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Query: "author" (Gregor Bo%C5%BEi%C4%8D) .

301 - 310 / 375
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301.
Pomen kliničnih raziskav za bolnike z rakom : priročnik za bolnike z rakom
2005, dictionary, encyclopaedia, lexicon, manual, atlas, map

Abstract: Priročnik je namenjen onkološkim bolnikom, njihovim družinam in prijateljem. Pomagal jim bo razumeti , kaj klinične raziskave pravzaprav so, kako so zasnovane in kako potekajo.
Published in DiRROS: 25.03.2019; Views: 1939; Downloads: 486
.pdf Full text (5,07 MB)

302.
Construction of Heuristic for Protein Structure Optimization using deep reinforcement learning
Rok Hribar, Jurij Šilc, Gregor Papa, 2018, published scientific conference contribution

Published in DiRROS: 15.03.2019; Views: 2413; Downloads: 1158
.pdf Full text (510,93 KB)

303.
The concept of an ecosystem model to support the transformation to sustainable energy systems
Anja Kostevšek, Jiri Klemeš, Petar Varbanov, Gregor Papa, Janez Petek, 2016, original scientific article

Published in DiRROS: 15.03.2019; Views: 2230; Downloads: 1124
.pdf Full text (1,01 MB)

304.
Multi-hop communication in Bluetooth Low Energy ad-hoc wireless sensor network
Branko Skočir, Gregor Papa, Anton Biasizzo, 2018, original scientific article

Published in DiRROS: 15.03.2019; Views: 2582; Downloads: 633
.pdf Full text (992,95 KB)

305.
Sensors in proactive maintenance : a case of LTCC pressure sensors
Marina Santo-Zarnik, Franc Novak, Gregor Papa, 2018, original scientific article

Published in DiRROS: 15.03.2019; Views: 2283; Downloads: 793
.pdf Full text (630,49 KB)

306.
A comparison of models for forecasting the residential natural gas demand of an urban area
Rok Hribar, Primož Potočnik, Jurij Šilc, Gregor Papa, 2019, original scientific article

Abstract: Forecasting the residential natural gas demand for large groups of buildings is extremely important for efficient logistics in the energy sector. In this paper different forecast models for residential natural gas demand of an urban area were implemented and compared. The models forecast gas demand with hourly resolution up to 60 h into the future. The model forecasts are based on past temperatures, forecasted temperatures and time variables, which include markers for holidays and other occasional events. The models were trained and tested on gas-consumption data gathered in the city of Ljubljana, Slovenia. Machine-learning models were considered, such as linear regression, kernel machine and artificial neural network. Additionally, empirical models were developed based on data analysis. Two most accurate models were found to be recurrent neural network and linear regression model. In realistic setting such trained models can be used in conjunction with a weather-forecasting service to generate forecasts for future gas demand.
Keywords: demand forecasting, buildings, energy modeling, forecast accuracy, machine learning
Published in DiRROS: 15.03.2019; Views: 2301; Downloads: 1079
.pdf Full text (968,06 KB)

307.
A formal framework of human-machine interaction in proactive maintenance : MANTIS experience
Špela Poklukar, Gregor Papa, Franc Novak, 2017, original scientific article

Published in DiRROS: 15.03.2019; Views: 2272; Downloads: 1062
.pdf Full text (1,95 MB)

308.
309.
Comparison of multi-objective approaches to the real-world production scheduling
Gregor Papa, Peter Korošec, 2019, published scientific conference contribution

Published in DiRROS: 06.03.2019; Views: 2287; Downloads: 959
.pdf Full text (763,38 KB)

310.
From a production scheduling simulation to a digital twin
Gregor Papa, Peter Korošec, 2018, published scientific conference contribution

Published in DiRROS: 06.03.2019; Views: 2144; Downloads: 584
.pdf Full text (567,95 KB)

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