Jingxian Liu | Computer Vision | Best Researcher Award

Posted on by Academic achievements

Mr. Jingxian Liu | Computer Vision | Best Researcher Award

Associate Research Fellow‌ at Guangzhou Maritime University, China.

Mr. Jingxian Liu is an Associate Research Fellow at Guangzhou Maritime University. Born in November 1984 in Guangzhou, China, he specializes in remote sensing and communication systems. His research focuses on digital twins, intelligent state prediction, and maneuvering-target tracking using advanced computational methods. Liu has authored numerous high-impact publications and has led several national and regional research projects, contributing significantly to the field of geoscience and remote sensing.

Profile Verification

Scopus 

Education

Jingxian Liu pursued his doctoral studies in Communication and Information Systems at Beihang University (2013-2018) after completing a Master’s degree in Geodetection and Information Technology from China University of Geoscience (Beijing, 2007-2010). He holds a Bachelor’s degree in Electronic Information Engineering from China University of Geoscience (Beijing, 2003-2007). His educational background has equipped him with a solid foundation in engineering and remote sensing technologies.

Experience

Jingxian Liu currently serves as an Associate Research Fellow at Guangzhou Maritime University since March 2024. Prior to this, he was an Associate Research Fellow at Guangxi University of Science and Technology from December 2018 to December 2023. During this period, he contributed significantly to research projects focusing on remote sensing and digital twin technologies. Earlier in his career, Liu worked as an Engineer at the China Shipbuilding Industry Corporation’s 760th Research Institute from July 2010 to June 2013. His role there involved conducting research and development activities aimed at advancing engineering technologies in shipbuilding and marine industries.

Research Interests

Jingxian Liu’s research primarily revolves around remote sensing image processing, digital twins, intelligent state prediction, and maneuvering-target tracking. His innovations include fast arbitrary-oriented object detection for remote sensing images, differential correction based shadow removal methods, and deep learning algorithms for maneuvering-target tracking. His work significantly advances understanding in these areas, applying cutting-edge computational techniques to solve complex challenges.

Publications

Fast arbitrary-oriented object detection for remote sensing images

Authors: Liu, J.; Tang, J.; Yang, F.; Zhao, Y.

Citations: 0

Year: 2024

Task Demands-Oriented Collaborative Offloading and Deployment Strategy in Software-Defined UAV-Assisted Edge Networks

Authors: Yan, J.; Wang, W.; Liu, J.; Yuan, H.; Zhu, Y.

Citations: 0

Year: 2024

HDDet: A More Common Heading Direction Detector for Remote Sensing and Arbitrary Viewing Angle Images

Authors: Ding, S.; Liu, J.; Yang, F.; Xu, M.

Citations: 1

Year: 2024

Digital Twins Based Intelligent State Prediction Method for Maneuvering-Target Tracking

Authors: Liu, J.; Yan, J.; Wan, D.; Al-Dulaimi, A.; Quan, Z.

Citations: 5

Year: 2023

Locating the propagation source in complex networks with observers-based similarity measures and direction-induced search

Authors: Yang, F.; Li, C.; Peng, Y.; Wen, J.; Yang, S.

Citations: 7

Year: 2023

Diffusion characteristics classification framework for identification of diffusion source in complex networks

Authors: Yang, F.; Liu, J.; Zhang, R.; Yao, Y.

Citations: 1

Year: 2023

A differential correction based shadow removal method for real-time monitoring

Authors: Liu, S.; Chen, M.; Li, Z.; Liu, J.; He, M.

Citations: 0

Year: 2023

A cross-and-dot-product neural network based filtering for maneuvering-target tracking

Authors: Liu, J.; Yang, S.; Yang, F.

Citations: 6

Year: 2022

Micro-Knowledge Embedding for Zero-shot Classification

Authors: Li, H.; Wang, F.; Liu, J.; Zhang, T.; Yang, S.

Citations: 3

Year: 2022

An identification strategy for unknown attack through the joint learning of space–time features

Authors: Wang, H.; Mumtaz, S.; Li, H.; Liu, J.; Yang, F.

