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undergraduate thesis
Računalni model strojnog učenja za sortiranje otpada
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. urn:nbn:hr:235:528266
Baronica, Dora
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

Cite this document

Baronica, D. (2023). Računalni model strojnog učenja za sortiranje otpada (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:528266

Baronica, Dora. "Računalni model strojnog učenja za sortiranje otpada." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:235:528266

Baronica, Dora. "Računalni model strojnog učenja za sortiranje otpada." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2023. https://urn.nsk.hr/urn:nbn:hr:235:528266

Baronica, D. (2023). 'Računalni model strojnog učenja za sortiranje otpada', Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 13 July 2024, https://urn.nsk.hr/urn:nbn:hr:235:528266

Baronica D. Računalni model strojnog učenja za sortiranje otpada [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2023 [cited 2024 July 13] Available at: https://urn.nsk.hr/urn:nbn:hr:235:528266

D. Baronica, "Računalni model strojnog učenja za sortiranje otpada", Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:235:528266

Metadata
TitleRačunalni model strojnog učenja za sortiranje otpada
Title (english)Machine learning computational model for waste sorting
AuthorDora Baronica
MentorTomislav Stipančić (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2023-09-28, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Precision Mechanical Engineering
Abstract
Globalni problem upravljanja smećem zahtijeva kreativne pristupe pojednostavljenju postupaka sortiranja. Moderni tokovi otpada predstavljaju jedinstvene izazove za konvencionalne pristupe, zbog čega je nužno korištenje najsuvremenijih tehnologija. Ovim radom cilj je prikazati mogućnosti korištenja potencijala strojnog učenja u svrhu pronalaženja novih strategija za pristup navedenoj problematici. Prikazat će se razvoj računalnog modela strojnog učenja za sortiranje otpada tako što će se trenirati već postojeća mrežna arhitektura u programskom jeziku Python uz korištenje potrebnih biblioteka. Prije samog procesa bilo je potrebno proučiti i odabrati adekvatne metode dubokog učenja prikladne za rješavanje zadanog problema. Isto tako, kako su temelj strojnog učenja odgovarajući skupovi podataka, bilo ih je potrebno pronaći, obraditi i organizirati. U ovom slučaju skup podataka predstavlja skup fotografija raznih vrsta otpada. Nakon svega, prikazat će se evaluacija rada modela kroz eksperimente u sklopu Laboratorija za projektiranje izradbenih i montažnih sustava.
Abstract (english)
The global problem of waste management requires creative approaches to simplify sorting procedures. Modern waste streams present unique challenges to conventional approaches, which is why the use of state-of-the-art technologies is necessary. The aim of this paper is to show the possibilities of using the potential of machine learning in order to find new strategies for approaching the mentioned problem. The development of a machine learning computational model for waste sorting will be presented by training an already existing neural network in the Python programming language using the necessary libraries. Before the process itself, it was necessary to study and select adequate deep learning methods suitable for solving the given problem. Also, since the basis of machine learning is the appropriate datasets, it was necessary to find, process and organize them. In this case, the dataset represents a set of photographs of various types of waste. After all, the evaluation of the model's work through experiments as part of the Laboratory for Designing Manufactured and Assembled Systems will be presented.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:528266
Study programmeTitle: Mechanical Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-09-26 23:14:27