Javascript must be enabled for the correct page display

Underwater object localization and identification using an extreme learning machine and artificial lateral line sensors

Brussen, Arjen (2018) Underwater object localization and identification using an extreme learning machine and artificial lateral line sensors. Bachelor's Thesis, Artificial Intelligence.

[img]
Preview
Text
AI_BA_2018_ARJENBRUSSEN.pdf

Download (712kB) | Preview
[img] Text
Toestemming.pdf
Restricted to Registered users only

Download (94kB)

Abstract

Fish possess a lateral line organ, consisting of flow detectors along the body of the fish. With this organ, they are able to detect water flow in surrounding waters. Using excitation patterns of these flow detectors, the location and direction of moving objects can be inferred. Using these findings as a basis, this paper shows if and how we can identify these objects in terms of shape, size and velocity using an artificial lateral line in combination with an extreme learning machine (ELM). Input normalization based on the maximum velocity outperforms other tested techniques. For shape identification, three object shapes were used (single object, school of fish, snake) and an identification accuracy of 66.3% is reported. For size identification (0.025m, 0.05m, 0.1m), the ELM achieves an overall accuracy of 68.3%. For velocity identification (0.065m/s, 0.13m/s,0.26m/s), an overall accuracy of 53.8% was measured.

Item Type: Thesis (Bachelor's Thesis)
Supervisor:
Supervisor nameSupervisor E mail
Netten, S.M. vanS.M.van.Netten@rug.nl
Wolf, B.J.B.J.Wolf@rug.nl
Pirih, P.P.Pirih@rug.nl
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 09 Jul 2018
Last Modified: 11 Jul 2018 14:09
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/17716

Actions (login required)

View Item View Item