tuvovan / YeongHyeon

 

Google Scholar

Papers

SCIE

• [2022] Efficient Non-Compression Auto-Encoder for Driving Noise-Based Road Surface Anomaly Detection ✏️ First Author
• [2020] Anomaly Detection in Particulate Matter Sensor using Hypothesis Pruning Generative Adversarial Network ✏️ First Author
• [2020] The CNN-based Coronary Occlusion Site Localization with Effective Preprocessing Method ✏️ First Author
• [2019] Preprocessing Method for Performance Enhancement in CNN-based STEMI Detection from 12-lead ECG ✏️ First Author
• [2019] Arrhythmia detection in electrocardiogram based on recurrent neural network encoder–decoder with Lyapunov exponent ✏️ First Author
• [2018] Fast Adaptive RNN Encoder–Decoder for Anomaly Detection in SMD Assembly Machine ✏️ First Author

International Conference

• [2022] Attention! Is Recycling Artificial Neural Network Effective for Maintaining Renewable Energy Efficiency? ✏️ First Author
• [2021] Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise ✏️ First Author
• [2021] Design of Cost-Effective Auto-Encoder for Electric Motor Anomaly Detection in Resource Constrained Edge Device ✏️ First Author

Domestic Conference

• [2022] Latent Vector Expansion using Autoencoder for Anomaly Detection
• [2022] Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection ✏️ First Author
• [2022] Attention 기반의 이상 부위 자동 labeling 기법 (Attention-based Automatic Labeling Method for Anomaly Area)
• [2021] Anomaly Detection Based on Multiple-Hypothesis Autoencoder
• [2021] Self-Weighted Ensemble Method to Adjust the Influence of Individual Models based on Reliability ✏️ First Author

arXiv

• [2018] Comparison of RNN Encoder-Decoder Models for Anomaly Detection ✏️ First Author

