7th SCS - 2023 Symposium
7th SCS-2023 - 3-5 December 2023
Symposium Keynote Speakers and Details
Symposium President Opening Ceremony Speech
09:00 am, 3rd of December 2023,
Zain - E-learning Center
University of Bahrain - Sukhair - Kingdom of Bahrain
Opening Speech by His Excellency
University of Bahrain President
Honoring Symposium Patron and Symposium General Chairperson
Her Excellency Speech is
Towards Smart Cities and Digital Twin Cities: A Novel Paradigm
December 3, 2023, 09:00+03 - 09:10+03
03-05 December 2023 Symposium Keynote Speakers
Power Electronics — the Key Technology for Grid Integration
Professor Dr. Frede Blaabjerg
Professor of Electric Power Systems and Microgrids, Aalborg University, Denmark
The energy paradigms in many countries (e.g., Germany and Denmark) have experienced a significant change from fossil-based resources to clean renewables (e.g., wind turbines and photovoltaics) in the past few decades. The scenario of highly penetrated renewables is going to be further enhanced– Denmark expects to be 100 percent fossil-free by 2050. Consequently, it is required that the production, distribution, and use of the energy should be as technologically efficient as possible and incentives to save energy at the end-user should also be strengthened. In order to realize the transition smoothly and effectively, energy conversion systems, currently based on power electronics technology, will again play an essential role in this energy paradigm shift. Using highly efficient power electronics in power generation, power transmission/distribution and end-user application, together with advanced control solutions, can pave the way for renewable energies. In light of this, some of the most emerging renewable energies — , e.g., wind energy and photovoltaic, which by means of power electronics are changing character as a major part in the electricity generation —, are discussed. Issues like technology development, implementation, power converter technologies, control of the systems, and synchronization are addressed. Special focuses are paid on the future trends in power electronics for those systems like how to lower the cost of energy and to develop emerging power devices and better reliability tool.
About Professor Frede Blaabjerg
Frede Blaabjerg (S’86–M’88–SM’97–F’03) was with ABB-Scandia, Randers, Denmark, from 1987 to 1988. From 1988 to 1992, he got the PhD degree in Electrical Engineering at Aalborg University in 1995. He became an Assistant Professor in 1992, an Associate Professor in 1996, and a Full Professor of power electronics and drives in 1998 at AAU Energy. From 2017 he became a Villum Investigator. He is honoris causa at University Politehnica Timisoara (UPT), Romania in 2017 and Tallinn Technical University (TTU), Estonia in 2018.His current research interests include power electronics and its applications such as in wind turbines, PV systems, reliability, Power-2-X, power quality and adjustable speed drives. He has published more than 600 journal papers in the fields of power electronics and its applications. He is the co-author of eight monographs and editor of fourteen books in power electronics and its applications.He has received 38 IEEE Prize Paper Awards, the IEEE PELS Distinguished Service Award in 2009, the EPE-PEMC Council Award in 2010, the IEEE William E. Newell Power Electronics Award 2014, the Villum Kann Rasmussen Research Award 2014, the Global Energy Prize in 2019 and the 2020 IEEE Edison Medal. He was the Editor-in-Chief of the IEEE TRANSACTIONS ON POWER ELECTRONICS from 2006 to 2012. He has been Distinguished Lecturer for the IEEE Power Electronics Society from 2005 to 2007 and for the IEEE Industry Applications Society from 2010 to 2011 as well as 2017 to 2018. In 2019-2020 he served as a President of IEEE Power Electronics Society. He has been Vice-President of the Danish Academy of Technical Sciences. He is nominated in 2014-2021 by Thomson Reuters to be between the most 250 cited researchers in Engineering in the world.
Satellite Imagery and Big Data for Smart Cities
Dr Michael Kio
Fellow of the Institution of Engineering and Technology, James Clarke School of Engineering, University of Maryland College Park, USA
Satellite technology provides images for every location on planet earth with onboard computers processing large amounts of data, producing insightful information and analysis. This is an application of big data, going above and beyond not only reading images obtained from space but also improving lives here on earth. Satellites implementing artificial intelligence (AI) are beginning to be utilized for real time images and analysis on how smart cities are transforming. One example is real time changes of when green areas are converted to built areas. By training computers on what to spot in images processed or produced by satellites, machine learning algorithms are implemented on large and expanding data sources which reveals how city development aligns with zoning and planning of communities exposed to flooding and climate change. From this big data, the machine learning algorithms predicts the temporal and spatial distribution of land use and land cover which are analyzed and utilized for the management of smart cities.