Citations: 6

Year: 2021

 

Conclusion

Jingxian Liu is a highly deserving candidate for the Research for Best Researcher Award due to his significant contributions to remote sensing, digital twins, and maneuvering-target tracking. His innovative research methodologies, high-impact publications, and leadership in large-scale research projects position him as a leader in his field. Continued efforts to enhance industry collaborations and community engagement will further solidify his status as a key figure in advancing technological solutions for environmental and geospatial challenges.

 

Yong-Guk Kim | Computer Vision | Best Researcher Award

Posted on by Academic achievements

Prof. Dr.Yong-Guk Kim | Computer Vision | Best Researcher Award

Professor at Sejong University, South Korea

Dr. Yong-Guk Kim is a Full Professor in the Department of Computer Engineering at Sejong University, Seoul, Korea, and a renowned expert in artificial intelligence and computer vision. His academic journey has taken him from Korea to prestigious institutions in the UK, Netherlands, and the US, contributing significantly to fields like Generative AI, facial expression recognition, and autonomous drone technology. With a career spanning over three decades, Dr. Kim has excelled in both academic research and industry collaborations, leading innovative AI projects and earning multiple accolades in international AI challenges.

Profile

ORCID

Education:

Dr. Kim completed his Ph.D. in Experimental Psychology, specializing in computational vision, from Cambridge University, where he explored visual surface representation for transparency, occlusion, and brightness. He also holds an M.S. in Electrical Engineering, majoring in Automatic Control, and a B.S. in Electrical Engineering, both from Korea University. His education set the foundation for a career at the intersection of engineering and cognitive science, particularly in AI and computer vision applications.

Experience:

Dr. Kim’s diverse career includes research roles at major organizations such as LG and KT in Korea, followed by advanced research opportunities abroad. He worked as a postdoctoral fellow at the Smith-Kettlewell Vision Institute in San Francisco and as an EU fellow at the Robotics Institute of Utrecht University in the Netherlands. He has served as a faculty member at Sejong University for over two decades, holding leadership positions such as Dean of International Affairs and Head of the Start-up Incubator. He has successfully founded the startup Affectronics, specializing in mobile 3D avatars, and played a pivotal role in several AI challenges, showcasing his expertise in applied AI.

Research Interest:

Dr. Kim’s primary research areas lie in Generative AI and Computer Vision. His work encompasses multi-modal large language models, video anomaly detection, and facial expression recognition, with a particular focus on real-world applications such as autonomous drones and personalized advertising platforms. His lab has made significant strides in AI-driven tasks, such as autonomous drone racing and emotion detection, winning multiple international competitions. His research is well-funded by both governmental bodies and private industries, highlighting the practical and impactful nature of his work.

Awards:

Dr. Kim has received numerous awards for his contributions to AI, particularly in global competitions. Notably, his lab won the prestigious Game of Drones Challenge in 2019, organized by Microsoft and Stanford University at the NeurIPS conference. He also placed second in the Inpainting and Denoising Challenge at the European Conference on Computer Vision (ECCV) in 2018 and won the Fake Emotion Detection Challenge at the International Conference on Computer Vision (ICCV) in 2017. These achievements underscore his prominence in the AI research community and his ability to lead teams in high-stakes, competitive environments.

Publications:

Dr. Kim has published extensively in top-tier journals, contributing to the advancement of AI and computer vision. His recent works include:

“Reinforcement Learning Based Drone Simulators: Survey, Practice, and Challenge” (2024) – Artificial Intelligence Review (Cited by: 281) Link.

“UET4Rec: U-net Encapsulated Transformer for Sequential Recommender” (2024) – Expert Systems with Applications (Cited by: 781) Link.

“Meme Analysis using LLM-based Contextual Information“ (2024) – IEEE Access (Cited by: 5) Link.

“Attention-based Residual Autoencoder for Video Anomaly Detection” (2023) – Applied Intelligence (Cited by: 240) Link.

“A Promising AI-based Tool to Simulate Hydrogen Sulfide Elimination” (2023) – Separation and Purification Technology (Cited by: 472) Link.

Conclusion:

Dr. Yong-Guk Kim’s extensive contributions to AI and computer vision, coupled with his successful track record in international AI challenges, academic excellence, and industry collaboration, make him a strong candidate for the Best Researcher Award. His teaching and entrepreneurial achievements further add to his case, demonstrating both academic prowess and real-world impact. By expanding his research into newer domains and engaging more with public discourse on AI, he could further solidify his standing as a world-class researcher.