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오현동

Oh, Hyondong
Autonomous Systems Lab.
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dc.citation.endPage 148 -
dc.citation.startPage 130 -
dc.citation.title INFORMATION FUSION -
dc.citation.volume 36 -
dc.contributor.author Hutchinson, Michael -
dc.contributor.author Oh, Hyondong -
dc.contributor.author Chen, Wen-Hua -
dc.date.accessioned 2023-12-21T22:09:09Z -
dc.date.available 2023-12-21T22:09:09Z -
dc.date.created 2016-12-12 -
dc.date.issued 2017-07 -
dc.description.abstract Understanding atmospheric transport and dispersal events has an important role in a range of scenarios. Of particular importance is aiding in emergency response after an intentional or accidental chemical, biological or radiological (CBR) release. In the event of a CBR release, it is desirable to know the current and future spatial extent of the contaminant as well as its location in order to aid decision makers in emergency response. Many dispersion phenomena may be opaque or clear, thus monitoring them using visual methods will be difficult or impossible. In these scenarios, relevant concentration sensors are required to detect the substance where they can form a static network on the ground or be placed upon mobile platforms. This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors. The review is concluded with a discussion on the current limitations of the state of the art and recommendations for future research. -
dc.identifier.bibliographicCitation INFORMATION FUSION, v.36, pp.130 - 148 -
dc.identifier.doi 10.1016/j.inffus.2016.11.010 -
dc.identifier.issn 1566-2535 -
dc.identifier.scopusid 2-s2.0-84996841786 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/20990 -
dc.identifier.url http://www.sciencedirect.com/science/article/pii/S156625351630152X -
dc.identifier.wosid 000394070100010 -
dc.language 영어 -
dc.publisher ELSEVIER SCIENCE BV -
dc.title A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.relation.journalWebOfScienceCategory Computer Science, Artificial Intelligence; Computer Science, Theory & Methods -
dc.relation.journalResearchArea Computer Science -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Source estimation -
dc.subject.keywordAuthor Inverse modelling -
dc.subject.keywordAuthor Boundary tracking -
dc.subject.keywordAuthor Atmospheric dispersion -
dc.subject.keywordAuthor Optimisation -
dc.subject.keywordAuthor Bayesian inference -
dc.subject.keywordAuthor Source localisation -
dc.subject.keywordAuthor Dispersion modelling -
dc.subject.keywordPlus ENVIRONMENTAL BOUNDARY TRACKING -
dc.subject.keywordPlus MULTIPLE-POINT RELEASES -
dc.subject.keywordPlus URBAN-LIKE ENVIRONMENT -
dc.subject.keywordPlus GENETIC ALGORITHM -
dc.subject.keywordPlus SOURCE LOCALIZATION -
dc.subject.keywordPlus INVERSION TECHNIQUE -
dc.subject.keywordPlus PARAMETER-ESTIMATION -
dc.subject.keywordPlus BAYESIAN-INFERENCE -
dc.subject.keywordPlus CONTAMINANT CLOUD -
dc.subject.keywordPlus NETWORK CONTROL -

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