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The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
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  3. 2026-04 (Vol.75 No.4)
  4. 10.5370/KIEE.2026.75.4.917
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References

1 
T. Lee, H. Lee, P. Jang, Y. Hwang, K. Nam, 2021, Position Fault Detection for UAM Motor With Seamless Transition, IEEE Access, Vol. 9, pp. 168042-168051DOI
2 
S. Zhao, F. Blaabjerg, H. Wang, 2021, An Overview of Artificial Intelligence Applications for Power Electronics, IEEE Trans. Power Electron., Vol. 36, No. 4, pp. 4633-4658DOI
3 
M. Z. Ali, M. N. S. K. Shabbir, X. Liang, Y. Zhang, T. Hu, 2019, Machine Learning-Based Fault Diagnosis for Single- and Multi-Faults in Induction Motors Using Measured Stator Currents and Vibration Signals, IEEE Trans. on Ind. Applicat., Vol. 55, No. 3, pp. 2378-2391DOI
4 
F. B. Abid, M. Sallem, A. Braham, 2020, Robust Interpretable Deep Learning for Intelligent Fault Diagnosis of Induction Motors, IEEE Trans. Instrum. Meas., Vol. 69, No. 6, pp. 3506-3515DOI
5 
F. Xie, X. Tang, F. Xiao, Y. Luo, H. Shen, Z. Shi, 2023, Online Diagnosis Method for Open-Circuit Fault of NPC Inverter Based on 1D-DSCNN-GMP Lightweight Edge Deployment, IEEE J. Emerg. Sel. Topics Power Electron., Vol. 11, No. 6, pp. 6054-6067DOI
6 
S. Lu, Q. He, T. Yuan, F. Kong, 2017, Online Fault Diagnosis of Motor Bearing via Stochastic-Resonance-Based Adaptive Filter in an Embedded System, IEEE Trans. Syst. Man Cybern, Syst., Vol. 47, No. 7, pp. 1111-1122DOI
7 
T. Han, Y.-F. Li, 2022, Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles, Reliability Engineering & System Safety, Vol. 226, pp. 108648DOI
8 
T. Qiu, X. Wen, F. Zhao, 2016, Adaptive-Linear-Neuron-Based Dead-Time Effects Compensation Scheme for PMSM Drives, IEEE Trans. Power Electron., Vol. 31, No. 3, pp. 2530-2538DOI
9 
B. Anoch, L. Parthiban, 2025, Uncertainty-Aware AI for Enhanced Chronic Kidney Disease Diagnosis: A Review of Explainable and Reliable Models, pp. 1-6DOI
10 
K. Wickstrom, K. O. Mikalsen, M. Kampffmeyer, A. Revhaug, R. Jenssen, 2021, Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series, IEEE J. Biomed. Health Inform., Vol. 25, No. 7, pp. 2435-2444DOI
11 
J. Kafunah, M. I. Ali, J. G. Breslin, 2024, Uncertainty-Aware Ensemble Combination Method for Quality Monitoring Fault Diagnosis in Safety-Related Products, IEEE Trans. Ind. Inf., Vol. 20, No. 2, pp. 1975-1986DOI
12 
U. Sarawgi, 2021, Uncertainty-Aware Ensembling in Multi-Modal AI and its Applications in Digital Health for Neurodegenerative Disorders, M.S. thesisGoogle Search
13 
J. Shim, G. Cheol Lim, S. Lee, J.-I. Ha, 2025, Fault Diagnosis for Electric Drives Using Averagely Pooled and Downsampled Data Fusion on Embedded Systems, IEEE J. Emerg. Sel. Topics Power Electron., Vol. 13, No. 1, pp. 1224-1241DOI
14 
N. Ståhl, G. Falkman, A. Karlsson, G. Mathiason, 2020, Information Processing and Management of Uncertainty in Knowledge-Based Systems, Vol. 1237, pp. 556-568DOI
15 
A. Alqarafi, H. Batool, T. Abbas, J. I. Janjua, S. A. Ramay, M. Ahmed, 2024, Estimating Uncertainty in Deep Learning Methods and Applications, pp. 1-6DOI