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dc.contributor.authorZhong, Yongmin
dc.contributor.authorShirinzadeh, B.
dc.contributor.authorYuan, X.
dc.contributor.authorAlici, G.
dc.contributor.authorSmith, J.
dc.contributor.editorW Shen
dc.date.accessioned2017-01-30T13:30:22Z
dc.date.available2017-01-30T13:30:22Z
dc.date.created2012-12-03T07:24:55Z
dc.date.issued2006
dc.identifier.citationZhong, Y. and Shirinzadeh, B. and Yuan, X. and Alici, G. and Smith, J. 2006. A cellular neural network for deformable object modelling, in Shen, W. (ed), Information technology for balanced manufacturing systems. pp. 329-336. Boston: Springer.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/32335
dc.description.abstract

This paper presents a new methodology for the deformation of soft objects bydrawing an analogy between cellular neural network (CNN) and elasticdeformation. An improved CNN model is developed to simulate thedeformation of soft objects. A fnite volume based method is presented to derivethe discrete dijferential operators over irregular nets for obtaining the internalelastic forces. The proposed methodology not only models the deformationdynamics in continuum mechanics, but it also simplifies the complexdeformation problem with simple setting CNN templates.

dc.publisherSpringer
dc.titleA cellular neural network for deformable object modelling
dc.typeBook Chapter
dcterms.source.startPage329
dcterms.source.endPage336
dcterms.source.titleInformation Technology For Balanced Manufacturing Systems
dcterms.source.isbn9780387365909
dcterms.source.placeBoston
dcterms.source.chapter52
curtin.department
curtin.accessStatusFulltext not available


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