추현승 연구실

Data preprocessing and manipulation is the most essential part of recent deep learning technologies. Research includes medical data processing, network mobility modeling, facial image transformation, and lifelog data management.

Medical imaging using computer vision and machine learning for diagnosis and analysis. Research includes bone age prediction from X-ray images and macular image transformation using fundus images and GAN technologies.

GAN-driven deep learning approaches for networking applications including data augmentation, privacy preserving, and anomaly detection. Current work focuses on proactive mobility for 5G networks.

Reinforcement learning techniques for Multi-access Edge Computing management including computation offloading, resource allocation, and mobility management in distributed cloud environments.

Integration of AI with IoT systems for massive and critical IoT applications. Focus on anomaly cognition including detection, prediction, and identification of data faults and cyber-attacks in 6G paradigm.

Network softwarization through Software-defined Networking (SDN) and Network Function Virtualization (NFV). Research includes network protection, video streaming, flow management, network slicing, and VNF monitoring.

Decentralized wireless networks where nodes cooperatively forward data. Research focuses on speed of collection and reliability under realistic wireless conditions with emerging technologies like energy harvesting.

Investigation of human interaction evolution with ubiquitous technology including smart phones, smart homes, and IoT devices. Research on intuitive interaction interfaces for smart homes and Industry 4.0 robotics.