What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Electrical engineering
Bo-Suk Yang focuses on Artificial intelligence, Support vector machine, Pattern recognition, Condition monitoring and Machine learning.
His Artificial intelligence research incorporates elements of Induction motor and Data mining.
His Support vector machine research includes elements of Fault, Feature extraction and Wavelet.
His Pattern recognition study integrates concerns from other disciplines, such as Dempster–Shafer theory, Incremental decision tree, Decision tree learning, Fuzzy classification and Degradation.
The study incorporates disciplines such as Manufacturing operations and Reliability engineering in addition to Condition monitoring.
His research investigates the connection with Machine learning and areas like Signal processing which intersect with concerns in Random forest and Intelligent decision support system.
His most cited work include:
- Support vector machine in machine condition monitoring and fault diagnosis (820 citations)
- Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors (247 citations)
- Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine (226 citations)
What are the main themes of his work throughout his whole career to date?
Bo-Suk Yang mainly investigates Artificial intelligence, Condition monitoring, Fault, Support vector machine and Bearing.
The concepts of his Artificial intelligence study are interwoven with issues in Induction motor, Machine learning, Data mining and Pattern recognition.
His Condition monitoring research is multidisciplinary, relying on both Acoustic emission, Artificial neural network, Vibration, Prognostics and Electronic engineering.
His Fault study integrates concerns from other disciplines, such as Control engineering and Embedded system.
As a member of one scientific family, he mostly works in the field of Support vector machine, focusing on Feature and, on occasion, Curse of dimensionality.
Bo-Suk Yang interconnects Control theory and Rotor in the investigation of issues within Bearing.
He most often published in these fields:
- Artificial intelligence (34.21%)
- Condition monitoring (30.92%)
- Fault (21.71%)
What were the highlights of his more recent work (between 2009-2016)?
- Condition monitoring (30.92%)
- Prognostics (10.53%)
- Artificial intelligence (34.21%)
In recent papers he was focusing on the following fields of study:
Bo-Suk Yang spends much of his time researching Condition monitoring, Prognostics, Artificial intelligence, Support vector machine and Data mining.
The concepts of his Condition monitoring study are interwoven with issues in Acoustic emission, Feature, Vibration, Real-time computing and Predictive maintenance.
The Prognostics study combines topics in areas such as Electronic engineering and Bearing.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Fault, Machine learning, Computer vision and Pattern recognition.
His research in Support vector machine tackles topics such as Reliability which are related to areas like Kurtosis, Failure rate and Weibull distribution.
His work on Decision tree is typically connected to Process and Nearest neighbour algorithm as part of general Data mining study, connecting several disciplines of science.
Between 2009 and 2016, his most popular works were:
- Intelligent prognostics for battery health monitoring based on sample entropy (217 citations)
- Application of relevance vector machine and logistic regression for machine degradation assessment (169 citations)
- Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance (168 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Electrical engineering
His main research concerns Prognostics, Condition monitoring, Support vector machine, Artificial intelligence and Data mining.
His Condition monitoring research incorporates themes from Reliability engineering, Real-time computing, Sensor fusion and Identification.
Bo-Suk Yang combines subjects such as Reliability, State, Reduction, Residual and Survival function with his study of Support vector machine.
Bo-Suk Yang has researched Artificial intelligence in several fields, including Autoregressive model, Autoregressive–moving-average model, Machine learning and Pattern recognition.
The Feature selection, Mahalanobis distance and Linear discriminant analysis research he does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Thermography, therefore creating a link between diverse domains of science.
His work deals with themes such as Smoothing, Artificial neural network and Bearing, which intersect with Data mining.
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