Ji Changpeng | Engineering | Best Researcher Award

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Prof. Ji Changpeng | Engineering | Best Researcher Award

Liaoning Technical University | China

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Early Academic Pursuits 🎓

Prof. Ji Changpeng began his academic journey with a Master’s degree in Computer Application Technology from Liaoning Technical University, which he completed in 2005. His strong foundation in computer applications laid the groundwork for his illustrious career in academia and research. With a keen interest in technological innovation and problem-solving, Prof. Ji's early academic endeavors marked the beginning of his contributions to the field of computer science.

Professional Endeavors 🏢

Currently a full professor and Master supervisor at Liaoning Technical University, Prof. Ji holds several prestigious roles. He is a recognized Codesys Senior Application Engineer and a Senior Artificial Intelligence Designer. As the Academic Leader of Information and Communication Engineering, he has played a pivotal role in shaping the department's vision. Additionally, his influence extends to academic leadership as a key member of the Outstanding Young Teacher initiative in Liaoning Province (2006). He also serves as an expert in discipline assessment and dissertation evaluations for the Ministry of Education, showcasing his authority in the field.

Contributions and Research Focus 🔬

Prof. Ji’s research contributions are vast and impactful. Having presided over more than 60 research projects, his work has significantly advanced the fields of artificial intelligence, information engineering, and communication systems. He has published over 160 academic papers and authored three academic works, contributing valuable insights and innovation to the global research community. His patents, numbering more than 40, highlight his practical approach to solving complex technological problems. Prof. Ji’s expertise as an editor and reviewer for esteemed journals such as Journal of Computers and IJConvC further solidifies his influence in academia.

Accolades and Recognition 🏆

Prof. Ji has received six prestigious science and technology advancement medals for his groundbreaking contributions. His role as an editorial board member and specialist reviewer for several reputed journals speaks volumes about his standing in the academic world. These accolades reflect his dedication to excellence and his commitment to pushing the boundaries of technology and innovation.

Impact and Influence 🌟

Through his extensive research, patents, and academic leadership, Prof. Ji has profoundly influenced the fields of artificial intelligence and communication engineering. His role in mentoring future researchers and supervising Master’s students ensures that his knowledge and vision continue to inspire the next generation. His work has not only shaped his university but has also had a far-reaching impact on the global research community.

Legacy and Future Contributions 🌍

Prof. Ji Changpeng’s contributions have left an indelible mark on the academic and technological landscape. His ability to blend research with practical application has set a benchmark for innovation. As he continues to explore new frontiers in artificial intelligence and communication engineering, his legacy will undoubtedly pave the way for groundbreaking advancements and a brighter future for technology and education.

 

Publications

📄 Design of Shared-Aperture Base Station Antenna with a Conformal Radiation Pattern
Journal: Electronics
Year: 2025
Authors: Ji Changpeng, Xin Ning, Wei Dai

📄 A Lightweight Safety Helmet Detection Algorithm Based on Receptive Field Enhancement
Journal: Processes
Year:2024
 Authors: Ji Changpeng, Zhibo Hou, Wei Dai

 

Xinhai Wang | Computer Science | Best Researcher Award

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Mr. Xinhai Wang | Computer Science | Best Researcher Award

Northeastern University | China

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Early Academic Pursuits 🎓

Mr. Xinhai Wang's academic journey began with an undergraduate degree in Mathematics and Applied Mathematics from Northeastern University, where he achieved a GPA of 3.81/5. His academic excellence earned him several accolades, such as the "Outstanding Student Cadre" and "Three Good Students" awards, reflecting his dedication to both academics and extracurricular activities. Wang was actively involved in numerous projects during his undergraduate years, honing his skills in advanced algebra, data mining, and mathematical modeling, laying the groundwork for his future endeavors.

Professional Endeavors 🏆

In September 2022, Xinhai Wang assumed the role of monitor for Northeastern University's Master of Science Class 2201, demonstrating exemplary leadership and organizational skills. His work extended beyond the classroom, where he helped in the construction of class activities and assisted in Party branch operations. Wang was awarded the honorary title of Outstanding Graduate Student Cadre for his relentless efforts in promoting student engagement and fostering a collaborative environment. As a deputy director in the Project Development Department of the Social Practice Department, he organized impactful student initiatives such as charity sales, making significant contributions to the student community.

Contributions and Research Focus 🔬

Mr. Wang's contributions to academia and research are vast, with his work primarily centered on applying advanced algorithms in real-world scenarios. He has engaged in several high-level projects, including the application of genetic algorithms in mobile chess and using deep learning techniques like Deep Q Networks for stock market predictions. His research has tackled challenges in time series prediction, exploring fractional order random configuration networks (FSCN) to address the inherent non-stationarity in real-world data. These projects showcase his technical expertise in MATLAB and Python, alongside his growing knowledge of reinforcement learning and machine learning.

Accolades and Recognition 🏅

Xinhai Wang's academic brilliance has been recognized throughout his career, both during his undergraduate and graduate studies. His GPA of 3.40/4 ranked him 2nd in his class, further earning him prestigious honors such as the President Scholarship and First-Class Academic Scholarship. His leadership in class and organizational roles has led to multiple "Outstanding Class Cadre" awards. Wang's academic achievements extend beyond his GPA and awards, with his research work being submitted to conferences and awaiting SCI journal reviews, positioning him as a rising star in applied statistics and data science.

