Hyosung Ahn

Title: Distributed Control in Cyber-Physical Energy Networks and Formation Systems


Abstract: This talk is composed of three parts. The first part presents a framework considering decentralized energy coordination and generation, and flow control for supply-demand balance in distributed cyber-physical networks. Consensus-like schemes using only relative information are employed to produce energy coordination, generation, and flow control signals. For the supply-demand balance, it is required to determine the amount of energy needed at each distributed resource. Also, due to the different generation capacities of each energy resource, coordination of energy flows among distributed energy resources is essentially required. The second part introduces a decentralized formation control via orientation alignment and position estimation. In this approach, it is assumed that the agents measure the relative positions of their neighbors with respect to their own local reference frames, the orientations of which are not aligned initially. We then propose a formation control strategy consisting of an orientation alignment, a position estimation, and a position control law. We show that the proposed control strategy allows the agents to achieve the desired formation if some specified conditions are satisfied. The last part of this talk presents real experimental results using quadcopters. Mainly, we present three experimental results for a rendezvous between two quadcopters, for a cultural performance show with more than 10 quadcopters, and for a distributed formation flying.

Boreom Lee

Title: Electroencephalographic Study for Human Speech Processing


Abstract: At present, speech and language processing are the core topics in the machine learning research field. Especially, the recent development of deep learning theories makes it very promising and useful tools for the human life. However, we only partly understand the processing in the brain related to the language processing, in spite of its remarkable development in the engineering area. Therefore, my research group has performed studies to investigate auditory neural processes corresponding to the explicit and implicit speech processing by human subjects. We did several electroencephalographic(EEG) experiments for the speech perception and imagination using multiple vowel syllables and words. After data acquisition, we applied diverse machine learning algorithms (including neuromorphic hardware neural network) for the categorical classification of those speech generated EEG signals. In conclusion, we obtained successful classifying performances for EEG signal features, which will be utilized for brain computer interface (BCI) technology and clinical application such as neurorehabilitation and language disorder treatment.

Last updated on October 30, 2018 by Annie