World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
10058
World Ranking
6148
National Ranking
46

Overview

Bo-Suk Yang is affiliated with Pukyong National University in South Korea. Their academic profile indicates a focus on research without specific details available on recent papers or documented research outputs.

No information is provided regarding frequent co-authors or collaborative research partnerships, suggesting that either such data is not recorded or their work may be conducted independently.

Similarly, there is no available data about commonly used publication venues, which leaves the venues of their research publications unspecified.

Details concerning fields and subfields of study are not listed, so the specific areas of scientific inquiry or specialization for Bo-Suk Yang cannot be determined from the current data.

The absence of main topics of work also means there is no detailed insight into the scientific themes or subjects addressed in their research.

There are no records of book publications or awards received, indicating either none have been documented or they have not been publicly acknowledged in the data provided.

This profile reflects all verifiable information available on Bo-Suk Yang as an active academic affiliated with Pukyong National University, without additional specifics on their research contributions, collaborations, or academic distinctions.

Best Publications

  • Support vector machine in machine condition monitoring and fault diagnosis

    Achmad Widodo;Bo-Suk Yang

  • Intelligent prognostics for battery health monitoring based on sample entropy

    Achmad Widodo;Min-Chan Shim;Wahyu Caesarendra;Bo-Suk Yang

  • Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors

    Achmad Widodo;Bo-Suk Yang;Tian Han

  • Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine

    Achmad Widodo;Eric Y. Kim;Jong-Duk Son;Bo-Suk Yang

  • Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors

    Achmad Widodo;Bo-Suk Yang

  • Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance

    Gang Niu;Gang Niu;Bo-Suk Yang;Michael G. Pecht;Michael G. Pecht

  • Application of relevance vector machine and logistic regression for machine degradation assessment

    Wahyu Caesarendra;Achmad Widodo;Bo-Suk Yang

  • Application of Dempster–Shafer theory in fault diagnosis of induction motors using vibration and current signals

    Bo-Suk Yang;Kwang Jin Kim

  • Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine

    Van Tung Tran;Hong Thom Pham;Bo-Suk Yang;Tan Tien Nguyen

  • Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

    Van Tung Tran;Bo-Suk Yang;Myung-Suck Oh;Andy Chit Chiow Tan

  • Random forests classifier for machine fault diagnosis

    Bo-Suk Yang;Xiao Di;Tian Han

  • Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines

    Bo-Suk Yang;Won-Woo Hwang;Dong-Jo Kim;Andy Chit Tan

  • Estimation and forecasting of machine health condition using ARMA/GARCH model

    Hong Thorn Pham;Bo-Suk Yang

  • Intelligent fault diagnosis of rotating machinery using infrared thermal image

    Ali M. D. Younus;Bo-Suk Yang

  • Multi-agent decision fusion for motor fault diagnosis

    Gang Niu;Tian Han;Bo-Suk Yang;Andy Chit Chiow Tan

  • Wavelet support vector machine for induction machine fault diagnosis based on transient current signal

    Achmad Widodo;Bo-Suk Yang

  • Development of an e-maintenance system integrating advanced techniques

    Tian Han;Bo-Suk Yang

  • Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis

    Bo-Suk Yang;Tian Han;Yong-Su Kim

  • VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table

    Bo-Suk Yang;Dong-Soo Lim;Andy Chit Chiow Tan

  • Machine health prognostics using survival probability and support vector machine

    Achmad Widodo;Bo-Suk Yang

  • Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine

    B K Choi;Dong-Sik Gu;Eric Kim;Joseph Mathew

Frequent Co-Authors

Gang Niu
Gang Niu Xi'an Jiaotong University
Kyoung Kwan Ahn
Kyoung Kwan Ahn University of Ulsan
Michael Pecht
Michael Pecht University of Maryland, College Park

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