8 citations · 8 across the 4 of their papers we have counts for
6 papers
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events
Brandon Foggo, Koji Yamashita, Nanpeng Yu
This paper introduces pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data. We have trained this model on…
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events -- Part I: Overview and Results
Brandon Foggo, Koji Yamashita, Nanpeng Yu
We present pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data. We have trained this model on thousands o…
Power System Event Identification based on Deep Neural Network with Information Loading
Jie Shi, Brandon Foggo, Nanpeng Yu
Online power system event identification and classification is crucial to enhancing the reliability of transmission systems. In this paper, we develop a deep neural network (DNN) b…
On the Maximum Mutual Information Capacity of Neural Architectures
Brandon Foggo, Nanpeng Yu
We derive the closed-form expression of the maximum mutual information - the maximum value of obtainable via training - for a broad family of neural network architectures.…
Improving Supervised Phase Identification Through the Theory of Information Losses
Brandon Foggo, Nanpeng Yu
This paper considers the problem of Phase Identification in power distribution systems. In particular, it focuses on improving supervised learning accuracies by focusing on exploit…
Information Losses in Neural Classifiers from Sampling
Brandon Foggo, Nanpeng Yu, Jie Shi +1
This paper considers the subject of information losses arising from the finite datasets used in the training of neural classifiers. It proves a relationship between such losses as…