Name
Title | ||
|---|---|---|
Error Detection Of Ocean Depth Series Data With Area Partitioning And Using Sliding Window |
Abstract | ||
|---|---|---|
In the ocean around the world, depth series ocean data of temperature and salinity have being measured. However, it is difficult to discriminate the errors from the normal data since the variation of ocean areas are different. In this research, using hierarchical clustering, we partitioned the ocean into some areas so that the ocean data have the same variation in each area. Then, transforming the ocean data into sets of sliding windows in consideration of depth series, we applied some anomaly detection methodologies. Finally, we succeeded in assigning high anomaly scores on errors that seemed to be normal. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICMLA.2016.91 | 2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016) |
Field | DocType | Citations |
Hierarchical clustering,Anomaly detection,Sliding window protocol,Sea surface temperature,Pattern recognition,Computer science,Remote sensing,Error detection and correction,Ocean surface topography,Artificial intelligence,Temperature measurement,Temperature salinity diagrams | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shogo Hayashi | 1 | 0 | 0.34 |
| Satoshi Ono | 2 | 219 | 39.83 |
| Shigeki Hosoda | 3 | 0 | 0.68 |
| Masayuki Numao | 4 | 390 | 89.56 |
| Ken-Ichi Fukui | 5 | 39 | 13.37 |