Lambert, Daan (2022) Swimmer Classificationusing an Artificial Lateral Lineand Time Series Approaches. Master's Thesis / Essay, Artificial Intelligence.
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Abstract
The lateral line system is a sensory organ most commonly found in aquatic vertebra. These systemstransduce movement of the water into a stimulus. Artificial analogues of such systems can be con-structed to replicate this conversion from movement to signal. In this thesis the signal produced byan artificial lateral line (ALL) is used for classification purposes. Recordings were made of 5 swim-mers passing by an ALL. We showed success in identifying the swimmers through the utilisationof several classification techniques. We tested two transformation techniques as data representationsalong side the raw data. The two data transformations which were tested are the spectrogram, and thediscrete wavelet transform of the data. Both of these data representations incorporate the frequencydomain as well as the time domain. For the classification we used three different machine learningapproaches. These are a nearest neighbours approach, a time series forests approach, and a deeplearning approach. All three of these approaches represent a different niche in the field of time seriesclassification. We show that the raw data representation outperforms the spectrogram in all situations.The wavelet transform outperforms the raw data depending on the classifier used. The classifiers pro-duce similar results on the data set, when comparing the best performing data representations. Eachof the classifiers can be useful depending on circumstances and aim of the system. Future researchs
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Supervisor name: | Netten, S.M. van and Wolf, B.J. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 28 Jan 2022 10:33 |
| Last Modified: | 28 Jan 2022 10:33 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/26507 |
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