About Dr. Michael Kio
Dr Michael Kio a fellow of the institution of engineering and technology IET has his PhD in Aerospace Engineering from Cranfield University in the United Kingdom and was a chief engineer in a national space agency and a consultant in satellite and communication technology, energy systems and project management. Dr Kio worked as a postdoctoral associate in the University of Maryland College Park and is currently an assistant research professor in the faculty of engineering University of Maryland. Dr Kio is a project management professional (PMP) in the United States of America and a senior member of the American Institute of Aeronautics and Astronautics (AIAA), where he chaired several technical sessions and reviewed manuscripts in the institution’s journals and conference proceedings.
Machine Learning Algorithms and Deep Learning Networks for Smart Grid Data Analytics
Professor Dr. D. Devaraj
Senior Professor, Department of Electrical & Electronics Engineering, Kalasalingam Academy of Research and Education,
Globally, the modernization of traditional power grid into smart grid is taking place. Smart Grid (SG) is a system of information and communication technologies integrated with electricity network, and customer end-use technologies. The establishment of smart grid enables the reduction in energy consumption, effective use of renewable energy and reduction in carbon emissions. Advanced Metering infrastructure (AMI) is an important component in the smart grid. The AMI contains smart meters installed at the customer premises, communication network and a meter data management system which collect information on thousands of users. Smart meters measure and communicate electrical consumption data from customer premises to the energy provider through the communication network. The smart meter data collected at a frequency of every 15 minutes to one hour provide utilities with detailed information about the energy consumption. The collected smart meter time series data can be analyzed further for efficient and sustainable operation of the Smart Grid. Moreover, end users can control their power usage and bills with this information. In recent years, Machine learning has proven to be a powerful tool for deriving insights from data. Machine learning is a form of data-driven programming that automatically learns based on data which can facilitate the analysis of large and heterogeneous data like the smart meter data. Also, the smart meter data can be combined with the other relevant variables like weather and demographic data to enrich the data analytics in smart grid operation. This talk will focus on leveraging the Machine learning tools like decision tree, support vector machine, clustering algorithms and Deep learning networks for various services like load profiling, energy consumption forecasting, electricity theft detection, demand response etc. using smart meter data. Case studies based on real time smart meter data will also be presented.
About Professor D. Devaraj
Professor D. Devaraj completed his B.E and M.E in Electrical & Electronics Engineering and Power System Engineering in the year 1992 and 1994, respectively, from Thiagarajar College of Engineering, Madurai. From 1994 to 1997, he worked as a Lecturer in Arulmigu Kalasalingam College of Engineering, Krishnankoil. He obtained his Ph.D degree from IIT Madras, Chennai in the year 2001. Since 2001, he is working as a faculty in the Electrical & Electronics Engineering department of Kalasalingam Academy of Research and Academy. He has organized 4 International Conferences, 9 National Conferences, 6 seminars and conducted 25 workshops. He has authored 2 text books, Power system analysis and Power system control. He has also co-authored 3 text books. He has published more than 180 papers in Journals and presented 250 papers in conferences. He has chaired 20 technical sessions in various National and International Conferences. He is the reviewer of IEEE Transaction on Fuzzy System, IEEE Transaction on System, Man, Cybernetics, IET Proceedings on Generation, Transmission & Distribution, International Journal on Electric Power & Energy Systems, Electric Power Components and Systems, Neuro computing and Applied Soft computing Journal. He has Supervised 28 PhD, 2 M.S and 25 M.E theses. Presently, he is guiding 6 Ph.D scholars. He has undertaken 4 research projects sponsored by DST, Government of India. Currently, he is the principal investigator for the DST-FIST project on “Establishment of Real time Simulation Platform for Renewable energy technology and Micro grid System Research”. His research interest includes Artificial Intelligence, Evolutionary algorithms, IoT and Data Mining Power system optimization, Renewable Energy and Smart Grid. He was the Head of the Electrical & Electronics Engineering Department from 2001 to 2009, Deputy Director, R&D, from 2008 to 2009, Dean, R&D, from 2009 to 2012, Dean, Planning & Development from 2012 to 2014, Director, Academic from 2014 to 2019 and Dean, School of Electronics and Electrical Technology from 2016 to 2021. Presently, he is a Senior Professor in the Electrical and Electronics Engineering Department of Kalasalingam Academy of Research and Academy (KARE), Krishnankoil. Dr.D.Devaraj has been recognized in the field of Artificial Intelligence and Image analysis as one among the top 2 % scientists/researchers across the world by Stanford University researchers in 2020, 2021 and 2022. He is a senior member of IEEE, and a member of IEEE Power & Energy System Society. He acted as the Secretary of IEEE Madras Section during 2020-21 and Vice-Chair (Educational Activities) of IEEE India Council during 2020-22.
