Name
Title | ||
|---|---|---|
MAGAN: A masked autoencoder generative adversarial network for processing missing IoT sequence data |
Abstract | ||
|---|---|---|
•Our work proposed the MAGAN model, which is less affected by the data loss rate than the baseline comparison model.•When the sensor collects data, it will be disturbed because of the change of environment.•After data filling and noise reduction work is completed, data analysis and mining can be carried out more effectively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patrec.2020.07.025 | Pattern Recognition Letters |
Keywords | DocType | Volume |
Missing data,Time series data,Sensor data,GAN,Deep learning | Journal | 138 |
ISSN | Citations | PageRank |
0167-8655 | 0 | 0.34 |
References | Authors | |
0 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wang Weihan | 1 | 0 | 0.34 |