Name
Abstract | ||
|---|---|---|
Anomaly detection is an important problem with many applications in industry. This paper introduces a new methodology for detecting anomalies in a real laser heating surface process recorded with a high-speed thermal camera (1000 fps, 32x32 pixels). The system is trained with non-anomalous data only (32 videos with 21500 frames). The proposed method is built upon kernel density estimation and is capable of detecting anomalies in time-series data. The classification should be completed in-process, that is, within the cycle time of the workpiece. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/978-1-61499-682-8-137 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Kernel density estimation,Anomaly detection,Time-series,Laser surface heating process | Computer vision,Anomaly detection,Data mining,Computer science,Laser,Artificial intelligence | Conference |
Volume | ISSN | Citations |
284 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Atienza | 1 | 0 | 0.34 |
| Concha Bielza | 2 | 909 | 72.11 |
| Javier Diaz | 3 | 0 | 0.34 |
| Pedro Larrañaga | 4 | 3882 | 208.54 |