Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/17653
Full metadata record
DC FieldValueLanguage
dc.contributor.authorD. Macuzic Saveljic, Slavica-
dc.contributor.authorArsić, Branko-
dc.contributor.authorSaveljic, Igor-
dc.contributor.authorLukić, Jovanka-
dc.date.accessioned2023-04-21T12:28:24Z-
dc.date.available2023-04-21T12:28:24Z-
dc.date.issued2022-
dc.identifier.issn1757-899Xen_US
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/17653-
dc.description.abstractDriving comfort is one of the important factors for vehicle users. There is a lot of research related to comfort or discomfort in a vehicle but there is still no well-defined way to assess it accurately. In this paper, an assessment of vehicle comfort during fore-and-aft random vibrations was made based on measured and predicted r.m.s. acceleration values. Measured values of r.m.s. accelerations were obtained by laboratory testing while the predicted values of r.m.s. accelerations obtained based on the ANN (Artifical Neural Network) model. 20 male subjects participated in the study. Their different anthropometric characteristics of the body were taken into account. Based on the measured r.m.s. values of acceleration formed ANN model which has ability to predict r.m.s. acceleration values based on measured values. The obtained results showed high accuracy of the model.en_US
dc.language.isoenen_US
dc.publisherIOP Conf. Series: Materials Science and Engineeringen_US
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.sourceIOP Conference Series: Materials Science and Engineering-
dc.titleIn-vehicle comfort assessment during fore-and-aft random vibrations based on artificial neural networks (ANN)en_US
dc.typearticleen_US
dc.description.versionPublisheden_US
dc.identifier.doi10.1088/1757-899X/1271/1/012021en_US
dc.type.versionPublishedVersionen_US
Appears in Collections:Faculty of Engineering, Kragujevac

Page views(s)

370

Downloads(s)

14

Files in This Item:
File Description SizeFormat 
Mačužić_Saveljić_2022_IOP_Conf._Ser. _Mater._Sci._Eng._1271_012021.pdf933.78 kBAdobe PDFThumbnail
View/Open


Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.