Heonjun Yoon

Assistant Professor

Soongsil University

South Korea

Research Interests

Explore related searches

Professors in South Korea

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

Articles (14)

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Collaborators (3)

Soo-Ho Jo

Assistant Professor

Dongguk University

SOUTH KOREA

View details

Byeng D. Youn

Seoul National University

SOUTH KOREA

View details

Chan Hee Park

Seoul National University

SOUTH KOREA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Heonjun Yoon

Research Interests

Explore related searches

Contact this professor

Articles (14)

Revolution and peak discrepancy-based domain alignment method for bearing fault diagnosis under very low-speed conditions

PCDC: prototype-assisted dual-contrastive learning with depthwise separable convolutional neural network for few-shot fault diagnosis of permanent magnet synchronous motors under new operating conditions

FASER: Fault-affected signal energy ratio for fault diagnosis of gearboxes under repetitive operating conditions

Self-supervised feature learning for motor fault diagnosis under various torque conditions

Double piezoelectric defects in phononic crystals for ultrasonic transducers

Stator current operation compensation (SCOC): A novel preprocessing method for deep learning-based fault diagnosis of permanent magnet synchronous motors under variable operating conditions

MPARN: multi-scale path attention residual network for fault diagnosis of rotating machines

Probabilistic framework for reliable optimal design of gearboxes in general-purpose industrial robots considering random use conditions

A Noise-Robust Feature Extraction Method for Rolling Element Bearing Diagnosis: Linear Power-Normalized Cepstral Coefficients (LPNCC)

A Noise-Robust Feature Extraction Method for Rolling Element Bearing Diagnosis: Linear Power-Normalized Cepstral Coefficients (LPNCC)

Opt-TCAE: Optimal temporal convolutional auto-encoder for boiler tuber leakage detection in a thermal power plant using multi-sensor data

PERL: Probabilistic energy-ratio-based localization for boiler tube leaks using descriptors of acoustic emission signals

A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map

Design principle for effective mechanical boundary using a resonance band gap under elastic waves

Collaborators (3)

Soo-Ho Jo

Byeng D. Youn

Chan Hee Park

Make your next move.