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http://hdl.handle.net/2445/180920
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DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Estradé Albiol, Sònia | - |
dc.contributor.advisor | Pozo Bueno, Daniel del | - |
dc.contributor.author | Roset Tomàs, Marc | - |
dc.date.accessioned | 2021-10-29T13:14:37Z | - |
dc.date.available | 2021-10-29T13:14:37Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.uri | http://hdl.handle.net/2445/180920 | - |
dc.description | Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2021, Tutors: Sònia Estradé, Daniel Del Pozo Bueno | ca |
dc.description.abstract | In this work we test machine learning tools such as the Support Vector Machine algorithm and neural network models on the task of Electron Energy-Loss Spectroscopy (EELS) spectra classification. Given many sample spectra of EELS applied on wüstite and magnetite nanocubes, we train both models to determine the oxidation state of iron. We show that SMV exhibits a good performance on classifying clean data, and we demonstrate the capability of neural networks of producing robust results given shifted data | ca |
dc.format.extent | 5 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | ca |
dc.rights | cc-by-nc-nd (c) Roset, 2021 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.source | Treballs Finals de Grau (TFG) - Física | - |
dc.subject.classification | Aprenentatge automàtic | cat |
dc.subject.classification | Espectroscòpia de pèrdua d'energia d'electrons | cat |
dc.subject.classification | Treballs de fi de grau | cat |
dc.subject.other | Machine learning | eng |
dc.subject.other | Electron energy loss spectroscopy | eng |
dc.subject.other | Bachelor's theses | eng |
dc.title | Machine learning for iron oxide identification from oxygen K edge in EELS spectra | eng |
dc.type | info:eu-repo/semantics/bachelorThesis | ca |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca |
Appears in Collections: | Treballs Finals de Grau (TFG) - Física |
Files in This Item:
File | Description | Size | Format | |
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ROSET TOMÀS MARC_4253527_assignsubmission_file_TFG-Roset-Tomas-Marc.pdf | 327.89 kB | Adobe PDF | View/Open |
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