Name
Title | ||
|---|---|---|
Adversarial Semi-Supervised Learning for Diagnosing Faults and Attacks in Power Grids |
Abstract | ||
|---|---|---|
This paper proposes a novel adversarial scheme for learning from data under harsh learning conditions of partially labelled samples and skewed class distributions. This novel scheme integrates the generative ability of the state-of-the-art conditional generative adversarial network with the semi-supervised deep ladder network and semi-supervised deep auto-encoder. The proposed generative-adversari... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TSG.2021.3061395 | IEEE Transactions on Smart Grid |
Keywords | DocType | Volume |
Generative adversarial networks,Training,Phasor measurement units,Data models,Semisupervised learning,Generators,Gallium nitride | Journal | 12 |
Issue | ISSN | Citations |
4 | 1949-3053 | 3 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Farajzadeh-Zanjani | 1 | 16 | 2.97 |
| Ehsan Hallaji | 2 | 9 | 2.81 |
| Roozbeh Razavi-Far | 3 | 95 | 19.93 |
| Mehrdad Saif | 4 | 27 | 8.84 |
| Masood Parvania | 5 | 31 | 13.72 |