prikaz prve stranice dokumenta Predviđanje budućih očitanja uređaja Interneta stvari putem dubokog učenja
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master's thesis
Predviđanje budućih očitanja uređaja Interneta stvari putem dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:683324
Ahac, Luka
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Ahac, L. (2023). Predviđanje budućih očitanja uređaja Interneta stvari putem dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:683324

Ahac, Luka. "Predviđanje budućih očitanja uređaja Interneta stvari putem dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:683324

Ahac, Luka. "Predviđanje budućih očitanja uređaja Interneta stvari putem dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:683324

Ahac, L. (2023). 'Predviđanje budućih očitanja uređaja Interneta stvari putem dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 07 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:683324

Ahac L. Predviđanje budućih očitanja uređaja Interneta stvari putem dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 August 07] Available at: https://urn.nsk.hr/urn:nbn:hr:168:683324

L. Ahac, "Predviđanje budućih očitanja uređaja Interneta stvari putem dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:683324

Metadata
TitlePredviđanje budućih očitanja uređaja Interneta stvari putem dubokog učenja
Title (english)Predicting IoT Readings With Deep Learning
AuthorLuka Ahac
MentorVladimir Čeperić (mentor)
Committee memberVladimir Čeperić (predsjednik povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
Committee memberTvrtko Mandić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Rad se bavi problemom predviđanja očitanja uređaja interneta stvari. Objašnjeni su koncepti interneta stvari, dubokog učenja i povratnih neuronskih mreža. Napravljene su tri različite arhitekture modela povratnih neuronskih mreža koje se razlikuju po tipovima ćelija. Korištene su osnovna RNN ćelija, LSTM ćelija i GRU ćelija. Proveden je postupak pretraživanja po rešetci kako bi se pronašli optimalni hiperparametri. Postupak je proveden nad jednostavnijem datasetu koji sadrži samo značajku temperature zraka i na potpunom datasetu koji sadrži sve dostupne vremenske značajke poput temperature, tlaka i vlage zraka, brzine i smjera vjetra i slično. Predstavljeni su i analizirani rezultati istreniranih modela te je razvijen sustav za alarmiranje na ThingsBoard IoT platformi koji aktivira alarme pri predviđanju temperature koja izlazi iz definiranih raspona.
Abstract (english)
The paper deals with the problem of predicting Internet of Things device readings. Concepts of Internet of Things, Deep Learning and Recurrent Neural Networks are explained. Three different architectures of recurrent neural network models were created, differing by cell type. Basic RNN cell, LSTM cell and GRU cell were used. A grid search procedure was performed to find the optimal hyperparameters. The procedure was performed on a simpler dataset containing only the air temperature feature and on a complete dataset containing all available weather features such as air temperature, air pressure, air humidity, wind speed and direction, and others. The results of the trained models were presented and analyzed, and an alarming system was developed on the ThingsBoard IoT platform that activates alarms when predicting a temperature that goes out of the defined ranges.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:683324
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
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Created on2023-10-11 08:15:37