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Graphical methods as a complements of analytical methods used in the research of dynamic models for networks reliability
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| dc.contributor.author | LEAHU, Alexei | |
| dc.contributor.author | BAGRIN-ANDRIEVSCHI, Veronica | |
| dc.contributor.author | ROTARU, Maria | |
| dc.date.accessioned | 2022-12-28T10:10:35Z | |
| dc.date.available | 2022-12-28T10:10:35Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | LEAHU, Alexei, BAGRIN-ANDRIEVSCHI, Veronica, ROTARU, Maria. Graphical methods as a complements of analytical methods used in the research of dynamic models for networks reliability. In: Electronics, Communications and Computing (IC ECCO-2022): 12th intern. conf., 20-21 Oct. 2022, Chişinău, Republica Moldova: conf. proc., Chişinău, 2022, pp. 168-171. | en_US |
| dc.identifier.uri | https://doi.org/10.52326/ic-ecco.2022/CS.04 | |
| dc.identifier.uri | http://repository.utm.md/handle/5014/21850 | |
| dc.description.abstract | Our work deals with a typical problem of comparing the reliability of a serial-parallel type network vs the reliability of a parallel-serial type network. Using graphic methods on elementary models, we show how they lead to the formulation of mathematically argued conclusions. These conclusions are then extended to whole families of probabilistic dynamic models related to the initial models. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Technical University of Moldova | en_US |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
| dc.subject | serial-parallel networks | en_US |
| dc.subject | parallel-serial networks | en_US |
| dc.subject | probabilistic dynamic models | en_US |
| dc.title | Graphical methods as a complements of analytical methods used in the research of dynamic models for networks reliability | en_US |
| dc.type | Article | en_US |
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2022
Proceedings of the 12th IC|ECCO; October 20-21, 2022
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States