Name
Title | ||
|---|---|---|
Supporting Maritime Situation Awareness Using Self Organizing Maps and Gaussian Mixture Models |
Abstract | ||
|---|---|---|
Maritime situation awareness is of importance in a lot of areas --e.g. detection of weapon smuggling in military peacekeeping operations, and harbor traffic control missions for the coast guard. In this paper, we have combined the use of Self Organizing Maps with Gaussian Mixture Models, in order to enable situation awareness by detecting deviations from normal behavior in an unsupervised way. Initial results show that simple anomalies can be detected using this approach. |
Year | Venue | Keywords |
|---|---|---|
2008 | SCAI | normal behavior,harbor traffic control mission,military peacekeeping operation,coast guard,maritime situation awareness,simple anomaly,initial result,self organizing maps,gaussian mixture models,situation awareness,maritime situation,technology,anomaly detection,gaussian mixture model,computer science,data mining |
Field | DocType | Volume |
Data mining,Anomaly detection,Peacekeeping,Simulation,Computer science,Situation awareness,Self-organizing map,Artificial intelligence,Guard (information security),Machine learning,Mixture model | Conference | 173 |
ISSN | Citations | PageRank |
0922-6389 | 8 | 0.52 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Riveiro | 1 | 133 | 18.64 |
| Fredrik Johansson | 2 | 57 | 9.36 |
| Göran Falkman | 3 | 173 | 22.13 |
| Tom Ziemke | 4 | 681 | 67.03 |