Hyunsoo Yoon

Assistant Professor

Yonsei University

South Korea

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Dr. Hyunsoo Yoon is an Assistant Professor at Yonsei University, South Korea. His research primarily focuses on self-supervised learning frameworks, visual anomaly detection, and fault diagnosis in rotating machinery. He has published extensively on topics including deepfake disruption, fire detection systems, and Alzheimer's disease classification. His recent work emphasizes the application of advanced neural networks to real-world challenges in various domains.

Articles (20)

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2020

Collaborators (7)

Jin Wen

Professor

Drexel University

UNITED STATES

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Teresa Wu

Arizona State University

UNITED STATES

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Kristin Swanson

Professor and Vice Chair

Mayo Clinic

UNITED STATES

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Zheng O'Neill

Texas A&M University

UNITED STATES

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Han Hu

Assistant Professor

University of Arkansas

UNITED STATES

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Petros Petridis

New York University

UNITED STATES

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Michael Argenziano

NewYork-Presbyterian Hospital/Columbia University Irving Medical Center

UNITED STATES

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Hyunsoo Yoon

Research Interests

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About

Articles (20)

Mamba-attention: A self-supervised framework for efficient remaining useful life prediction

UniFormaly: Towards task-agnostic unified framework for visual anomaly detection

Improving rotating machinery fault diagnosis across heterogeneous machines with domain adversarial perturbation in semi-supervised domain adaptation scenario

LEAT: Towards robust deepfake disruption in real-world scenarios via Latent Ensemble Attack

SimUV-Net: A novel approach for UV signal-based fire detection and classification leveraging supervised contrastive learning

TANet: Tri-Aspects Network for Camouflaged Object Detection

AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain

A New Model for Building Energy Modeling and Management Using Predictive Analytics: Partitioned Hierarchical Multitask Regression (PHMR)

Healthy core: Harmonizing brain MRI for supporting multicenter migraine classification studies

Multiclass classification of Alzheimer's disease prodromal stages using sequential feature embeddings and regularized multikernel support vector machine

Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma

A cosine-based correlation information entropy approach for building automatic fault detection baseline construction

A Novel Semi-Supervised Learning Model for Smartphone-Based Health Telemonitoring

A novel multi-task linear mixed model for smartphone-based telemonitoring

Deep learning strategies for critical heat flux detection in pool boiling

Prediction of fall risk among community-dwelling older adults using a wearable system

AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction

A feature transfer enabled multi-task deep learning model on medical imaging

Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images

Deep Residual Inception Encoder–Decoder Network for Medical Imaging Synthesis

Collaborators (7)

Jin Wen

Teresa Wu

Kristin Swanson

Zheng O'Neill

Han Hu

Petros Petridis

Michael Argenziano

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