Persistent Naming Based on Graph Transformation Rules to Reevaluate Parametric Specification

Abstract

The process of motorcycle seat styling is a grey system with partially known and partially unknown information and is influenced by various factors. In this study, Grey Modelling (GM)(1,1) is used to predict the style of a motorcycle seat, and the shape features of the seat are extracted via morphological analysis and are parameterized. The process of shape evolution is established, and the modelling characteristics are predicted by GM(1,1). The kansei study is performed using five adjectives describing the seat styles to establish the equation of kansei regression analysis. The regression analysis is employed to modify predictive modelling. A certain brand of motorcycle seats is modelled to analyse and verify the feasibility and scientific applicability of adopting GM(1,1) in predicting motorcycle seat styling, which provided a feasible and effective reference for the motorcycle seat design.

Topological consistency preservation with graph transformation schemes

“…To design a modeler with Jerboa, one has to specify the dimensions of the objects (2D, 3D, 4D), the embeddings (position, curvature, density), and the rule-based transformations. Jerboa has been successively used for several modelers: one dedicated to architecture [90], one for the simulation of plant growth [91], and another one for the analysis of soils in geology [92]. In its current iteration, Jerboa supports the definition of modeler kernels using G-maps in any dimension.…”