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Träfflista för sökning "WFRF:(Belov Vladimir) "

Sökning: WFRF:(Belov Vladimir)

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  • Belov, Vladimir, et al. (författare)
  • Mathematical Model of Multi-Phase Power Converter for Parallel Computation
  • 2018
  • Ingår i: International Journal of Emerging Electric Power Systems. - : Walter de Gruyter GmbH. - 2194-5756 .- 1553-779X.
  • Tidskriftsartikel (refereegranskat)abstract
    • A mathematical model of a multi-phase power conversion system composed of modified bridge-elements (B-system) capable for parallel computation has been developed. Experimental validation on the example of a power system including a synchronous generator and an AC-DC rectifier has been performed. A mathematical algorithm for B-system assembly and steps to obtain mathematical model of the B-system have been developed. Integration of mathematical models of conversion system into the complete model of a multi-phase power system has been explained and evaluation of computational efficiency of parallel computation techniques for the developed model of an AC-DC-AC converter has been performed. The presented modelling method can be employed in the design phase of smart grids, for power quality and conducted emission analysis.
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  • Belov, Vladimir, et al. (författare)
  • Power quality and EMC solutions in micro grids with energy-trading capability
  • 2014
  • Ingår i: Proceedings of the international symposium on electromagnetic compatibility, EMC Europe, 1-4/9, 2014, Gothenburg, Sweden. - Piscataway, N.J. : IEEE Press. - 9781479932252 ; , s. 1203-1208, s. 1203-1208
  • Konferensbidrag (refereegranskat)abstract
    • A mathematical model of an AC/DC/AC power converter with an energy storage device has been developed on the basis of a bridge-element concept, that can be employed in the design phase for power quality and conducted emission analysis ofmicro grids. A prototype of a 5 kW AC/DC/AC power converter is built and a mockup of electric energy trading system is realized. Measurements conducted for three operating modes emulating electric energy transfer and power consumption intrading conditions have revealed a low voltage total harmonic distortion, not exceeding 1.3% for the tested cases.
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  • Belov, Vladimir, et al. (författare)
  • Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
  • 2024
  • Ingår i: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 14:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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