Prof. Chong Hyun Lee | Deep Learning | Best Researcher Award
Professor | Jeju National University | South Korea
Dr. Chong Hyun Lee is a distinguished researcher and academic in the fields of electronics, power transfer systems, ocean system engineering, and applied signal processing, with a prolific research record and recognized scientific excellence. As a long-standing IEEE member, Dr. Lee has made significant contributions to the advancement of electronic and communication technologies through innovation, leadership, and collaborative research. His work has been recognized with prestigious honors, including the Best Science and Technology Paper Award from the Korean Federation of Science and Technology Societies (KOFST) and the Best Paper Award in Ocean System Engineering from the Institute of Electronics and Information Engineers in South Korea, reflecting his high academic impact and technical expertise. Dr. Lee’s research encompasses a wide range of advanced topics, including compact impedance matching circuits for wideband power transfer in Janus Helmholtz transducers, weakly supervised graph neural networks for line spectrum extraction, and DC power boosting circuits for triboelectric nanogenerators with high intrinsic impedance and multi-harmonic output. His interdisciplinary work bridges marine engineering, electronics, and nanogenerator technologies, offering practical applications in sustainable energy systems, signal processing, and sensor innovation.
Featured Publications:
Lee, C. H., & co-authors. (2024). DC power boosting circuit for freestanding-sliding triboelectric nanogenerators with high intrinsic impedance and multi-harmonic output. Advanced Materials Technologies.
Lee, C. H., & co-authors. (2024). Passive sonar signal classification using graph neural network based on image patch. Journal of the Acoustical Society of Korea.
Lee, C. H., & co-authors. (2023). Encoding and decoding-based normalizing flows for image anomaly localization. Electronics Letters.
Lee, C. H., & co-authors. (2023). Abnormal sonar signal detection using recurrent neural network and vector quantization. Journal of the Acoustical Society of Korea.