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Design of commercial environment space based on digital media technology

Sai Yin

Sai Yin

Department of Environmental Design, Academy of Fine Arts, Henan UniversityKaifeng, China
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Yin, Sai
  
Feb 27, 2025
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Published Online: Feb 27, 2025
Received: Oct 25, 2024
Accepted: Jan 29, 2025
DOI: https://doi.org/10.2478/amns-2025-0111
Keywords
© 2025 Sai Yin, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

The evolutionary plot of the 4 test functions
The evolutionary plot of the 4 test functions

Figure 2.

The evolutionary plot of the 2 test functions
The evolutionary plot of the 2 test functions

Figure 3.

Multiobjective differential evolution improvement algorithm
Multiobjective differential evolution improvement algorithm

Figure 4.

Frontier diagram of each test function
Frontier diagram of each test function

Figure 5.

Frontier diagram of each test function
Frontier diagram of each test function

Figure 6.

Frontier diagram of each test function
Frontier diagram of each test function

Figure 7.

Evolutionary process of AMODE-MPS and control algorithm (1)
Evolutionary process of AMODE-MPS and control algorithm (1)

Figure 8.

Evolutionary Process of AMODE-MPS and Control Algorithm (2)
Evolutionary Process of AMODE-MPS and Control Algorithm (2)

Figure 9.

Experimental results
Experimental results

Figure 10.

The number of iterations required for the three algorithms to reach convergence
The number of iterations required for the three algorithms to reach convergence

Figure 11

Layout accuracy curve and loss value curve of a training set of the network model
Layout accuracy curve and loss value curve of a training set of the network model

Figure 12.

The floor effect drawing after the recommended spatial layout
The floor effect drawing after the recommended spatial layout

statistical comparisons of the Wilcoxon test

IDE-BOVS IDE IGA
P Values 0.008 0.000

Test set accuracy

Test set accuracy% First, for the first time Second time Third time The fourth time Average accuracy
After adding the embedded layer (Single house section) 98.54 99.01 97.63 99.22 98.60
After adding the embedded layer (Whole house section) 91.40 90.79 93.31 93.12 92.16
Join the embedding layer before (Single house section) 90.24 88.54 91.06 89.14 89.75
Join the embedding layer before (Whole house section) 72.93 75.67 70.66 74.67 73.48

spatial strategy

Tactics Merit Shortcoming
Homogenization Simple in structure and easy to implement Significant loss of information and less detail
Like meta polymerization Retain more information and reduce the noise May cause boundary blur and is not suitable for complex terrain
Space averaging method Reduce the local outliers, more smooth processing The information is vague and can not retain the spatial characteristics
Target selection Important information can be selected flexibly, according to the analysis requirements Selection criteria need to be specified, and important data may be missed
Multiscale analysis Be able to understand spatial phenomena from multiple perspectives Computing complexity is high, and the integration is difficult
Weighted assessment Be able to consider the importance of each data source comprehensively Weight setting requires professional knowledge and has a high degree of uncertainty
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Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
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