Communication Analysis of Network-Centric Warfare via Transformation of System of Systems Model into Integrated System Model Using Neural Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Bong Gu | - |
dc.contributor.author | Seo, Kyung-Min | - |
dc.contributor.author | Kim, Tag Gon | - |
dc.date.accessioned | 2023-08-16T08:30:31Z | - |
dc.date.available | 2023-08-16T08:30:31Z | - |
dc.date.issued | 2018-06 | - |
dc.identifier.issn | 1076-2787 | - |
dc.identifier.issn | 1099-0526 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114276 | - |
dc.description.abstract | Communication system in the network-centric warfare (NCW) has been analyzed from the perspective of the system of systems (SoS), which consists of a combat system and a network system so that the two reflect each other's effects. However, this paradoxically causes a prolonged execution time. To solve this problem, this paper proposes an advanced integrated modeling method for the communication analysis in the NCW via the transformation of the SoS, which reduces the simulation execution time while ensuring the accuracy of the communication effects. The proposed models mainly cover interentity traffic and intraentity mobility developed in the form of feed-forward neural networks to guarantee two-way interactions between the combat system and the network system. Because they are characterized as discrete events, the proposed models are designed with the discrete-event system specification (DEVS) formalism. The experimental results show that the proposed transformation reduced an error by 6.40% compared to the existing method and reduced the execution time 3.78-fold compared to the SoS-based NCW simulation. © 2018 Bong Gu Kang et al. | - |
dc.format.extent | 17 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | John Wiley & Sons Inc. | - |
dc.title | Communication Analysis of Network-Centric Warfare via Transformation of System of Systems Model into Integrated System Model Using Neural Network | - |
dc.type | Article | - |
dc.publisher.location | 영국 | - |
dc.identifier.doi | 10.1155/2018/6201356 | - |
dc.identifier.scopusid | 2-s2.0-85049834364 | - |
dc.identifier.wosid | 000437973400001 | - |
dc.identifier.bibliographicCitation | Complexity, pp 1 - 17 | - |
dc.citation.title | Complexity | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 17 | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | SIMULATION | - |
dc.subject.keywordPlus | INTEROPERATION | - |
dc.identifier.url | https://www.hindawi.com/journals/complexity/2018/6201356/ | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.