Kyongchul Kim | Tech Innovations | Best Researcher Award

Kyongchul Kim | Tech Innovations | Best Researcher Award

Dr Kyongchul Kim Korea, Peninsula Infrastructure Special Committee, South Korea

Based on the detailed information provided, Dr. Kyongchul Kim appears to be a strong candidate for the Best Researcher Award. His extensive academic background, combined with a significant research output and practical engineering experience, showcases his expertise and contributions to the field of Civil Engineering.

Publication profile

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Educational Background

Dr. Kim earned his Ph.D. in Civil Engineering from Kunsan National University, where his research focused on the structural behavior of steel fiber-reinforced ultra-high-performance concrete beams subjected to bending. His academic journey, starting with a Bachelor of Engineering and progressing through a Master of Science, highlights a consistent and rigorous pursuit of knowledge in Civil and Environmental Engineering.

Research Experience and Achievements

Dr. Kim has demonstrated his research prowess through various roles, including his current position as a Senior Researcher at the Korean Peninsula Infrastructure Special Committee at KICT. His work on developing modular bridge structures and validating their performance using ultra-high-performance concrete reflects his deep involvement in innovative and practical engineering solutions. His previous roles, including those at JANGMIN enc and DM ENGINEERING, further underscore his versatility and hands-on experience in the field.

Research Projects

Dr. Kim has participated in several notable research projects, such as the development of design technology in residential floating architecture and the practical use of eco-friendly green large block retaining wall systems. These projects not only demonstrate his ability to lead and collaborate on large-scale engineering challenges but also highlight his contribution to advancing sustainable construction practices.

Publications and Conference Presentations

Dr. Kim has an impressive portfolio of publications and conference presentations, with his research being widely recognized in the field. His work on the flexural behavior of ultra-high-performance concrete and hybrid steel fiber-reinforced concrete beams has been published in reputable journals and presented at key conferences, contributing valuable insights to the civil engineering community.

Conclusion

Dr. Kyongchul Kim’s academic achievements, research contributions, and practical engineering experiences make him a suitable candidate for the Best Researcher Award. His work has not only advanced the field of Civil Engineering but also demonstrated a commitment to addressing critical infrastructure challenges with innovative solutions. His leadership in research projects and active participation in knowledge dissemination through publications and presentations further solidify his credentials as a top researcher in his field.

Research focus

A Comparative Experimental Study on the Flexural Behavior of High‐Strength Fiber‐Reinforced Concrete and High‐Strength Concrete Beams

An experimental study on the ductility and flexural toughness of ultrahigh-performance concrete beams subjected to bending

Effects of Single and Hybrid Steel Fiber Lengths and Fiber Contents on the Mechanical Properties of High‐Strength Fiber‐Reinforced Concrete

Flexural strength of hybrid steel fiber-reinforced ultra-high strength concrete beams

Structural behavior of concrete beams containing recycled coarse aggregates under flexure

Mechanical properties and predictions of strength of concrete containing recycled coarse aggregates

Material properties and structural characteristics on flexure of steel fiber-reinforced ultra-high-performance concrete

Structural behavior of hybrid steel fiber-reinforced ultra high performance concrete beams subjected to bending

Electrical characteristics of ultra-high-performance concrete containing carbon-based materials

Effect of broad-spectrum biofilm inhibitor raffinose, a plant galactoside, on the inhibition of co-culture biofilm on the microfiltration membrane

 

Yiming Xu | Tech Innovations | Best Researcher Award

Yiming Xu | Tech Innovations | Best Researcher Award

Mr Yiming Xu, Cranfield University, United Kingdom

Yiming Xu is a Ph.D. candidate in Energy at Cranfield University (2020-2024) with a focus on AI for energy flexibility and decarbonisation. He holds an MSc in Advanced Mechanical Engineering from Cranfield University and a BEng in Mechanical Engineering from Nanjing University of Aeronautics and Astronautics. Yiming has contributed to Innovate UK projects, presented at conferences such as ICAE and ISGT, and published papers on energy trading. He has interned at DJI Technology Co., Ltd, and holds patents in finger flexibility devices and mountain-climbing aids. Proficient in Python, C++, and data visualization, he is also an amateur Muay Thai fighter. 🧠🔋🤖📚🥊

Publication profile

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Education

With a PhD in Energy from Cranfield University (2020-2024) 🎓, He focused on AI for energy flexibility modelling and decarbonisation 🌱, vehicle-to-vehicle energy trading, and EV owner behaviour analysis 🚗. He presented at ICAE, ISGT, ICPADS, and other seminars 🎤. My MSc in Advanced Mechanical Engineering (2019-2020) included a thesis on peer-to-peer energy trading for EVs ⚡ and courses like CFD and risk engineering 📚. During an AI exchange at Imperial College London (2018), I designed computer vision algorithms for a robotic arm 🤖. My BEng from Nanjing University (2015-2019) involved a thesis on 3D printing and courses in mechanics and materials 🛠️.