Impact and Influence 🌟

Through his roles in student governance and research, Wang has had a lasting impact on both his peers and the academic community. He has innovated branch activities, guided students in social practice initiatives, and created platforms for broader engagement in scientific and social matters. His research endeavors, such as the application of deep learning to stock prediction and time series analysis, contribute to the growing body of knowledge in the field of statistical modeling and artificial intelligence, influencing future technological advancements.

Legacy and Future Contributions 💡

Mr. Xinhai Wang's journey reflects a commitment to excellence in academic leadership, research, and innovation. As he continues to explore the boundaries of machine learning, algorithm design, and data modeling, his future contributions will likely have a profound effect on emerging fields like stock prediction and industrial data analysis. His ongoing projects in MATLAB and Python, combined with his growing expertise in reinforcement learning, position him for future success in both academic and professional arenas.

 

Publications

📄  Prediction of Ship-Unloading Time Using Neural Networks
Author: Zhen Gao, Danning Li, Danni Wang, Zengcai Yu, Witold Pedrycz, Xinhai Wang
Journal: Applied Sciences
Year: 2024-09

📄  Novel Admissibility Criteria and Multiple Simulations for Descriptor Fractional Order Systems with Minimal LMI Variables
Author: Xinhai Wang, Jin-Xi Zhang
Journal: Fractal and Fractional
Year: 2024-06

 

Soopil Kim | Computer Science | Best Researcher Award

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Dr. Soopil Kim | Computer Science | Best Researcher Award

Daegu Gyeongbuk Institute of Science and Technology | South Korea

Author Profile

Scopus

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Early Academic Pursuits 🎓

Dr. Soopil Kim's academic journey began with a Bachelor of Engineering in Robotics and Mechatronics Engineering from Daegu Gyeongbuk Institute of Science & Technology (DGIST), where he graduated Cum Laude. He continued his studies at DGIST, pursuing a Master’s and Ph.D. in the same field, focusing on medical image segmentation. His research during these years emphasized label-efficient segmentation models and limited pixel-level annotation, laying a strong foundation for his future work in deep learning and computer vision.

Professional Endeavors 💼

Dr. Kim's career has seen significant milestones, including a role as a Visiting Student at Stanford University's CNSLAB under the supervision of Prof. Kilian M. Pohl and Ehsan Adeli. Currently, he is a Post-Doctoral Research Fellow at the Medical Image & Signal Processing Lab (MISPL) at DGIST, where he works under Prof. Sang Hyun Park. His professional trajectory reflects a commitment to advancing the field of computer vision through innovative research and collaboration.

Contributions and Research Focus 🔬

Dr. Kim’s research is at the forefront of deep learning and computer vision. His work addresses the challenges of image segmentation with partially labeled datasets by developing federated learning strategies and few-shot segmentation techniques. His notable contributions include the creation of a medical image segmentation model that integrates meta-learning and bi-directional recurrent neural networks, a semi-supervised segmentation model based on uncertainty estimation, and a transductive segmentation model for industrial imaging. These advancements aim to improve the efficiency and accuracy of image segmentation processes.

Accolades and Recognition 🏆

Dr. Kim has received several awards that highlight his exceptional contributions to the field. Notably, he was ranked 3rd among 40 teams in the SNUH Sleep AI Challenge in 2021 and was honored with the Outstanding Student Award from the Department of Robotics and Mechatronics Engineering at DGIST in 2022. In 2024, he was recognized at the KCCV Oral/Poster Presentation Doctoral Colloquium for his work on label-efficient segmentation models.

Impact and Influence 🌍

Dr. Kim's research has made a significant impact on the field of computer vision, particularly in the area of image segmentation. His innovative approaches to handling partially labeled datasets and federated learning have the potential to advance both academic research and practical applications in medical imaging and beyond. His work on few-shot learning and uncertainty-aware models addresses critical challenges in the field, contributing to more robust and adaptable segmentation solutions.

Legacy and Future Contributions 🚀

As Dr. Kim continues his research, his focus on improving segmentation models and developing new methodologies promises to shape the future of computer vision. His commitment to exploring federated learning and few-shot learning techniques will likely drive further innovations in the field, offering solutions to complex challenges and enhancing the accuracy of image analysis across various applications.

 

Publications 📘

📄Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features
Authors: Sion An, Soopil Kim, Philip Chikontwe, Jiwook Jung, Hyejeong Jeon, Jaehong Kim, Sang Hyun Park
Journal: Expert Systems with Applications
Year: 2024

📄Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets
Authors: Soopil Kim, Haejun Park, Myeongju Kang, Kilian M. Pohl, Sang Hyun Park
Journal: Medical Image Analysis
Year: 2024

📄FedNN: Federated learning on concept drift data using weight and adaptive group normalizations
Authors: Myeongju Kang, Soopil Kim, Kwang-Hyun Jin, Kilian M. Pohl, Sang Hyun Park
Journal: Pattern Recognition
Year: 2024

📄Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
Authors: Soopil Kim, Sion An, Philip Chikontwe, Kilian M. Pohl, Sang Hyun Park
Conference: Proceedings of the AAAI Conference on Artificial Intelligence
Year: 2024

📄Uncertainty-aware semi-supervised few shot segmentation
Authors: Soopil Kim, Philip Chikontwe, Sion An, Sang Hyun Park
Journal: Pattern Recognition
Year: 2023