KEYNOTE LECTURES

Prof. Jungwon Yoon received the Ph.D. degree in mechatronics from Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea, in 2005. From 2010 to 2011, he was a Visiting Fellow at Functional and Applied Biomechanics Section, Rehabilitation Medicine of Department, Clinical Center, National Institutes of Health (NIH), Bethesda, MD, USA. He was also a Senior Researcher in Electronics Telecommunication Research Institute (ETRI), Daejeon, South Korea, in 2005. From 2005 to 2017, he was a Professor with the School of Mechanical and Aerospace Engineering, Gyeongsang National University, Jinju, South Korea. In 2017, he joined the School of Integrated Technology, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea, where he is currently a Professor. Since 2019, he has been the director of Research Center for Nanorobotics in Brain (RCNB) in GIST. He is an also principal project director in several research projects supported by South Korea governments in the fields of brain stimulation, drug delivery, and hyperthermia using nano robotics. His current research interests include magnetic particle imaging and nano-robotics. He has authored or coauthored more than 200 peer-reviewed international journal and conference articles. Dr. Yoon is an Associate Editor for Frontiers in Robotics and AI and served as a Technical Editor for IEEE/ASME Transactions on Mechatronics.

Magnetic nanoparticles (MNPs) are a promising candidate for use as carriers in targeted drug delivery systems because they can function at both the cellular and molecular levels. Electromagnetic sensing and guidance schemes using magnetic nanoparticles (MNPs) can allow a nanotechnology-based drug delivery approach to be feasible for targeted therapies for brain diseases such as brain cancer, stroke, and Alzheimer's disease. Magnetic particle imaging (MPI) is a fast and sensitive imaging modality that is used to measure the spatial distribution of MNPs. MPI systems offer spatial resolutions on the millimeter scale and high temporal resolutions, which fulfill the requirements for cardiovascular, neurological, and peripheral vascular applications. An electromagnetic navigation scheme using MPI can deliver magnetic nanoparticles to efficiently targeted regions of a brain with feedback information while minimizing particles’ aggregation and passing through blood brain barrier (BBB). This talk will show how MPI scheme can be combined together with the electromagnetic guidance scheme. The proposed MPI-based targeting approaches can be finally adapted to medical robotic platforms for brain drug targeting, brain stimulation, and brain hyperthermia. 

Ton Duc Do received the B.S. and M.S. degrees in electrical engineering from the Hanoi University of Science and Technology, Hanoi, Vietnam, in 2007 and 2009, respectively, and the Ph.D. degree in electrical engineering from Dongguk University, Seoul, South Korea, in 2014. From 2008 to 2009, he worked at the Division of Electrical Engineering, Thuy Loi University, Vietnam, as a Lecturer. He was at the Division of Electronics and Electrical Engineering, Dongguk University, as a Postdoctoral Researcher, in 2014. He was also a Senior Researcher at the Pioneer Research Center for Controlling Dementia by Converging Technology, Gyeongsang National University, South Korea, from May 2014 to August 2015. Since September 2015, he has been an Assistant Professor and then Associate Professor at the Department of Robotics and Mechatronics, Nazarbayev University, Kazakhstan. His research interests include the field of advanced control system theories, electric machine drives, renewable energy conversion systems, uninterruptible power supplies, electromagnetic actuator systems, targeted drug delivery systems, and nanorobots. He has been an Associate Editor of IEEE Access since 04/2017 and IEEE Robotics and Automation Letters since 09/2023. He has been also a Guest Editor for special issues of several journals such as Mathematical Problems in Engineering, Electronics, Energies, and Sensors. He received the Best Research Award from Dongguk University in 2014, the Most Cited Paper Award from Wind Energy in 2020-2021, and the Outstanding Associate Editor of IEEE Access in 2021 and 2022. He has been recently listed top 2% of scientists with both single-year and career-wide tables based on a systematic bibliometric study by researchers from Stanford University, in 2021 and 2022.

This keynote presentation will delve into the latest advancements in wind speed estimation and prediction algorithms, along with the crucial role of data analysis in the control, operation, and planning of wind power plants. We'll begin by providing an overview of Wind Energy Conversion Systems (WECSs). Next, we'll explore the development of Disturbance Observers (DO) designed for estimating aerodynamic torque and wind speed, essential for optimizing Maximum Power Point Tracking in WECSs. We'll delve into the evolution of conventional DOs into high-order DOs, providing a detailed examination of this problem. Our presentation will include comprehensive simulations and experimental results to illustrate these concepts. In the second part of our keynote, we'll share findings related to short-term wind speed prediction. Lastly, utilizing real-world wind speed and generated power data from various wind power plants, we will demonstrate how we can derive long-term predictions of wind speed and wind power. These insights will prove invaluable for enhancing the operational efficiency of WECSs.