Jongmyon Kim
Jongmyon Kim
울산대 IT융합학부 교수
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Cited by
Cited by
A hybrid prognostics technique for rolling element bearings using adaptive predictive models
W Ahmad, SA Khan, JM Kim
IEEE Transactions on Industrial Electronics 65 (2), 1577-1584, 2017
Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis
M Kang, J Kim, JM Kim, ACC Tan, EY Kim, BK Choi
IEEE Transactions on Power Electronics 30 (5), 2786-2797, 2014
A hybrid feature model and deep-learning-based bearing fault diagnosis
M Sohaib, CH Kim, JM Kim
Sensors 17 (12), 2876, 2017
A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models
W Ahmad, SA Khan, MMM Islam, JM Kim
Reliability Engineering & System Safety 184, 67-76, 2019
Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines
MMM Islam, JM Kim
Reliability Engineering & System Safety 184, 55-66, 2019
Time-varying and multiresolution envelope analysis and discriminative feature analysis for bearing fault diagnosis
M Kang, J Kim, LM Wills, JM Kim
IEEE Transactions on Industrial Electronics 62 (12), 7749-7761, 2015
Fire flame detection in video sequences using multi-stage pattern recognition techniques
TX Truong, JM Kim
Engineering Applications of Artificial Intelligence 25 (7), 1365-1372, 2012
A hybrid feature selection scheme for reducing diagnostic performance deterioration caused by outliers in data-driven diagnostics
M Kang, MR Islam, J Kim, JM Kim, M Pecht
IEEE Transactions on Industrial Electronics 63 (5), 3299-3310, 2016
Automated irrigation system using solar power
J Uddin, SMT Reza, Q Newaz, J Uddin, T Islam, JM Kim
2012 7th International Conference on Electrical and Computer Engineering …, 2012
Adaptive ECG denoising using genetic algorithm-based thresholding and ensemble empirical mode decomposition
P Nguyen, JM Kim
Information sciences 373, 499-511, 2016
An effective four-stage smoke-detection algorithm using video images for early fire-alarm systems
TX Tung, JM Kim
Fire Safety Journal 46 (5), 276-282, 2011
Reduction of overfitting in diabetes prediction using deep learning neural network
A Ashiquzzaman, AK Tushar, MR Islam, D Shon, K Im, JH Park, DS Lim, ...
IT Convergence and Security 2017: Volume 1, 35-43, 2018
Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm
M Kang, J Kim, JM Kim
Information Sciences 294, 423-438, 2015
Overview of KSTAR initial operation
M Kwon, YK Oh, HL Yang, HK Na, YS Kim, JG Kwak, WC Kim, JY Kim, ...
Nuclear Fusion 51 (9), 094006, 2011
An FPGA-based multicore system for real-time bearing fault diagnosis using ultrasampling rate AE signals
M Kang, J Kim, JM Kim
IEEE Transactions on Industrial Electronics 62 (4), 2319-2329, 2014
Robust condition monitoring of rolling element bearings using de-noising and envelope analysis with signal decomposition techniques
P Nguyen, M Kang, JM Kim, BH Ahn, JM Ha, BK Choi
Expert Systems with Applications 42 (22), 9024-9032, 2015
Singular value decomposition based feature extraction approaches for classifying faults of induction motors
M Kang, JM Kim
Mechanical Systems and Signal Processing 41 (1-2), 348-356, 2013
Bearing fault diagnosis under variable speed using convolutional neural networks and the stochastic diagonal levenberg-marquardt algorithm
V Tra, J Kim, SA Khan, JM Kim
Sensors 17 (12), 2834, 2017
An overview of KSTAR results
JG Kwak, YK Oh, HL Yang, KR Park, YS Kim, WC Kim, JY Kim, SG Lee, ...
Nuclear Fusion 53 (10), 104005, 2013
A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems
DK Appana, R Islam, SA Khan, JM Kim
Information Sciences 418, 91-101, 2017
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