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master's thesis
Implementacija i verifikacija algoritma za procjenu podudarnosti obrazaca
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. urn:nbn:hr:168:026531
Sakač, Filip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Sakač, F. (2017). Implementacija i verifikacija algoritma za procjenu podudarnosti obrazaca (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:026531

Sakač, Filip. "Implementacija i verifikacija algoritma za procjenu podudarnosti obrazaca." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:026531

Sakač, Filip. "Implementacija i verifikacija algoritma za procjenu podudarnosti obrazaca." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:026531

Sakač, F. (2017). 'Implementacija i verifikacija algoritma za procjenu podudarnosti obrazaca', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 23 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:026531

Sakač F. Implementacija i verifikacija algoritma za procjenu podudarnosti obrazaca [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2024 November 23] Available at: https://urn.nsk.hr/urn:nbn:hr:168:026531

F. Sakač, "Implementacija i verifikacija algoritma za procjenu podudarnosti obrazaca", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:168:026531

Metadata
TitleImplementacija i verifikacija algoritma za procjenu podudarnosti obrazaca
Title (english)Implementation and verification of an algorithm for pattern similarity estimation
AuthorFilip Sakač
MentorVedran Podobnik (mentor)
Committee memberVedran Podobnik (predsjednik povjerenstva)
Committee memberŽeljka Car (član povjerenstva)
Committee memberMarina Bagić Babac (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2017-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Poslovanje u modernom svijetu svakim danom zahtijeva obradu sve većih skupova podataka. Porast procesorske moći računala i razvoj algoritama strojnog učenja omogućuju računalima samostalnu obradu takvih skupova podataka. Ovaj rad opisuje primjenu algoritama strojnog učenja u domeni tvorničkog testiranja elektroničke komunikacijske opreme. Rezultate testova potrebno je analizirati kako bi se smanjio broj proizvoda koji je nepotrebno proglašen neispravnim. Problem se svodi na problem klasifikacije rezultata testova na ispravne i neispravne. Strojno učenje je implementirano kao komponenta alata FRAG BDA, kako bi se ubrzao proces analize rezultata testova. U radu su opisani korišteni algoritmi, i analizirani njihovi rezultati i korisnost krajnjim korisnicima alata. Zaključeno je da se za opisani problem najbolji rezultati postižu algoritmom nasumične šume zajedno s algoritmima Simhash i Mutual information.
Abstract (english)
Business in the modern world requires working with bigger and bigger data sets. The rise in processing power of computers and the development of machine learning algorithms allow computers to process such data sets all by themselves. This paper describes the use of machine learning algorithms in the production testing of electronic communication equipment. The results of these tests need to be analyzed in order to minimize the number of products wrongly declared faulty. The task is to classify failed test as false positives or actual faults in the products. Machine learning algorithms where developed as part of the FRAG BDA tool in order to speed up the analysis of test results. This paper describes machine learning algorithms, and compares their results and usefulness to the users. It is concluded that the random forest classifier used together with Simhash and Mutual information algorithms shows the best results for the described problem.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:026531
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2019-03-14 00:34:35