Name
Abstract | ||
|---|---|---|
In this paper, we present a real time treat classification approach to be used in a distributed acoustic sensing system that is developed for monitoring linear assets with a maximum length of 50 kms. The Convolutional Neuaral Network (CNN) based deep learning approach is used for treat classification. The classification accuracies and execution times for neural networks with different architecture and complexity are measured. The proposed approach for classifying all the detected treats without decreasing the detection accuracy is introduced. The maximum allowable execution time for the network structure that is appropriate for the proposed approach is analyzed for the worst case scenario. Hence, the most appropriate network architecture selection can be performed based on classification accuracy and also applicability in real-time criterion. |
Year | Venue | Keywords |
|---|---|---|
2018 | Signal Processing and Communications Applications Conference | Distributed acoustic sensing,phase-OTDR,deep learning,convolutional neural networks,CNN,threat detection,threat classification,real-time processing |
Field | DocType | ISSN |
Real time classification,Pattern recognition,Computer science,Convolutional neural network,Network architecture,Execution time,Distributed acoustic sensing,Artificial intelligence,Worst-case scenario,Deep learning,Artificial neural network | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hakan Maral | 1 | 0 | 0.34 |
| Toygar Akgun | 2 | 90 | 9.39 |
| Aktas, Metin | 3 | 4 | 3.44 |