(Top Award)

Task: CNC milling machine tool RUL prediction & optimal operating condition 

Method: 1) Adaptive labeling 2) Transformer  3) Physics-based curve fitting 4) Kernel density estimation based ensemble method

(Encouragement Award)

Task: Solar power generation ensemble prediction

Method: 1) Transformer  2) Kernel density estimation based ensemble method

(Rank: 2nd place)

Task: Anomaly detection & Fault diagnosis for spacecraft 

Method: 1) XGBoost  2) Rule-based model

(Rank: 1st place)

Task: Fault classification of a gear dataset

Method: 1) Inter-class maximum mean discrepancy (Metric learning)  2) Depth-wise 1-d convolutional neural network 3) Ensemble method

Presentation: 2023 / 6 / 6 (Montreal, Québec, Canada)

(Excellence Prize)

Task: Solar power generation prediction

Method: 1) Weighted Forcast Observatory  2) CNN-LSTM model 3) Ensemble-based Dynamic confidence interval

(Rank: 2nd place)

Task: Fault classification of a rock drill dataset

Method: 1) Physics-based cropping technique  2) Domain adaptation 3) Metric learning 4) Ensemble method

Presentation: 2022 / 11 / 08 (Nashville, Tennessee, United States of America)

(Encouragement Award)

Task 1: Image classification

Task 2: OCR Algorithm

Method: 1-1) Imbalanced weighting 1-2) Ensemble training 1-3)Bayseign Optimization

2) Transformation / Feature extraction / Sequence (Bidirectional LSTM) / Prediction

(Rank: 4th place)

 Task: Predict the remaining useful lifetime (RUL) for aircraft engines under conditions of high variability

Method: 1) augmentation through sliding window method 2) 1D CNN architecture 3) ensemble method