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doctoral thesis
Deep learning-based analysis of fuel spray images
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:190245
Huzjan, Fran
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Huzjan, F. (2023). Deep learning-based analysis of fuel spray images (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:190245

Huzjan, Fran. "Deep learning-based analysis of fuel spray images." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:190245

Huzjan, Fran. "Deep learning-based analysis of fuel spray images." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:190245

Huzjan, F. (2023). 'Deep learning-based analysis of fuel spray images', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 22 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:190245

Huzjan F. Deep learning-based analysis of fuel spray images [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 November 22] Available at: https://urn.nsk.hr/urn:nbn:hr:168:190245

F. Huzjan, "Deep learning-based analysis of fuel spray images", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:190245

Metadata
TitleDeep learning-based analysis of fuel spray images
Title (croatian)Analiza slika spreja goriva dubokim učenjem
AuthorFran Huzjan
MentorSven Lončarić (mentor)
Committee memberMarko Subašić (predsjednik povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
Committee memberMilan Vujanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-18, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Universal decimal classification
(UDC)		004 - Computer science and technology. Computing. Data processing
Abstract
Direct rail injection systems in internal combustion engines depend on various parameters, including nozzle diameter, injection pressure, and injector geometry, to shape the spray strategies. These factors influence macroscopic parameters such as spray cone angle, penetration length, and spray area, all crucial for optimizing engine efficiency and reducing pollutants and harmful emissions. To estimate these parameters from spray images, we have utilized computer vision methods, particularly deep learning techniques. In this research, two primary methods were proposed for spray image analysis: spray image segmentation and direct parameter estimation from image sequences. A lightweight segmentation neural network, termed Min U-Net, was developed for the first method. Min U-Net achieved state-of-the-art results while being faster and requiring fewer parameters, outperforming other tested methods in estimating spray macroscopic parameters. For the second method, a regression deep neural network was employed, achieving a more accurate estimate by using a sequence of images as input rather than a single image. An extension was proposed to the second method, incorporating an additional fully connected layer to enhance prediction accuracy. These methods offer promising future research avenues in spray analysis and could help optimize engine efficiency and emission reduction.
Abstract (croatian)
Sustavi s izravnim ubrizgavanjem goriva u motore s unutarnjim izgaranjem ovise o različitim parametrima, uključujući promjer mlaznice, tlak ubrizgavanja i geometriju injektora, kako bi se oblikovale strategije spreja. Ti faktori utječu na makroskopske parametre kao što su kut spreja, duljina penetracije i površina spreja, od kojih su svi ključni za optimizaciju efikasnosti motora i smanjenje zagađenja i štetnih emisija. Kako bi se procijenili makroskopski parametre iz slika spreja, korištene su metode računalnog vida, posebice metode dubokog učenja. U ovom istraživanju predložene su dvije metode za analizu slika spreja: segmentacija slike spreja i izravna procjena parametara iz sekvenci slika spreja. Za prvu metodu razvijena je neuronska mreža niske složenosti za segmentaciju, nazvana Min U-Net. Min U-Net postigao je suvremene rezultate, dok je pritom bio brži i manje složenosti od suvremenih metoda, nadmašivši druge testirane metode u procjeni makroskopskih parametara spreja. Za drugu metodu koristila se regresijska duboka neuronska mreža, postižući točniju procjenu korištenjem sekvence slika spreja umjesto jedne slike kao ulaz. Također je i predloženo proširenje druge metode, uključivanjem dodatnog potpuno povezanog sloja kako bi se poboljšala točnost predviđanja. Ove metode nude obećavajuće pravce budućih istraživanja u analizi spreja i mogu pomoći u optimizaciji učinkovitosti motora i smanjenju štetnih emisija.
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:168:190245
Promotion2024
Study programmeTitle: Computer Science
Study programme type: university
Study level: postgraduate
Academic / professional title: Doktor znanosti (Doktor znanosti)
Type of resourceText
Extentix, 77 str. : graf. prikazi ; 31 cm.
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-02-13 15:23:30