Dr Mark Scott of Miami University

Power electronics generate electromagnetic interference (EMI) due to semiconductors' high-speed switching transitions and naturally occurring parasitic coupling paths.  EMI makes power conversion hardware less reliable and presents compatibility issues for surrounding equipment.  While it is generally a nuisance, EMI also contains useful diagnostic information about the power electronics, its energy sources, and loads.  This is because EMI changes with the age of components. 

This presentation teaches the fundamentals of using conducted EMI as a tool for diagnosing capacitor health.  The talk begins by comparing this method to existing approaches used in literature and applications.  Next, it reviews the precursors for capacitor failure.  Afterwards, it illustrates, via circuit analysis techniques, how certain precursors impact conducted EMI. It further demonstrates the use of digital signal processing (DSP) techniques and machine learning (ML) tools as a means for improving diagnostic capabilities of prognostic and health management (PHM) systems.  The presentation includes experimental results to support the theoretical analysis provided to the audience.

Dr Ran Zhang of Miami University

Unmanned aerial vehicles (UAVs) have been demonstrating impressive potentials in next generation wireless communications. Compared to the terrestrial cellular base stations, UAVs equipped with wireless transceivers can serve as mobile base stations, and stand out in providing highly on-demand services with flexible 3D mobility, better wireless connectivity with higher chance of Line-of-Sight links, and much lower deployment cost with almost infrastructure-free network construction. As promising the UAVs are, how to optimally regulate the UAV network when the serving UAV crew dynamically changes remains embryonic. To this end, our work investigates how the UAV communication networks should responsively handle and further proactively control the dynamic change of the UAV crew. Specifically, responsive self-regulation of the network is first studied when one or more UAVs are about to quit or join the network, with considering dynamic user distribution. We target an optimal UAV trajectory control strategy which can relocate the UAVs whenever the UAV crew is about to change, rather than passively dispatch the UAVs after the change. Moving one step further, a proactive control strategy is developed for the solar-powered self-sustainable UAV communication network. The strategy can proactively control the quit and join-in of the UAVs by pre-shaping their solar-charging plan leveraging on the time-variability of solar radiation intensity and user traffic demand. The research outcomes are expected to provide valuable inspirations and benchmarking to autonomous AI powered management of aerial access communication networks under a dynamic network setup.