Rolf Mueller has studied bat biosonar from the perspectives of biophysics and bioinspired engineering for over 25 years. He received his degrees from the University of Tuebingen in Germany and was a postdoc at Yale University, and has held faculty positions in Germany, Denmark, China, and the USA. He currently serves as the Lynn professor of Mechanical Engineering at Virginia Tech and the university's Center for Bioinspired Science and Technology. In his international activities, he has conducted research on east and southeast Asian bats for almost 20 years. His work on bat biosonar and its engineering replications has been published in some of the most reputed journals in the relevant fields such Proceedings of the National Academy of Sciences, Physics Review Letters, Nature Machine Learning, and the Journal of Experimental Biology. He has been a Fellow of the Acoustical Society of America since 2019.

Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA

Accomplishing useful tasks in complex natural environments poses thehighest-level challenge for autonomous systems. Among numerous other difficulties, the sensing aspect of this problem continues to elude state-of-the-art autonomy solutions. The approaches used in attempts to address these challenges are overwhelmingly based on camera imaging and LIDAR which could be seen as a three-dimensional extension of the imagining approach. Similarly, the approaches to data processing and inference often rely on vision-inspired paradigms such as the recognition of deterministic shapes. However, bat species that rely on biosonar sensing to navigate dense vegetation demonstrate that the sensory information to support these demanding tasks can be obtained from one-dimensional echo trains received at the two ears in a highly parsimonious fashion. This parsimony is evident not only in the low input data rates, but also in the small sizes of the animals brains, and the small data sets that are sufficient for bats to learn their tasks. All these abilities are even more remarkable since bats - as small mammals - are not well suited as a platform for conventional sonar sensing paradigms that rely heavily on the ability to form narrow beams with large apertures. Applying a combination of biomimetic robotics and deep-learning data analysis can shed light on how bats encode and extract the sensory information that they need to navigate in dense vegetation and explain capabilities such as location identification and passageway finding. The abilities of bats indicated the feasibility of alternative AI paradigms for dealing with complex environments that are not based on concepts from vision, but can derive all necessary sensory information from statistical invariants in signals that have to be regarded as random for lack of knowledge and can hence not support template-matching based approaches.

Seung-Bok Choi is currently working as a leading professor (碩座敎授) at Mechanical Engineering Department of The State University of New York, Korea (SUNY Korea) located in Incheon Global Campus, Korea, after retirement in 2021 from Inha University where he had worked for 30 years His main research fields are vibration control and control theory development associated with smart materials such as magnetorheological (MR) fluid and piezoelectric materials. So far, he has published over 800 articles based on Web of Science and also published more than 30 books/book chapters. He is still actively serving editorship of 20 international journals including Smart Materials and Structures, Journal of Intelligent Material Systems and Structures and Scientific Report. He has received many distinguished awards from ASME (America Society of Mechanical Engineers), IMehE (Institute of Mechanical Engineers, England), NAEK (The National Academy of Engineering of Korea) and KAST (Korea Academy of Science and Technology). In addition, He received Order of Science and Technology Merit in 2019.

Department of Mechanical Engineering, The State University of New York, Korea (SUNY Korea)

In this talk, unique features and benefits of several smart materials which are inherently adaptable to vibration control of flexible structures and dynamic systems are briefly introduced in terms of saliant characteristics of actuators and sensors. Then, some examples of vibration control systems such as vibration control of a semi-active suspension system in vehicle engineering and effective control strategies for smart material-based actuators are discussed, followed by recent research trends for vibration control strategies which combine passive, semi-active and active methods. It is then concluded that my lifetime devoted to vibration control study over 35 years will be summarized showing themes and scholar evaluation platforms such as Google Scholar and ScholarsGPS.Com.