Yoongun Jung |Biophotonics Systems Integration | Research Excellence Award

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Mr. Yoongun Jung | Biophotonics Systems Integration | Research Excellence Award

Mr. Yoongun jung | Korea University | South Korea

Mr. Yoongun Jung is an emerging researcher in Electrical Engineering, currently pursuing an M.S.–Ph.D. integrated program at Korea University, Seoul (2021–2027), after completing his B.S. in Electrical Engineering from the same institution in 2021. His research focuses on cutting-edge domains including AI applications in power systems, HVDC control, and energy management systems, contributing to intelligent, secure and sustainable power infrastructures. Demonstrating strong academic and research excellence, he has received several prestigious recognitions, beginning with an Excellence Award at the Kepco Creative Innovation Idea Contest in 2020 for his work on deep learning-based frequency response prediction. His impactful research has earned multiple Conference Best Paper Awards, including contributions on adaptive Volt–Var control using multiagent deep reinforcement learning (KIEE PES 2021, Jeju), cost-effective dynamic multi-microgrid formulation (KIEE Power System Research Association, 2023, Jeju), and transient stability data-driven special protection schemes using reinforcement learning (First Best Paper Award, ICRERA 2024, Japan). In addition, he has been recognized at IEEE Student Paper Awards for innovations such as solar power prediction using LGBM (2021), dynamic multi-microgrid formulation using spanning tree algorithms (2022), and federated reinforcement learning-based AGC algorithms, which won the Best Paper Award at the KIEE PES North America Chapter Meeting in 2025. Through his prolific research achievements, Mr. Jung continues to advance intelligent energy systems and contribute significantly to the future of smart power engineering.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

“Enhancing frequency stability with decentralized adaptive control using multi-agent deep reinforcement learning of multi-VSGs”, S Kang, Y Jung, D You, G Jang, International Journal of Electrical Power & Energy Systems 172, 2025.

“Cost-Effective automatic generation control for Renewable-dominated grids: Multi-Agent deep reinforcement learning approach”, Y Jung, S Kang, J Cha, S Song, M Yoon, G Jang, International Journal of Electrical Power & Energy Systems 173, 2025.

“Coordinate control of multi-infeed VSC-HVDC for enhancing power system reliability”, K Kim, Y Jung, M Chang, J Cha, S Kang, H Ku, G Kwon, M Yoon, G, Jang International Journal of Electrical Power & Energy Systems 166, 2025.

“Physics-informed neural network-based VSC back-to-back HVDC impedance model and grid stability estimation”, M Chang, Y Jung, S Kang, G Jang, Electronics 13 (13), 2024.

“Transient Stability Data Driven Special Protection Scheme Using Deep Reinforcement Learning”, Y Jung, S Kang, K Kim, H Woo, S Choi, G Jang, S Song, M Yoon 2024, 13th International Conference on Renewable Energy Research, 2024.

Rachel merveille fomekong | Biophotonics Systems Integration | Computational Biophotonics Award

Published on by Biophotonics Research

Mrs. Rachel merveille fomekong | Biophotonics Systems Integration | Computational Biophotonics Award

Ph.D. Candidate at Guangdong Ocean University, China

Fomekong Fomekong Rachel Merveille is a dedicated and innovative researcher specializing in underwater robotics, SLAM (Simultaneous Localization and Mapping), deep learning, and sensor fusion. Currently pursuing a PhD at Guangdong Ocean University in China, she is passionate about leveraging advanced technologies to enhance navigation and environmental perception in unmanned underwater vehicles (UUVs). With expertise in designing robotic systems and integrating cutting-edge tools, Rachel is committed to pushing the boundaries of underwater navigation systems.

Profile

ORCID

Education 🎓

Rachel holds a Bachelor’s degree in Physics (Fundamental Physics) and a Master’s in Environmental Physics from the University of Yaoundé 1 in Cameroon. She further advanced her academic journey with a Master’s in Control Engineering from Huzhou University, China, focusing on robotic solutions for manufacturing. Currently, she is completing her PhD in Marine Engineering at Guangdong Ocean University, with a thesis emphasizing multi-sensor integration for enhancing underwater SLAM systems.

Experience 🛠️

Rachel has extensive experience in underwater robotics, having worked as a Robotics Operator at Guangdong Ocean University. Her contributions include integrating sensor fusion techniques to improve underwater navigation. She also gained hands-on expertise in drone assembly and 3D printing at Huzhou University and contributed to automation projects in manufacturing industries. In Cameroon, she co-designed an educational platform and supported mechanical physics education for high school students.

Research Interests 🔬

Rachel’s research focuses on enhancing underwater SLAM systems through deep learning and sensor fusion. Her interests span underwater navigation, environmental perception, point cloud processing, and autonomous systems design. She is particularly intrigued by the potential of neural networks in optimizing underwater vehicle operations.

Awards 🏆

Rachel has received numerous accolades, including the Guangdong Provincial Scholarship (2022, 2023), the Zhejiang Provincial Scholarship for Excellent International Students (2021), and a Graduate Merit Scholar Award from Huzhou University. Her accomplishments underscore her academic excellence and dedication to innovation.

Publications 📚

Rachel has published influential articles on underwater robotics and Industry 4.0 technologies:

Enhancing Manufacturing Efficiency: A New Robotic Arm Design for Injection Molding with Improved Adaptability and Precision

  • Date: 2024-11-27
  • Type: Preprint
  • DOI: 10.21203/rs.3.rs-5524250/v1
  • Contributors: Fomekong Fomekong Rachel Merveille, HuGe Jile, Bissih Fred

Advancements in Sensor Fusion for Underwater SLAM: A Review on Enhanced Navigation and Environmental Perception

  • Date: 2024-11-24
  • Type: Journal article
  • DOI: 10.3390/s24237490
  • Contributors: Fomekong Fomekong Rachel Merveille, Baozhu Jia, Zhizun Xu, Bissih Fred

Enhancing Underwater SLAM Navigation and Perception: A Comprehensive Review of Deep Learning Integration

  • Date: 2024-10-31
  • Type: Journal article
  • DOI: 10.3390/s24217034
  • ISSN: 1424-8220
  • Contributors: Fomekong Fomekong Rachel Merveille, Baozhu Jia, Zhizun Xu, Bissih Fred

Conclusion 🌟

While Fomekong demonstrates exceptional technical expertise and computational skills, their research focus is not directly related to computational biophotonics. They might be more suited to awards in robotics, sensor fusion, or machine learning applications in engineering. For the Computational Biophotonics Award, candidates with specific experience in photonics, optical imaging, or biophysics would be stronger contenders.