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Dosimetry of ionizing radiation with an artificial neural network: With an unsorted, sequential input
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this thesis the verification of a neural network’s proficiency at labeling ionizing radiation particles from the unsorted output of a timepix3 camera is attempted. Focus is put on labeling single particles in separate data sequences with slightly preprocessed input data. Preprocessing of input data is done to simplify the patterns that should be recognized. Two major choices were available for this project, Elman-network and Jordan-network. A more complicated type was not an option because of the longer time needed to implement them. The network type chosen was Elman because of freedom in context size. The neural network is created and trained with the TensorFlow API in python with labeled data that was not created by hand. The network recognized the length difference between gamma particles and alpha particles. Beta particles were not considered by the network. It is concluded that the Elman-style network is not proficient in labeling the sequences, which were considered short enough and to have simple enough input data. A more modern network type is therefore likely required to solve this problem.

Place, publisher, year, edition, pages
2018. , p. 38
Keywords [en]
Artificial neural network, recurent neural network, ionizing radiation, dosimetry, timepix3 radiation camera
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-34068Local ID: ET-V18-G3-001OAI: oai:DiVA.org:miun-34068DiVA, id: diva2:1229833
Subject / course
Electrical Engineering ET2
Educational program
Master of Science in Electronics Engineering TELSA 300 higher education credits
Supervisors
Examiners
Available from: 2018-07-02 Created: 2018-07-02 Last updated: 2018-07-02Bibliographically approved

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CiteExportLink to record
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  • apa
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