diplomsko delo
Denis Feratović (Author), Nejc Ilc (Mentor)

Abstract

Svet interneta stvari postaja vse bolj priljubljen, saj nam olajša vsakodnevna opravila. Pri tem lahko nastanejo velike množice podatkov in z njimi potreba po njihovi obdelavi in analizi, kjer nam lahko področje strojnega učenja dodatno razširi funkcionalnost sistemov. Hkrati pa tovrstna tematika vse pogosteje postaja del industrijskih obratov, kjer sta hitrost in kakovost obdelave primarna vidika. Namen izbrane diplomske naloge je meritev in analiza električnega toka električnega mešalnika za hrano ter posledično napoved vsebine naprave s pomočjo metod strojnega učenja. Električni mešalnik je pri tem zgolj poceni in praktičen nadomestek pravih industrijskih naprav. Izvedli smo več meritev posameznih vhodnih sestavin mešalnika, ki smo jih kasneje uporabili v štirih metodah strojnega učenja. Metodam smo nato ovrednotili čas izvajanja in natančnost napovedovanja. Pogosto smo uspeli napovedati stanje naprave, a smo opazili največ težav pri napovedovanju vhodnih sestavin, ki so bile po gostoti oziroma viskoznosti najbolj podobne.

Keywords

IoT;električni mešalnik;električni tok;napovedovanje;uvrščanje;časovne vrste;visokošolski strokovni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [D. Feratović]
UDC: 004.8:004.7:641.54(043.2)
COBISS: 148030979 Link will open in a new window
Views: 53
Downloads: 9
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Other data

Secondary language: English
Secondary title: Prediction of ingredients inside an electric shaker
Secondary abstract: The world of IoT is becoming increasingly popular as it makes everyday tasks easier but can lead to large data sets. We need to process and analyze the obtained data, and this is where the field of machine learning can further extend the functionality of our systems. At the same time, IoT and machine learning are becoming part of industrial plants, where speed and quality of processing are primary considerations. The aim of this diploma thesis is to measure and analyze the electric current of an electric food mixer and predict the content of the device using machine learning algorithms. An electric mixer is merely a cheap and practical substitute for real industrial devices. We conducted several measurements of individual input ingredients of the mixer, which were subsequently used in four machine learning methods. The methods were then evaluated for their execution time and prediction accuracy. While we were often able to accurately predict the state of the device, we observed the greatest difficulty in predicting input ingredients that were most similar in density or viscosity.
Secondary keywords: IoT;electric shaker;machine learning;electric current;prediction;classification;time series;computer science;diploma;Strojno učenje;Internet stvari;Kuhinjski mešalniki;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000470
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 47 str.
ID: 18286088