New Publication in AIP Advances
Undergraduate researcher Jun Young Lee published a paper on long-term permanent magnet synchronous motor temperature prediction in AIP Advances.
Undergraduate researcher Jun Young Lee and colleagues published the paper Adaptive Sequence-to-Sequence Learning for Long-Term Permanent Magnet Synchronous Motor Temperature Prediction in AIP Advances.
The study proposes sequence-to-sequence learning methods for accurate and stable long-term prediction of permanent magnet temperature.
The proposed models incorporate adaptive normalization, probabilistic teacher forcing, and an attention mechanism to improve long-horizon prediction performance.
학부연구생 이준영(Jun Young Lee) 학생과 공동연구진의 논문 Adaptive Sequence-to-Sequence Learning for Long-Term Permanent Magnet Synchronous Motor Temperature Prediction이 AIP Advances에 게재되었습니다.
본 연구에서는 영구자석 동기모터(Permanent Magnet Synchronous Motor)의 장기 온도를 정확하고 안정적으로 예측하기 위한 Sequence-to-Sequence 기반 딥러닝 모델을 제안하였습니다.
제안한 방법은 Adaptive Normalization, Probabilistic Teacher Forcing, 그리고 Attention Mechanism을 결합하여 장기 예측 성능을 향상시켰습니다.