magistrsko delo
Urban Marovt (Author), Marko Bajec (Mentor)

Abstract

Povezljivost in enostavnost integracije naprav IoT sta ena izmed poglavitnih razlogov, zakaj je v zadnjih letih močno naraslo zanimanje za pametne domove. Starejše tehnologije, kot je KNX, imajo veliko težavo slediti razvoju in zagotavljati enako uporabniško izkušnjo, kot jo danes ponujajo največji proizvajalci na trgu naprav IoT za pametni dom. Veliko prepreko na trgu pametnih domov KNX predstavlja predvsem konfiguracija, saj ta ne vsebuje informacij o končnih napravah v uporabnikovem domu, ampak zgolj konfiguracijo posameznih funkcij. V magistrskem delu smo razvili komponento, ki iz projektne datoteke, kjer je zapisana celotna konfiguracija funkcij v KNX-domu, rekonstruira končne naprave v primerni obliki za vizualizacijo ali glasovno opravljanje naprav v domu. V prvem koraku smo sestavili podatkovno množico, kjer so funkcije razdeljene v 65 različnih razredov funkcionalnosti. Iz krajših tekstovnih razlag v angleškem in nemškem jeziku smo nato z metodami procesiranja naravnega jezika in večrazredno klasifikacijo izdelali napovedni model za predikcijo funkcijskih razredov. Na podlagi napovedanih razredov in preostalih parametrov konfiguracije smo potem rekonstruirali naprave v domu. Na koncu smo razvili aplikacijski modul, ki uporabniku omogoča, da naloži projektno datoteko doma, preko le-te pa je nato zgenerirana ustrezna konfiguracija. Implementirano rešitev smo primerjali z alternativno rešitvijo in ugotovili, da smo z našo metodo precej povečali število pravilno zaznanih naprav.

Keywords

pametni domovi;KNX;procesiranje naravnega jezika;večrazredna klasifikacija;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [U. Marovt]
UDC: 004
COBISS: 18738265 Link will open in a new window
Views: 1460
Downloads: 212
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Other data

Secondary language: English
Secondary title: Device reconstruction based on KNX project file
Secondary abstract: Connectivity and integration simplicity are two of the main features of the IoT market which drive the rising popularity of Smart Home in the last years. Older technologies like KNX are struggling to follow the rapid development and to deliver the same user experience as IoT giants do. The biggest barrier for the KNX market lies in its configuration specifics, which is not aware of end-customer devices included in the home but only saves the configuration of each functionality. In this master's thesis we developed a component, which reconstructs the complete home configuration into a form ready for visualization or voice control of specific devices based on KNX project file, which includes a complete configuration of KNX Smart Home. In the first phase we prepared a dataset of KNX functions, which are distributed into 65 classes. Using natural language processing techniques and multi-class classification algorithms, we then constructed a prediction model for predicting specific function class based on English and German short text function descriptions. Using this information and other parameters from KNX project file we then group functions into meaningful devices included in the home. At the end, we developed an application module, which includes an element for uploading KNX project file based on which we then generate the adequate configuration. Comparison between the implemented module and the alternative solution showed that we have successfully increased the number of correctly detected devices.
Secondary keywords: smart home;KNX;natural language processing;multi-class classification;
Type (COBISS): Master's thesis/paper
Study programme: 0
Thesis comment: Univ. v Ljubljani, Fak. za matematiko in fiziko, Oddelek za matematiko, Računalništvo in matematika - 2. stopnja
Pages: 60 str.
ID: 11237662