What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Statistics
His main research concerns Control theory, Linear system, Artificial intelligence, Mathematical optimization and Control system.
His Control theory study frequently draws connections between adjacent fields such as Control engineering.
His Linear system research is multidisciplinary, relying on both Projection, Linear matrix inequality, Discrete time and continuous time, Riccati equation and Adaptive control.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Algorithm, Computer vision and Pattern recognition.
Many of his research projects under Mathematical optimization are closely connected to Component, Efficient energy use and Model building with Component, Efficient energy use and Model building, tying the diverse disciplines of science together.
His Control system research is multidisciplinary, relying on both Spread spectrum, Subsequence, Lyapunov function, Impulse and Symmetric matrix.
His most cited work include:
- Robust Kalman filtering for uncertain discrete-time systems (445 citations)
- Brief Reliable H∞ controller design for linear systems (420 citations)
- Brief paper: Optimal linear estimation for systems with multiple packet dropouts (272 citations)
What are the main themes of his work throughout his whole career to date?
Yeng Chai Soh focuses on Control theory, Mathematical optimization, Linear system, Optics and Nonlinear system.
Control theory connects with themes related to Control engineering in his study.
Yeng Chai Soh studied Mathematical optimization and Discrete time and continuous time that intersect with Estimator.
His Linear system study combines topics from a wide range of disciplines, such as Exponential stability and Bounded function.
Yeng Chai Soh is interested in Sliding mode control, which is a branch of Nonlinear system.
The study incorporates disciplines such as Control and Adaptive control in addition to Robust control.
He most often published in these fields:
- Control theory (43.38%)
- Mathematical optimization (16.45%)
- Linear system (11.32%)
What were the highlights of his more recent work (between 2014-2020)?
- Mathematical optimization (16.45%)
- Artificial intelligence (10.26%)
- Extreme learning machine (5.56%)
In recent papers he was focusing on the following fields of study:
Mathematical optimization, Artificial intelligence, Extreme learning machine, Algorithm and Multi-agent system are his primary areas of study.
His work in the fields of Mathematical optimization, such as Optimization problem, intersects with other areas such as Convex function.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Pattern recognition.
His research investigates the connection with Algorithm and areas like Moving average which intersect with concerns in Probabilistic logic.
His Multi-agent system research incorporates themes from Linear matrix inequality, Control theory, Distributed computing and Scalar.
His Control theory research is multidisciplinary, incorporating elements of Control engineering and Integrator.
Between 2014 and 2020, his most popular works were:
- Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization (244 citations)
- Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes (155 citations)
- Smartphone Inertial Sensor-Based Indoor Localization and Tracking With iBeacon Corrections (104 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Control theory
His primary scientific interests are in Artificial intelligence, Mathematical optimization, Thermal comfort, Multi-agent system and Artificial neural network.
Yeng Chai Soh interconnects Machine learning, Data mining and Computer vision in the investigation of issues within Artificial intelligence.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Resource allocation, Inverse problem and Monotonic function.
The concepts of his Multi-agent system study are interwoven with issues in Information exchange, Strongly connected component, Distributed computing and Robust control.
His Linear matrix inequality study is associated with Control theory.
His Control theory study frequently draws connections between related disciplines such as Induction motor.
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