prikaz prve stranice dokumenta Automatizirano upravljanje sustavima korištenjem senzorskih podataka
No public access
master's thesis
Automatizirano upravljanje sustavima korištenjem senzorskih podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. urn:nbn:hr:168:304635
Malbašić, Nino
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Malbašić, N. (2017). Automatizirano upravljanje sustavima korištenjem senzorskih podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:304635

Malbašić, Nino. "Automatizirano upravljanje sustavima korištenjem senzorskih podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:304635

Malbašić, Nino. "Automatizirano upravljanje sustavima korištenjem senzorskih podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:304635

Malbašić, N. (2017). 'Automatizirano upravljanje sustavima korištenjem senzorskih podataka', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:304635

Malbašić N. Automatizirano upravljanje sustavima korištenjem senzorskih podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2024 July 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:304635

N. Malbašić, "Automatizirano upravljanje sustavima korištenjem senzorskih podataka", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:168:304635

Metadata
TitleAutomatizirano upravljanje sustavima korištenjem senzorskih podataka
Title (english)System Management Using Sensor Data
AuthorNino Malbašić
MentorSaša Dešić (mentor)
Committee memberSaša Dešić (predsjednik povjerenstva)
Committee memberGordan Ježić (član povjerenstva)
Committee memberMario Kušek (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2017-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Internet stvari je popularni koncept koji omogućuje povezivanje senzorskih sustava ili uređaja i stvari koje se svakodnevno koriste, pružajući mogućnost da stvari komuniciraju međusobno te s višim slojevima sustava slanjem i primanjem podataka. Algoritmi strojnog učenja nude optimizirane metode analize i obrade velikih količina podataka te su vrlo pogodni za unošenje inteligencije u sustave. Kombinacija velike količine podataka koju produciraju senzorski sustavi te algoritama učenja unutar sustava interneta stvari mogu stvoriti vrlo kvalitetne sustave koji mijenjaju način života čovjeka ili industrije u kojoj su primijenjeni. Fokus ovog rada je na razvoju pametnog sustava koji će veliku količinu podataka prikupljenih sa senzorskih sustava iskoristiti u svrhu automatiziranog upravljanja sustavom. Kao konkretan primjer implementiran je sustav pametnog termostata koji uči čovjekove navike tijekom zadnjih 30 dana korištenja te je sposoban preuzeti automatsku kontrolu nad podešavanjem temperature na termostatu. Implementacija se sastoji od nekoliko dijelova: Mongo baze podataka, podatkovnog modela, upravljača pametnog termostata, algoritma učenja te korisničkog sučelja. Mongo baza podataka popunjena je senzorskim podacima koje za potrebe ovog rada simulira korisnik promjenom stanja termostata. Centralni upravljač pametnog termostata brine o komunikaciji s bazom podatka, pokretanju i obradi rezultata algoritma učenja te posluživanju korisničkog sučelja. Implementacija algoritma učenja u obliku je algoritma k – medoida.
Abstract (english)
Internet of things is a popular concept that provides connectivity between sensors or devices and everyday objects, enabling them to communicate with each other or with higher layers by sending and receiving data. Machine learning algorithms provide optimized data analytics and processing methods. They are very suitable for implementing artificial intelligence into systems. A combination of large sets of data, that are produced by sensors, and learning algorithms, implemented in the IoT systems, can create systems of great quality. Also, it can bring life-changing effects for humans or an industry where they are applied. The focus of this thesis is a development of a smart system which will use a large set of data collected from sensors and its purpose is managing automated systems. The concrete implementation is a smart thermostat system which learns human habits for last 30 days and it can take automatic control of thermostats temperature. The implementation consists of several parts: Mongo database, data model, smart thermostat controller, learning algorithm and user interface. Mongo database with sensors data which is, for this purpose, simulated with users’ actions of changing thermostat value. Central smart thermostat controller takes care of communication with the database, starting learning algorithm, result analysis and serving data for the user interface. Learning algorithm contains an implementation of k – medoids clustering.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:304635
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
Terms of useLicence In copyright icon
Public note
Created on2019-03-13 22:30:51