His primary scientific interests are in Artificial intelligence, Pattern recognition, Support vector machine, Control theory and Linear system. When carried out as part of a general Artificial intelligence research project, his work on Probabilistic logic, Relevance vector machine, Feature and Feature extraction is frequently linked to work in Electroencephalography, therefore connecting diverse disciplines of study. His Pattern recognition study integrates concerns from other disciplines, such as Error detection and correction, Benchmark and Artifact.
The Support vector machine study which covers Artificial neural network that intersects with Estimation theory, Exponential stability and Upper and lower bounds. His research in Control theory is mostly concerned with Overhead crane. His Linear system research is multidisciplinary, relying on both Dynamic programming, Bounded function, Applied mathematics and Nonlinear system.
The scientist’s investigation covers issues in Control theory, Linear system, Mathematical optimization, Model predictive control and Support vector machine. His research in Control theory tackles topics such as Bounded function which are related to areas like Domain and Robust control. The various areas that Chong Jin Ong examines in his Linear system study include Numerical stability, Computation, Invariant and Applied mathematics.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Stability and Function. His Support vector machine research incorporates elements of Artificial neural network and Data set. His Pattern recognition research is multidisciplinary, incorporating elements of Probabilistic logic and Data mining.
The scientist’s investigation covers issues in Linear system, Mathematical optimization, Nonlinear system, Applied mathematics and Control theory. His research in Linear system intersects with topics in Algorithm, Computation and Discrete time and continuous time. His Discrete time and continuous time study combines topics from a wide range of disciplines, such as Distributed model predictive control and Optimal control.
His work in the fields of Mathematical optimization, such as Optimization problem, intersects with other areas such as Constraint. In Optimization problem, Chong Jin Ong works on issues like Exponential stability, which are connected to Model predictive control, Nonlinear control and Stability theory. With his scientific publications, his incorporates both Control theory and Transient.
Chong Jin Ong mostly deals with Linear system, Control theory, Computation, Optimization problem and Mathematical optimization. His Linear system research incorporates themes from Linear programming, Algorithm and Strongly connected component. His biological study spans a wide range of topics, including Artificial neural network and Tracking.
His Computation study integrates concerns from other disciplines, such as Quadratic equation, Class, Invariant, Applied mathematics and Linear matrix. His studies in Optimization problem integrate themes in fields like Discrete time and continuous time, Exponential stability and Model predictive control. His Discrete time and continuous time research is multidisciplinary, incorporating perspectives in Distributed model predictive control and Optimal control.
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Spiral microchannel with rectangular and trapezoidal cross-sections for size based particle separation
Guofeng Guan;Lidan Wu;Ali Asgar S. Bhagat;Zirui Li;Zirui Li.
Scientific Reports (2013)
Parallel sequential minimal optimization for the training of support vector machines
L. J. Cao;S. S. Keerthi;Chong-Jin Ong;J. Q. Zhang.
IEEE Transactions on Neural Networks (2006)
EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate
Kai-Quan Shen;Xiao-Ping Li;Chong-Jin Ong;Shi-Yun Shao.
Clinical Neurophysiology (2008)
Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction
Shi-Yun Shao;Kai-Quan Shen;Chong Jin Ong;E. Wilder-Smith.
IEEE Transactions on Biomedical Engineering (2009)
A Feature Selection Method for Multilevel Mental Fatigue EEG Classification
Kai-Quan Shen;Chong-Jin Ong;Xiao-Ping Li;Zheng Hui.
IEEE Transactions on Biomedical Engineering (2007)
Growth distances: new measures for object separation and penetration
Chong Jin Ong;E.G. Gilbert.
international conference on robotics and automation (1996)
Bayesian support vector regression using a unified loss function
Wei Chu;S.S. Keerthi;Chong Jin Ong.
IEEE Transactions on Neural Networks (2004)
Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay
Dan Zhang;Li Yu;Qing-Guo Wang;Chong-Jin Ong.
IEEE Transactions on Neural Networks (2012)
Device for laparoscopic or thoracoscopic surgery
Ah San Pang;Chong Jin Ong;Chee Kong Chui.
An improved conjugate gradient scheme to the solution of least squares SVM
Wei Chu;Chong Jin Ong;S.S. Keerthi.
IEEE Transactions on Neural Networks (2005)
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