11 citations · 13 across the 4 of their papers we have counts for
4 papers
Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Dani Korpi, Rachel Wang, Jerry Wang +20
Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Var…
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems
Kursat Rasim Mestav, Xinyi Wang, Lang Tong
A deep learning approach is proposed to detect data and system anomalies using high-resolution continuous point-on-wave (CPOW) or phasor measurements. Both the anomaly and anomaly-…
Universal Data Anomaly Detection via Inverse Generative Adversary Network
Kursat Rasim Mestav, Lang Tong
The problem of detecting data anomaly is considered. Under the null hypothesis that models anomaly-free data, measurements are assumed to be from an unknown distribution with some…
Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning
Kursat Rasim Mestav, Jaime Luengo-Rozas, Lang Tong
The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. T…