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. πŸ§ πŸ”‹πŸ€–πŸ“šπŸ₯Š

Publication profile

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Education

With a PhD in Energy from Cranfield University (2020-2024) πŸŽ“, He focused on AI for energy flexibility modelling and decarbonisation 🌱, vehicle-to-vehicle energy trading, and EV owner behaviour analysis πŸš—. 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 πŸ“š. During an AI exchange at Imperial College London (2018), I designed computer vision algorithms for a robotic arm πŸ€–. My BEng from Nanjing University (2015-2019) involved a thesis on 3D printing and courses in mechanics and materials πŸ› οΈ.

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. πŸŒπŸ€–πŸ”§πŸ‘¨β€πŸ”§πŸ“·βœ…

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 πŸ“ˆπŸ”‹. 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 πŸ“±πŸ . 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 πŸš—πŸ’‘. All projects met quality standards and were successfully delivered βœ….

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. πŸŒπŸ”‹πŸš—πŸ’‘πŸ“‰πŸ”’

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. πŸ“šπŸ€–πŸ’»πŸŒ

Publication profile

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Education

πŸ“š From September 1968 to June 1974, completed secondary education, earning a Certificate of High School Graduation in Science. πŸŽ“ 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. πŸ“œ From February to June 1983, obtained a Certificate of Educational Studies from PATES of SELETE, Greece. πŸ“– Achieved a Bachelor in Theology from the University of Athens, completed between October 1979 and December 1987. πŸŽ“ 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

πŸ“˜ 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. πŸŽ“πŸ”¬

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. πŸŒπŸ“šπŸ’»πŸŽ“

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. πŸŒ±πŸŸπŸ’»πŸ“Š

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