DC Element | Wert | Sprache |
---|---|---|
dc.contributor.advisor | Hensel, Marc | - |
dc.contributor.author | Soud, Elias | - |
dc.date.accessioned | 2024-06-26T10:36:28Z | - |
dc.date.available | 2024-06-26T10:36:28Z | - |
dc.date.created | 2022-07-18 | - |
dc.date.issued | 2024-06-26 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12738/15979 | - |
dc.description.abstract | Diese Arbeit beschreibt den Entwurf und das Training eines künstlichen neuronalen Netzwerks mit Keras Tensorflow high level Machine Learning framework. Dieses Training ermöglicht es dem Netzwerk, Widerstände in Eingabebildern nach ihrem Wert zu klassifizieren. Das Modell soll nach dem Training und der Auswertung gespeichert werden, um dann in diesem Projekt von einer Android-Anwendung verwendet zu werden. | de |
dc.description.abstract | This work describes designing and training an Artificial Neural Network with Keras Tensorflow high level Machine Learning framework. this training enables the network to classify resistors within input images according to their value. The model is to be saved after training and evaluation, in order to then be employed in this project by an Android application. | en |
dc.language.iso | en | en_US |
dc.subject | Artificial Neurons | en_US |
dc.subject | Artificial Neural Networks | en_US |
dc.subject | Image Processing | en_US |
dc.subject | Supervised learning | en_US |
dc.subject | Reinforced Learning | en_US |
dc.subject | Training Neural Networks | en_US |
dc.subject | Parameters and Hyper-parameters | en_US |
dc.subject | weight | en_US |
dc.subject | bias | en_US |
dc.subject | learning rate | en_US |
dc.subject | batch size | en_US |
dc.subject | epoch | en_US |
dc.subject | data set | en_US |
dc.subject | optimizer | en_US |
dc.subject | Android | en_US |
dc.subject | Künstliche Neuronen | en_US |
dc.subject | Künstliche Neuronale Netze | en_US |
dc.subject | Bildverarbeitung | en_US |
dc.subject | Überwachtes Lernen | en_US |
dc.subject | Verstärktes Lernen | en_US |
dc.subject | Training Neuronaler Netze | en_US |
dc.subject | Parameter und Hyper-Parameter | en_US |
dc.subject | Gewicht | en_US |
dc.subject.ddc | 600: Technik | en_US |
dc.subject.ddc | 620: Ingenieurwissenschaften | en_US |
dc.title | Classification of Electrical Resistors by a Deep Learning Model embedded in an Application for Mobile Devices | en |
dc.type | Thesis | en_US |
openaire.rights | info:eu-repo/semantics/openAccess | en_US |
thesis.grantor.department | Department Informations- und Elektrotechnik | en_US |
thesis.grantor.universityOrInstitution | Hochschule für Angewandte Wissenschaften Hamburg | en_US |
tuhh.contributor.referee | Rauscher-Scheibe, Annabella | - |
tuhh.identifier.urn | urn:nbn:de:gbv:18302-reposit-188232 | - |
tuhh.oai.show | true | en_US |
tuhh.publication.institute | Department Informations- und Elektrotechnik | en_US |
tuhh.publication.institute | Fakultät Technik und Informatik | en_US |
tuhh.type.opus | Bachelor Thesis | - |
dc.type.casrai | Supervised Student Publication | - |
dc.type.dini | bachelorThesis | - |
dc.type.driver | bachelorThesis | - |
dc.type.status | info:eu-repo/semantics/publishedVersion | en_US |
dc.type.thesis | bachelorThesis | en_US |
dcterms.DCMIType | Text | - |
tuhh.dnb.status | domain | en_US |
item.advisorGND | Hensel, Marc | - |
item.creatorGND | Soud, Elias | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
item.creatorOrcid | Soud, Elias | - |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
item.openairetype | Thesis | - |
Enthalten in den Sammlungen: | Theses |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
---|---|---|---|---|
BA_Classification of Electrical Resistors by a Deep Learning Model_geschwärzt.pdf | 3.66 MB | Adobe PDF | Öffnen/Anzeigen |
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