Geunbo Yang (양근보) is currently a researcher at KG Steel. He received his BS and MS degree in Computer Engineering from Kwangwoon University in Seoul, South Korea.

His research interests include machine learning algorithms and spiking neural networks (SNN), with a focus on energy-efficient bio-signal processing.

Publications

Journal Articles

• Geunbo Yang, Youngshin Kang, Peter H Charlton, Panayiotis A Kyriacou, Ko Keun Kim, Ling Li, Cheolsoo Park, “Energy-efficient PPG-based respiratory rate estimation using spiking neural networks”, Sensors, 24(12), 3980, MDPI, Jun. 2024, IF=3.4

• Choongseop Lee, Geunbo Yang, Jaewoo Baek, Yuntae Park, Mingyu Cheon, Jongkil Park, Cheolsoo Park, “Regression Model Employing Spiking Neural Network for Bio-Signal Analysis With Hardware Integration”, IEEE Access, Feb. 2025

• G Yang, W Lee, Y Seo, C Lee, W Seok, J Park, D Sim, C Park, “Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons”, MDPI Sensors, 2023, 7232, IF=3.847

• S Baek, J Kim, H Yu, G Yang, I Sohn, Y Cho, C Park, “Intelligent Feature Selection for ECG-Based Personal Authentication Using Deep Reinforcement Learning”, MDPI Sensors, 2023, IF=3.847

• Y Kang, G Yang, H Eom, S Han, S Baek, S Noh, Y Shin, C Park, “GAN-based patient information hiding for an ECG authentication system”, Biomedical Engineering Letters, 2023, 197-297, IF=3.92

• Y Park, U Sunarya, G Yang, C Lee, J Baek, S Baek, C Park, “Classification using a Spiking Neural Network: A Review”, IEIE Transaction on Smart Processing & Computing, 2023, 64-71

• J Kim, G Yang, J Kim, S Lee, KK Kim, C Park, “Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning”, MDPI Sensors, 2021, 21, 1568, IF=3.735

Patents

• 박철수, 양근보, 이충섭, “몽유병을 감지하는 엣지 디바이스”, 출원번호: 10-2022-0164099, Dec. 2022

• 박철수, 이지운, 양근보, “확률 모델에 기반한 손상된 시계열 생체 신호의 복원 방법”, 출원번호: 10-2022-0189990, Dec. 2022

• 박철수, 양근보, 김준모, “사용자 인증 장치 및 방법”, 출원번호: 10-2019-0179392, Dec. 2019