Jin Uk Ko
Orcid: 0000-0003-1009-1500
According to our database1,
Jin Uk Ko
authored at least 12 papers
between 2020 and 2024.
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Bibliography
2024
Gradient Alignment based Partial Domain Adaptation (GAPDA) using a domain knowledge filter for fault diagnosis of bearing.
Reliab. Eng. Syst. Saf., 2024
Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance.
Expert Syst. Appl., 2024
Latent space alignment based domain adaptation (LSADA) for fault diagnosis of rotating machinery.
Adv. Eng. Informatics, 2024
Spectrum-guided GAN with density-directionality sampling: Diverse high-fidelity signal generation for fault diagnosis of rotating machinery.
Adv. Eng. Informatics, 2024
2023
Frequency-learning generative network (FLGN) to generate vibration signals of variable lengths.
Expert Syst. Appl., October, 2023
Opt-TCAE: Optimal temporal convolutional auto-encoder for boiler tube leakage detection in a thermal power plant using multi-sensor data.
Expert Syst. Appl., April, 2023
Deep-learning-based fault detection and recipe optimization for a plastic injection molding process under the class-imbalance problem.
J. Comput. Des. Eng., March, 2023
2022
Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery.
Reliab. Eng. Syst. Saf., 2022
A new auto-encoder-based dynamic threshold to reduce false alarm rate for anomaly detection of steam turbines.
Expert Syst. Appl., 2022
2021
Multi-task learning of classification and denoising (MLCD) for noise-robust rotor system diagnosis.
Comput. Ind., 2021
Label-based, Mini-batch Combinations Study for Convolutional Neural Network Based Fluid-film Bearing Rotor System Diagnosis.
Comput. Ind., 2021
2020
Direct Connection-Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis of Rotor Systems.
IEEE Access, 2020