Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Bertalanič, Blaž"'
With the process of democratization of the network edge, hardware and software for networks are becoming available to the public, overcoming the confines of traditional cloud providers and network operators. This trend, coupled with the increasing im
Externí odkaz:
http://arxiv.org/abs/2407.11905
Domain specific digital twins, representing a digital replica of various segments of the smart grid, are foreseen as able to model, simulate, and control the respective segments. At the same time, knowledge-based digital twins, coupled with AI, may a
Externí odkaz:
http://arxiv.org/abs/2406.06566
Due to growing population and technological advances, global electricity consumption, and consequently also CO2 emissions are increasing. The residential sector makes up 25% of global electricity consumption and has great potential to increase effici
Externí odkaz:
http://arxiv.org/abs/2405.18869
Autor:
Rožanec, Jože M., Petelin, Gašper, Costa, João, Bertalanič, Blaž, Cerar, Gregor, Guček, Marko, Papa, Gregor, Mladenić, Dunja
In many cases, a machine learning model must learn to correctly predict a few data points with particular values of interest in a broader range of data where many target values are zero. Zero-inflated data can be found in diverse scenarios, such as l
Externí odkaz:
http://arxiv.org/abs/2310.08088
In recent years, the traditional feature engineering process for training machine learning models is being automated by the feature extraction layers integrated in deep learning architectures. In wireless networks, many studies were conducted in auto
Externí odkaz:
http://arxiv.org/abs/2308.03530
Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand response ap
Externí odkaz:
http://arxiv.org/abs/2307.09244
The so-called black-box deep learning (DL) models are increasingly used in classification tasks across many scientific disciplines, including wireless communications domain. In this trend, supervised DL models appear as most commonly proposed solutio
Externí odkaz:
http://arxiv.org/abs/2305.10060
Location based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes. The increasingly popular Deep Learning (DL) artificial intelligence methods perform very well i
Externí odkaz:
http://arxiv.org/abs/2211.01759
Publikováno v:
Computer Communications, Volume 212, 1 December 2023, Pages 353-365
In recent years, much work has been done on processing of wireless spectrum data involving machine learning techniques in domain-related problems for cognitive radio networks, such as anomaly detection, modulation classification, technology classific
Externí odkaz:
http://arxiv.org/abs/2210.02899
The digital transformation of the energy infrastructure enables new, data driven, applications often supported by machine learning models. However, domain specific data transformations, pre-processing and management in modern data driven pipelines is
Externí odkaz:
http://arxiv.org/abs/2205.04267