Enhanced grey relational analysis method for neutrosophic cubic number madm and applications to decorative wall hanging design effect evaluation

Author:

Liu Pengyu1

Affiliation:

1. Changchun University, Changchun, Jinlin, China

Abstract

The so-called wall hanging is actually a decorative picture hung indoors. It can also be said that painting, printing, dyeing, weaving, and embroidery on the fabric. The difference between traditional Chinese painting, watercolor painting, and oil painting lies in the fact that it is made in real objects with a material sense of texture, quality, and luster; Secondly, it has a purely decorative purpose; Therefore, it must be coordinated with the entire interior design. Judging from the current situation of production of wall hangings in China, most of them are high-end products such as wool weaving, flannelette embroidery, and embroidery, which are exclusively for export. The theme content is mostly taken from scenic spots and historical sites. The silk paintings unearthed in Mawangdui, Changsha, can also be regarded as the earliest wall hangings in China. Decorative wall hanging is an increasingly important item in modern interior decoration, and good decorative wall hanging is often regarded as the “finishing touch” in interior decoration design; It can refresh and brighten the indoor environment, and is an indispensable and important part of modern indoor overall design. The decorative wall hanging design effect evaluation is often considered as a multi-attribute decision making (MADM) problem. In this paper, the grey relational analysis (GRA) decision method is extended to the neutrosophic cubic sets (NCSs) setting to deal with MAGDM. First, the NCSs are reviewed. Then, the CRITIC is used to obtain the weights of attributes. In addition, the neutrosophic cubic number GRA (NCN-GRA) is established for MADM, and the computational steps for all designs are listed. Finally, the decorative wall hanging design effect evaluation is given to demonstrate the NCN-GRA model and some good comparative analysis is done to demonstrate the advantages of NCN-GRA.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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