Repositories

Repositories  
│
├── TensorFlow 
│    ├── Publications (Sorted by year in ascending order)
│    │    ├── Preprocessing Method for Performance Enhancement in CNN-based STEMI Detection from 12-lead ECG
│    │    │    ├── IEEE Access (2019): https://ieeexplore.ieee.org/abstract/document/8771175
│    │    │    └── Source: https://github.com/YeongHyeon/Preprocessing-Method-for-STEMI-Detection
│    │    ├── Arrhythmia detection in electrocardiogram based on recurrent neural network encoder–decoder with Lyapunov exponent
│    │    │    ├── IEEJ (2018): https://onlinelibrary.wiley.com/doi/abs/10.1002/tee.22927
│    │    │    └── Source: https://github.com/YeongHyeon/Arrhythmia_Detection_RNN_and_Lyapunov
│    │    └── Fast Adaptive RNN Encoder–Decoder for Anomaly Detection in SMD Assembly Machine
│    │         ├── MDPI (2018): https://www.mdpi.com/1424-8220/18/10/3573
│    │         └── Source: https://github.com/YeongHyeon/FARED_for_Anomaly_Detection
│    │  
│    ├── Discriminative Model
│    │    ├── Series Inception
│    │    │    ├── Inception: https://github.com/YeongHyeon/Inception_Simplified-TF2
│    │    │    └── XCeption: https://github.com/YeongHyeon/XCeption-TF2
│    │    ├── Series Residual
│    │    │    ├── ResNet: https://github.com/YeongHyeon/ResNet-TF2
│    │    │    ├── ResNeXt: https://github.com/YeongHyeon/ResNeXt-TF2
│    │    │    ├── WRN: https://github.com/YeongHyeon/WideResNet_WRN-TF2
│    │    │    ├── ResNeSt: https://github.com/YeongHyeon/ResNeSt-TF2
│    │    │    └── ReXNet: https://github.com/YeongHyeon/ReXNet-TF2
│    │    ├── Series Bayesian / Gaussian
│    │    │    └── SWA-Gaussian: https://github.com/YeongHyeon/SWA-Gaussian-TF2
│    │    ├── Series Graph
│    │    │    └── PIPGCN: https://github.com/YeongHyeon/PIPGCN-TF2
│    │    └── Ohters
│    │         ├── SE-Net: https://github.com/YeongHyeon/SENet-Simple
│    │         ├── SK-Net: https://github.com/YeongHyeon/SKNet-TF2
│    │         ├── GhostNet: https://github.com/YeongHyeon/GhostNet
│    │         ├── Network-in-Network: https://github.com/YeongHyeon/Network-in-Network-TF2
│    │         ├── Shake-Shake Regularization: https://github.com/YeongHyeon/Shake-Shake
│    │         ├── MNIST Attention Map: https://github.com/YeongHyeon/MNIST_AttentionMap
│    │         └── MLP-Mixer: https://github.com/YeongHyeon/MLP-Mixer-TF2
│    │    
│    ├── Generative Model
│    │    ├── Generals
│    │    │    ├── GAN: https://github.com/YeongHyeon/GAN-TF
│    │    │    ├── WGAN: https://github.com/YeongHyeon/WGAN-TF
│    │    │    ├── CGAN: https://github.com/YeongHyeon/CGAN-TF
│    │    │    ├── Normalizing Flow: https://github.com/YeongHyeon/Normalizing-Flow-TF2
│    │    │    └── Transformer: https://github.com/YeongHyeon/Transformer-TF2
│    │    ├── Anomaly Detection
│    │    │    ├── CVAE (Convolution & Variational): https://github.com/YeongHyeon/CVAE-AnomalyDetection
│    │    │    ├── GANomaly: https://github.com/YeongHyeon/GANomaly-TF
│    │    │    ├── Skip-GANomaly: https://github.com/YeongHyeon/Skip-GANomaly
│    │    │    ├── ConAD: https://github.com/YeongHyeon/ConAD
│    │    │    ├── MemAE: https://github.com/YeongHyeon/MemAE
│    │    │    ├── f-AnoGAN: https://github.com/YeongHyeon/f-AnoGAN-TF
│    │    │    ├── DGM: https://github.com/YeongHyeon/DGM-TF
│    │    │    └── ADAE: https://github.com/YeongHyeon/ADAE-TF
│    │    └── Special Purpose
│    │         ├── SRCNN: https://github.com/YeongHyeon/Super-Resolution_CNN
│    │         ├── Context-Encoder: https://github.com/YeongHyeon/Context-Encoder
│    │         └── Sequence-Autoencoder: https://github.com/YeongHyeon/Sequence-Autoencoder
│    │    
│    └── Additional Methods
│         ├── SGDR: https://github.com/YeongHyeon/ResNet-with-SGDR-TF2
│         ├── Learning rate WarmUp: https://github.com/YeongHyeon/ResNet-with-LRWarmUp-TF2
│         └── ArcFace: https://github.com/YeongHyeon/ArcFace-TF2
│
└── PyTorch
     └── Generative Model
          ├── Anomaly Detection
          │    ├── CVAE (Convolution & Variational): https://github.com/YeongHyeon/CVAE-AnomalyDetection-PyTorch
          │    ├── GANomaly: https://github.com/YeongHyeon/GANomaly-PyTorch
          │    └── ConAD: https://github.com/YeongHyeon/ConAD-PyTorch
          └── Special Purpose
               └── SRCNN: https://github.com/YeongHyeon/Super-Resolution_CNN-PyTorch

Kaggle

Notebooks Expert 🎓

• 🥉 Riiid! step by step guide for Beginner/EDA/PyTorch @ Riiid Answer Correctness Prediction
• 🥉 Easy to run, Keras Full Package! (including EDA) @ [T-Academy X KaKr] 성인 인구조사 소득 예측 대회
• 🥉 Shopee, Easy to Run! @ Shopee - Price Match Guarantee
• 🥉 SETI, step by step guide for Beginner/EDA/TF @ SETI Breakthrough Listen - E.T. Signal Search
• 🥉 Convert DICOM to Numpy Array (Super Simple) @ RSNA-MICCAI Brain Tumor Radiogenomic Classification
• 😆 Step-by-Step MNIST | Full Package, EDA, TensorFlow @ Digit Recognizer
• 😆 Step-by-Step, Herbarium 2021! @ Herbarium 2021 - Half-Earth Challenge - FGVC8

Datasets

• 😆 RSNA-MICCAI BTRC2021 @ RSNA-MICCAI Brain Tumor Radiogenomic Classification