Experience

During my internship at DJI Technology Co., Ltd in Shenzhen, China, from June to August 2018, I participated in the global young engineer competition ROBOMASTER, working with a team that included top universities from China and overseas. I served as venue maintenance personnel in the ROBOMASTER machinery group, responsible for debugging mechanical organs and sensors, and maintaining the visual recognition module of the referee system. I inspected and maintained over 50 units of equipment, resolving issues more than 10 times, ensuring the smooth operation of the event. 🌍🤖🔧👨‍🔧📷✅

Research Projects

As a Research Assistant on three Innovate UK projects, I optimized energy flow management in urban EV charging with Lesla Ltd (Aug 2023 – Jan 2024), designing AI models to schedule charging behavior and forecast energy demand 📈🔋. I established a smart home EV charger system for Entrust Smart Home Ltd (Jan 2021 – Mar 2021), focusing on app design and peer-to-peer energy trading 📱🏠. Additionally, I worked with SNRG Ltd and Electric Corby CIC (Oct 2020 – Mar 2021) on advanced grid services, analyzing driving behavior data and designing trading algorithms 🚗💡. All projects met quality standards and were successfully delivered ✅.

Research focus

Yiming Xu’s research primarily focuses on vehicle-to-vehicle (V2V) energy trading, particularly through innovative auction models and flexible trading platforms. His work explores sustainable energy solutions, fraud prevention, and efficient market mechanisms in V2V energy exchanges. Xu’s studies integrate advanced technologies like the K-factor approach and double auction systems to enhance energy trading efficiency and security. His research contributions are significant in the fields of smart grids, green computing, and sustainable energy, aiming to develop robust frameworks for future energy systems. 🌍🔋🚗💡📉🔒

Publication top notes

Vehicle-to-Vehicle Energy Trading Framework: A Systematic Literature Review

An Anti-fraud Double Auction Model in Vehicle-to-Vehicle Energy Trading with the K-factor Approach

A Vehicle-to-vehicle Energy Trading Platform Using Double Auction With High Flexibility

 

Ioannis Chatzilygeroudis | Computer Science and Artificial Intelligence | Best Researcher Award

Ioannis Chatzilygeroudis | Computer Science and Artificial Intelligence | Best Researcher Award

Prof Ioannis Chatzilygeroudis, University of Patras, Greece

Prof. Emeritus at the University of Patras, Greece, with a rich educational background in Mechanical and Electrical Engineering (NTUA), Theology (University of Athens), MSc in Information Technology, and a PhD in Artificial Intelligence (University of Nottingham). Fluent in Greek and English, he specializes in AI, KR&R, knowledge-based systems, theorem proving, intelligent tutoring, e-learning, machine learning, natural language generation, sentiment analysis, semantic web, and educational robotics. His prolific research includes a PhD thesis, 18 edited volumes, 21 book chapters, 46 journal papers, 115 conference papers, 8 national conference papers, and 14 workshop papers. 📚🤖💻🌐

Publication profile

Orcid

Education

📚 From September 1968 to June 1974, completed secondary education, earning a Certificate of High School Graduation in Science. 🎓 Pursued a Diploma in Mechanical and Electrical Engineering with a specialization in Electronics at the National Technical University of Athens from October 1974 to July 1979. 📜 From February to June 1983, obtained a Certificate of Educational Studies from PATES of SELETE, Greece. 📖 Achieved a Bachelor in Theology from the University of Athens, completed between October 1979 and December 1987. 🎓 Earned an MSc in Information Technology from the University of Nottingham in 1989, followed by a PhD in Artificial Intelligence from the same university in 1992. 🧠 Thesis: “Integrating Logic and Objects for Knowledge Representation and Reasoning.”

Experience

📘 From Feb. 1982 to June 1982, I served as a part-time lab professor at PALMER Higher School of Electronics in Greece, teaching Electronics Lab. My full-time teaching journey began at TEI of Athens (1982-84), where I covered courses like Electrotechnics and Circuit Theory. My secondary education tenure (1984-92) focused on electrical engineering subjects. I then transitioned to higher education, teaching at TEI of Kozani and Chalkida, and later at the University of Nottingham (1990-92). From 1995-2006, I was a senior researcher and lecturer at the University of Patras, ultimately becoming a professor (2009-2023). Now, I am a Professor Emeritus. 🎓🔬

Projects

From June 1993 to November 1995, I managed the CTI team for the DELTA-CIME project, developing a knowledge-based production control system. I led several initiatives, including the MEDFORM project for multimedia education and the national project for educational software in chemistry. As a senior researcher, I contributed to intelligent systems for tele-education and hybrid knowledge representation. I led multiple European projects like MENUET, AVARES, and TESLA, focusing on innovative education through virtual reality. My work aims to enhance learning experiences across disciplines, involving collaboration with various international partners. 🌍📚💻🎓

Research focus

Ioannis Hatzilygeroudis specializes in artificial intelligence and its applications in various domains, particularly in agriculture and healthcare. His research includes intelligent systems for diagnosing farmed fish diseases, employing deep learning techniques for image analysis, and exploring natural language processing methods. He has contributed significantly to the development of expert systems and reinforcement learning approaches to improve disease prediction in aquaculture. Additionally, his work in sentiment analysis and e-learning demonstrates a commitment to advancing educational technologies and user experience. Hatzilygeroudis’s interdisciplinary approach combines computer science with practical applications, making significant strides in health and environmental management. 🌱🐟💻📊

Publication focus

Using Level-Based Multiple Reasoning in a Web-Based Intelligent System for the Diagnosis of Farmed Fish Diseases

An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture

Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism

Expert Systems for Farmed Fish Disease Diagnosis: An Overview and a Proposal

Expert Systems for Farmed Fish Disease Diagnosis: An Overview and a Proposal

A Convolutional Autoencoder Approach for Boosting the Specificity of Retinal Blood Vessels Segmentation

Evaluating Deep Learning Techniques for Natural Language Inference