‘Adequate amount of data’‐driven system design theory: how far can we know/control/protect?

Abstract

In contrast to the natural tendency of many researchers to focus on collecting as much data as possible, Assistant Professor Tomonori Sadamoto, from the Department of Mechanical Engineering and Intelligent Systems at the University of Electro-Communications in Japan, believes that the pursuit of big data is not always desirable. He is promoting a shift towards the acceptance of a “adequate amount of data”-driven system design theory. Sadamoto is concentrating on data-driven methodologies and their application in social systems. Through key international collaborations with colleagues at leading institutions, he is advancing his research. His work on data-driven methodologies focuses on interdisciplinary studies that combine machine learning and control theory. His more applied work primarily falls within the realm of smart grids. In his projects, he formulates his questions mathematically from the perspective of control theory. For instance, Sadamoto has developed a novel mathematical tool known as the VARX (vector autoregressive with exogenous input) framework, which facilitates the tractable analysis of dynamic systems. Using this new tool, he has developed data-dependent system identification analyses when only an “insufficient amount of data” is available. Furthermore, for the first time, Sadamoto was able to demonstrate that the informativeness of data in a certain class of dynamic output controller design is equivalent to the identification of the target system. His efforts are aimed at expanding the horizons of these novel control theories into the field of smart grids.