areas such as Control system, Real-time Control System, Cart and Robustness.
His Artificial intelligence research integrates issues from Machine learning, Computer vision and Pattern recognition.
His research in Underactuation intersects with topics in Pendulum and Trajectory.
He most often published in these fields:
- Control theory (29.56%)
- Robot (21.07%)
- Control engineering (19.81%)
What were the highlights of his more recent work (between 2016-2021)?
- Control theory (29.56%)
- Robot (21.07%)
- Artificial intelligence (15.09%)
In recent papers he was focusing on the following fields of study:
His main research concerns Control theory, Robot, Artificial intelligence, Underactuation and Knowledge management.
His Control theory study integrates concerns from other disciplines, such as Mechanism and Pendulum.
His Robot research includes elements of Control engineering, Mechatronics and Simulation.
The Activity recognition, Support vector machine and Feature selection research Hongnian Yu does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Accelerometer, therefore creating a link between diverse domains of science.
His work carried out in the field of Underactuation brings together such families of science as Robotic systems, Robotics, Efficient energy use and Trajectory.
His Knowledge management research incorporates elements of Supply chain and Hospitality.
Between 2016 and 2021, his most popular works were:
- Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond (99 citations)
- Smart manufacturing based on cyber-physical systems and beyond (61 citations)
- SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Electrical engineering
- Statistics
His primary areas of investigation include Control theory, Underactuation, Trajectory, Artificial intelligence and Knowledge management.
Hongnian Yu interconnects Multi-agent system and Moment in the investigation of issues within Control theory.
His study in Underactuation is interdisciplinary in nature, drawing from both Control variable, Tracking error, Robotics and Adaptive control.
The concepts of his Artificial intelligence study are interwoven with issues in Wearable computer and Computer vision.
The Nonlinear system study combines topics in areas such as Vibration, Mechatronics, Control theory, Energy and Robot.
His Activity recognition research incorporates themes from Deep learning and Feature learning.
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