FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2927719488> ?p ?o ?g. }

• W2927719488 endingPage "2090" @default.
• W2927719488 startingPage "2083" @default.
• W2927719488 abstract "Selecting the best configuration for a cyber-network system is considered as one of the major challenges in terms of the reliability evolution of smart systems because of a specific relationship between power network system and cyber network. Although producing some possible cyber networks for one simple power system and selecting the best one does not need any formulas and can be done through trial and error as performed in previous studies, producing all possible n th elements of the cyber network that observe the cyber protocols and choosing the best one has not been done before. Choosing the best configuration for any n th elements cyber network that can be connected to a bulk power network needs a robust and adequate computer algorithm considering cyber protocols and decision-making goals. In this paper, a novel method is proposed in order to introduce the best configuration for a cyber-network system to accommodate cyber protocols and have a remarkable effect on decreasing expected energy not supplied (EENS) compared with those previously studied. To this end, two mathematical concepts are proposed; a graph theory to consider cyber protocols and teaching–learning-based optimization to select the best cyber configuration with minimum EENS. During the first time, choosing the best configuration with each specific goal for n th devices of a cyber-network system is applicable by converting it into an $n times n$ adjacency matrix and using the proposed graph theory mixed with teaching–learning-based optimization algorithm. Moreover, Monte Carlo simulation was used in this paper as one of the most precise probabilistic methods. The test results indicate that the proposed method selected for identifying the best configuration of a cyber-network system has significantly improved reliability indices compared to those in previous papers and it could be useful for every wide power–cyber network. Additionally, different types of cyber network are studied for validating the proposed method. This method is applied to the realistic feeder of the Hormozgan Regional Electric Company as a smart pilot system." @default.
• W2927719488 created "2019-04-11" @default.
• W2927719488 creator A5056451976 @default.
• W2927719488 creator A5073750393 @default.
• W2927719488 creator A5079569246 @default.
• W2927719488 date "2019-04-01" @default.
• W2927719488 modified "2023-09-25" @default.
• W2927719488 title "Optimizing Configuration of Cyber Network Considering Graph Theory Structure and Teaching–Learning-Based Optimization (GT-TLBO)" @default.
• W2927719488 cites W1513720878 @default.
• W2927719488 cites W1981619817 @default.
• W2927719488 cites W1994807158 @default.
• W2927719488 cites W2001422417 @default.
• W2927719488 cites W2020798599 @default.
• W2927719488 cites W2025412567 @default.
• W2927719488 cites W2052350400 @default.
• W2927719488 cites W2075410688 @default.
• W2927719488 cites W2098397627 @default.
• W2927719488 cites W2112971141 @default.
• W2927719488 cites W2118574227 @default.
• W2927719488 cites W2129394334 @default.
• W2927719488 cites W2154860395 @default.
• W2927719488 cites W2169528473 @default.
• W2927719488 cites W2194450966 @default.
• W2927719488 cites W2315183894 @default.
• W2927719488 cites W2346700178 @default.
• W2927719488 cites W2371678681 @default.
• W2927719488 cites W2544480101 @default.
• W2927719488 cites W4246247914 @default.
• W2927719488 cites W905005337 @default.
• W2927719488 doi "https://doi.org/10.1109/tii.2018.2860984" @default.
• W2927719488 hasPublicationYear "2019" @default.
• W2927719488 type Work @default.
• W2927719488 sameAs 2927719488 @default.
• W2927719488 citedByCount "18" @default.
• W2927719488 countsByYear W29277194882019 @default.
• W2927719488 countsByYear W29277194882020 @default.
• W2927719488 countsByYear W29277194882021 @default.
• W2927719488 countsByYear W29277194882022 @default.
• W2927719488 countsByYear W29277194882023 @default.
• W2927719488 crossrefType "journal-article" @default.
• W2927719488 hasAuthorship W2927719488A5056451976 @default.
• W2927719488 hasAuthorship W2927719488A5073750393 @default.
• W2927719488 hasAuthorship W2927719488A5079569246 @default.
• W2927719488 hasConcept C114614502 @default.
• W2927719488 hasConcept C154945302 @default.
• W2927719488 hasConcept C33923547 @default.
• W2927719488 hasConcept C41008148 @default.
• W2927719488 hasConcept C80444323 @default.
• W2927719488 hasConcept C88230418 @default.
• W2927719488 hasConceptScore W2927719488C114614502 @default.
• W2927719488 hasConceptScore W2927719488C154945302 @default.
• W2927719488 hasConceptScore W2927719488C33923547 @default.
• W2927719488 hasConceptScore W2927719488C41008148 @default.
• W2927719488 hasConceptScore W2927719488C80444323 @default.
• W2927719488 hasConceptScore W2927719488C88230418 @default.
• W2927719488 hasIssue "4" @default.
• W2927719488 hasLocation W29277194881 @default.
• W2927719488 hasOpenAccess W2927719488 @default.
• W2927719488 hasPrimaryLocation W29277194881 @default.
• W2927719488 hasRelatedWork W1969952464 @default.
• W2927719488 hasRelatedWork W2392953011 @default.
• W2927719488 hasRelatedWork W2479275014 @default.
• W2927719488 hasRelatedWork W2806270048 @default.
• W2927719488 hasRelatedWork W3012195680 @default.
• W2927719488 hasRelatedWork W3106287265 @default.
• W2927719488 hasRelatedWork W3107474891 @default.
• W2927719488 hasRelatedWork W4311655325 @default.
• W2927719488 hasRelatedWork W7173002 @default.
• W2927719488 hasRelatedWork W3088849728 @default.
• W2927719488 hasVolume "15" @default.
• W2927719488 isParatext "false" @default.
• W2927719488 isRetracted "false" @default.
• W2927719488 magId "2927719488" @default.
• W2927719488 workType "article" @default.