Reid, Jonathan (2018) Source Detection Performance Comparison between Potential and Turbulent Flow: An Artificial Neural Network Approach. Bachelor's Thesis, Artificial Intelligence.
|
Text
AI_BA_2018_JonathanReid.pdf Download (626kB) | Preview |
|
![[img]](https://fse.studenttheses.ub.rug.nl/style/images/fileicons/text.png) |
Text
Toestemming.pdf Restricted to Registered users only Download (94kB) |
Abstract
In this paper, we compare the performance of several neural networks on underwater source detection using an artificial lateral line on realistic simulated flow data. Previous work has shown how the fish lateral line helps fish determine a source's location in water. Based on this, a number of studies using potential flow data have shown neural networks to be able to perform source location and source angle estimation satisfactorily. In the present study, we aimed to recreate those results using more realistic turbulent flow data generated by using a three dimensional Stam-type fluid solver to simulate flow produced by a sphere moving through a two dimensional plane of fluid. The more realistic data did indeed prove to be more difficult for the neural networks. Even so, the source location estimation suffered a loss in accuracy of only a factor of two, while the angle estimation suffered a loss in accuracy of a factor of five.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Netten, S.M. van and Pirih, P. and Wolf, B.J. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 12 Jul 2018 |
| Last Modified: | 13 Jul 2018 14:05 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/17793 |
Actions (login required)
 |
View Item |