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Pregled bibliografske jedinice broj: 900527

Detection of Faults in Electrical Panels Using Deep Learning Method


Mlakić, Dragan; Nikolovski, Srete; Baus, Zoran
Detection of Faults in Electrical Panels Using Deep Learning Method // Proceedings of International Conference on Smart Systems and Technologies 2017 (SST 2017) / Drago Žagar, Goran Martinović, Snježana Rimac Drlje, Kruno Miličević (ur.).
Osijek: Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2017. str. 55-61 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Detection of Faults in Electrical Panels Using Deep Learning Method

Autori
Mlakić, Dragan ; Nikolovski, Srete ; Baus, Zoran

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of International Conference on Smart Systems and Technologies 2017 (SST 2017) / Drago Žagar, Goran Martinović, Snježana Rimac Drlje, Kruno Miličević - Osijek : Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2017, 55-61

ISBN
978-1-5386-3776-0

Skup
International Conference on Smart Systems and Technologies 2017 (SST 2017)

Mjesto i datum
Osijek, Hrvatska, 18-20.10.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Deep Learning ; electrical components ; thermal imaging ; picture analysis ; Convolution Neural Network Introduction

Sažetak
In the image analysis, a big trend within the field of artificial intelligence is using the Deep Learning method, which is an upgrade of the existing neural network adaptive architecture (ANN). Deep Learning is a major new field in machine learning that encompasses a wide range of neural network architectures designed to perform various tasks. In the thermography energy sector, examples that are processed on a daily basis are sampling of active energy components, focus segmentation, and fault classification. The most popular network architecture for Deep Learning in image analysis is the convolution neural network (CNN), where traditional machine learning methods require determination and calculation, from which the algorithm training comes. Deep Learning approach captures important features as well as the appropriate weight of these attributes to make decision for new data. This paper describes a method and tool that are available to build and conduct an effective analysis of the Deep Learning Method for electrical components.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek