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Detail publikace
VOŘECHOVSKÝ, M. CISZKIEWICZ, A.
Originální název
Advanced sampling discovers apparently similar ankle models with distinct internal load states under minimal parameter modification
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
Creating valid and trustworthy models is a key issue in biomedical engineering that affects the quality of life of both patients and healthy individuals in various scientific and industrial domains. This however is a difficult task due to the complex nature of biomechanical joints. In this study, a sampling strategy combining Genetic Algorithm and clustering is proposed to investigate biomechanical joints. A computational model of a human ankle joint with 43 input parameters serves as an illustrative case for the procedure. The Genetic Algorithm is used to efficiently search for distinct variants of the model with similar output, while clustering helps to quantify the obtained results. The search is performed in a close vicinity to the original model, mimicking subjective decisions in parameter acquisition. The method reveals twelve distinct clusters in the model parameter set, all resulting in the same angular displacements. These clusters correspond to three unique internal load states for the model, confirming the complex nature of the ankle. The proposed approach is general and could be applied to study other models in mechanical engineering and robotics.
Klíčová slova
Genetic algorithm; Clustering; Uncertainty quantification; Multibody system method
Autoři
VOŘECHOVSKÝ, M.; CISZKIEWICZ, A.
Vydáno
25. 4. 2024
Nakladatel
ELSEVIER
Místo
AMSTERDAM
ISSN
1877-7503
Periodikum
Journal of Computational Science
Ročník
82
Číslo
102425
Stát
Nizozemsko
Strany počet
10
URL
https://doi.org/10.1016/j.jocs.2024.102425
BibTex
@article{BUT193732, author="Miroslav {Vořechovský} and Adam {CISZKIEWICZ}", title="Advanced sampling discovers apparently similar ankle models with distinct internal load states under minimal parameter modification", journal="Journal of Computational Science", year="2024", volume="82", number="102425", pages="10", doi="10.1016/j.jocs.2024.102425", issn="1877-7503", url="https://doi.org/10.1016/j.jocs.2024.102425" }