Efficient modelling and simulation of soft tissue deformation using mass-spring systems

This source preferred by Jian Jun Zhang

Authors: Duysak, A., Zhang, J.J. and Ilankovan, V.

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B7581-48PK9S7-24&_user=1682380&_coverDate=06%2F30%2F2003&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000011378&_version=1&_urlVersion=0&_userid=1682380&md5=4201fa30f64ea33f524026d906ee9a0d

Start date: 25 June 2003

Pages: 337-342

Publisher: Elsevier

Place of Publication: London

DOI: 10.1016/S0531-5131(03)00423-0

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Authors: Duysak, A., Zhang, J.J. and Ilankovan, V.

Journal: International Congress Series

Volume: 1256

Issue: C

Pages: 337-342

ISSN: 0531-5131

DOI: 10.1016/S0531-5131(03)00423-0

In this paper, we use a mass-spring system to simulate facial soft tissue deformation resulting from the bone realignment at the lower jaw area. Since the materials concerned often exhibit significant nonlinearity, correct simulation parameters are needed to capture the nonlinear characteristics in order to achieve satisfying simulation accuracy. We propose a neural network identification method that takes mass-spring structure into account and uses only two neural networks to identify these parameters, which are usually nonlinear functions. An adaptive learning rate formula is also introduced to improve the simulation accuracy and convergence speed. © 2003, Elsevier Science B.V. and CARS. All rights reserved.

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