A cellular neural network for deformable object modelling
| dc.contributor.author | Zhong, Yongmin | |
| dc.contributor.author | Shirinzadeh, B. | |
| dc.contributor.author | Yuan, X. | |
| dc.contributor.author | Alici, G. | |
| dc.contributor.author | Smith, J. | |
| dc.contributor.editor | W Shen | |
| dc.date.accessioned | 2017-01-30T13:30:22Z | |
| dc.date.available | 2017-01-30T13:30:22Z | |
| dc.date.created | 2012-12-03T07:24:55Z | |
| dc.date.issued | 2006 | |
| dc.identifier.citation | Zhong, 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.uri | http://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.publisher | Springer | |
| dc.title | A cellular neural network for deformable object modelling | |
| dc.type | Book Chapter | |
| dcterms.source.startPage | 329 | |
| dcterms.source.endPage | 336 | |
| dcterms.source.title | Information Technology For Balanced Manufacturing Systems | |
| dcterms.source.isbn | 9780387365909 | |
| dcterms.source.place | Boston | |
| dcterms.source.chapter | 52 | |
| curtin.department | ||
| curtin.accessStatus | Fulltext not available |