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Institution

University of Ljubljana

EducationLjubljana, Slovenia
About: University of Ljubljana is a education organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Population & Liquid crystal. The organization has 17210 authors who have published 47013 publications receiving 1082684 citations. The organization is also known as: Univerza v Ljubljani.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the phenolic composition in walnut husks of the Slovenian cultivar Elit, which is a basic material for the traditional making of walnut liqueur, was investigated by HPLC with a PDA detector.

204 citations

Journal ArticleDOI
TL;DR: This study explores this novel architecture of CIoV, as well as research opportunities in vehicular network, and highlights crucial cognitive design issues from three perspectives, namely, intra-vehicle network, inter-Vehicle network and beyond-vehicles network.

203 citations

Journal ArticleDOI
04 Aug 2019-Sensors
TL;DR: This study analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms and used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections, showing that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult.
Abstract: Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset.

203 citations

Journal ArticleDOI
TL;DR: PHOEBE as discussed by the authors is an open source modeling code for computing theoretical light and radial velocity curves that addresses both problems by incorporating missing physics and by increasing the computational fidelity, including triangulation as a superior surface discretization algorithm, meshing of rotating single stars, light travel time effects, advanced phase computation, volume conservation in eccentric orbits, and improved computation of local intensity across the stellar surfaces.
Abstract: The precision of photometric and spectroscopic observations has been systematically improved in the last decade, mostly thanks to space-borne photometric missions and ground-based spectrographs dedicated to finding exoplanets. The field of eclipsing binary stars strongly benefited from this development. Eclipsing binaries serve as critical tools for determining fundamental stellar properties (masses, radii, temperatures, and luminosities), yet the models are not capable of reproducing observed data well, either because of the missing physics or because of insufficient precision. This led to a predicament where radiative and dynamical effects, insofar buried in noise, started showing up routinely in the data, but were not accounted for in the models. PHOEBE (PHysics Of Eclipsing BinariEs; http://phoebe-project.org) is an open source modeling code for computing theoretical light and radial velocity curves that addresses both problems by incorporating missing physics and by increasing the computational fidelity. In particular, we discuss triangulation as a superior surface discretization algorithm, meshing of rotating single stars, light travel time effects, advanced phase computation, volume conservation in eccentric orbits, and improved computation of local intensity across the stellar surfaces that includes the photon-weighted mode, the enhanced limb darkening treatment, the better reflection treatment, and Doppler boosting. Here we present the concepts on which PHOEBE is built and proofs of concept that demonstrate the increased model fidelity.

203 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a basic control system which enables the UPFC to follow the changes in reference values of the active and reactive power supplied from the outer system controller, based on the transformation of the three-phase power system to the rotating reference frame.
Abstract: A unified power flow controller (UPFC) is a typical FACTS device capable of instantaneous control of three power system parameters. This paper presents a basic control system which enables the UPFC to follow the changes in reference values of the active and reactive power supplied from the outer system controller. The analysis is based on the transformation of the three-phase power system to the rotating reference frame. As a step closer to a practical application of the UPFC, a modified control structure with a predictive control loop and precontrol signal for a DC-voltage control was designed. The new control system offers better stability and transient performance in comparison with the classical decoupled strategy, especially considering the harmonic distortion of the current being controlled. The derived basic control of the UPFC was tested with the NETOMAC program system.

203 citations


Authors

Showing all 17388 results

NameH-indexPapersCitations
David Miller2032573204840
Hyun-Chul Kim1764076183227
James M. Tour14385991364
Carmen García139150396925
Bernt Schiele13056870032
Vladimir Cindro129115782000
Teresa Barillari12998478782
Sven Menke129112182034
Horst Oberlack12998580069
Hubert Kroha129112680746
Peter Schacht129103080092
Siegfried Bethke1291266103520
Igor Mandić128106579498
Stefan Kluth128126184534
Andrej Gorišek12895167830
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202390
2022331
20213,150
20203,110
20192,780
20182,479