Illustrating Edge AI Techniques and Tools towards Digitally Transformed Cities
Professor Dr. Pethuru Raj
Chief Architect and Vice President, Edge AI Division, Reliance Jio Platforms Ltd, Bangalore, India
With the astounding growth in the artificial intelligence (AI) technology ecosystem, a variety of everyday problems across industry verticals are being attempted to be automated and accelerated. Today we have a bevy of pioneering AI algorithms and models empowering business behemoths and start-ups to be right and relevant to their customers and consumers. With the ready availability of big data and greater computational power, AI-based data analytics brings forth predictive, prescriptive and personalized insights in time. The knowledge discovered gets disseminated to appropriate systems and devices to exhibit intelligent behavior in their assignments and obligations. There are a dazzling array of cutting-edge technologies and state-of-the-art platforms for simplifying and speeding up AI model engineering, evaluation, optimisation and deployment tasks. Now with the exponential growth of connected devices (alternatively referred to as networked embedded systems or IoT edge devices) joining mainstream computing in the digital era, the computing activity is being systematically shifted to IoT edge devices, which individually and collectively perform proximate data processing to extract timely and actionable insights, which, in turn, results in a slew of real-time and real-world services and applications. By translating heavyweight AI models into lightweight models using a suite of compression techniques and tools, hosting and running AI models on edge devices and their clusters become the talk of the town. Such a transition empowers edge devices to be intelligent in their operations, offerings and outputs. In this talk, I would like to demystify the edge AI paradigm and how it is going to be a game-changing phenomenon for the entire society. Further on, I will focus on detailing some prominent personal and professional use cases of edge AI. Especially setting and sustaining intelligent environments and enterprises is being simplified and speeded up through the smart leverage of the distinct power of the edge AI paradigm.
About Professor Pethuru Raj
Professor is leading a team of competent AI engineers and data scientists to bring forth and deploy highly optimized AI models for a variety of problems in the telecommunication, retail, healthcare, manufacturing and cloud management domains. Focusing on incremental learning algorithms towards Real-time AI, Generative AI, Explainable AI, and Efficient AI systems.
Effective and Efficient Riverine Waste Management of Building Sustainable Smart Cities
Dr. Sharina Yunus
Electrical and Electronic Engineering Department and currently serves as the
Deputy Director of the Enterprise Office for Universiti Teknologi Brunei
Effective and efficient riverine waste management is an essential component of building sustainable Smart Cities that prioritises the health and well-being of its residents. In recent years, the problem of riverine waste management has become an increasingly urgent issue in many ASIAN countries. Rapid economic growth, urbanization and several other several factors have led to a surge in waste generation, which often ends up in rivers. During this keynote speech, attendees will gain a vivid understanding of the current state of riverine waste management in ASIAN countries and the major challenges that must be overcome. The speaker will highlight promising multi-modal approaches that are being used to tackle riverine waste management and showcase the vital role of technology in addressing these challenges. Emerging technologies such as blockchain and artificial intelligence will also be discussed. Drawing from personal experience and research, the speaker will provide valuable insights into effective strategies for managing riverine waste. Overall, the speech intends to provide a timely analysis of this pressing problem, emphasizing the importance of collaboration and innovation in combating plastic pollution and the role it can play in building sustainable Smart Cities. .
About Dr. Sharina Yunus
Dr. Sharina Yunus is an Assistant Professor in the Electrical and Electronic Engineering Department and currently serves as the Deputy Director of the Enterprise Office for Universiti Teknologi Brunei. In this role, she has been responsible for developing the IP and commercialization policies to enhance the Innovation Ecosystem at the University. She has recently completed a research project funded by the ASEAN research grant program on ICT startup issues and challenges. Dr. Sharina has worked with ASEAN European Foundation (ASEF) as a mentor for the 2nd AI Innovation Lab on The Universities Role in Artificial Intelligence AI Innovation Ecosystem. She has also served as a guest panel expert in Digital Education Learning 4 All (DEL4ALL) under the European Research Project. She is currently a member of the National Nano Technology Committee. She firmly believes in giving back to the community and regularly dedicates her time to mentoring robotic teams to promote STEM education. Dr. Sharina has served as a member of the judging panel in the MAKEX International Robotic Competition. She is also the founder of the Learning Ladders Society, a non-profit organization for Children with Autism and Related Disorders. Dr. Sharina has a Doctorate in Optoelectronics from the University of Bath, United Kingdom. Her research interest lies in integrated robotics and AI, robotics for STEM education, Smart Sustainable Cities, Agrotechnology, and PV renewable energy.