Thermal energy resource utilization and sample image restoration technology based on Machine vision simulation in interior design

• This paper proposes a sample image restoration technique based on machine vision simulation. • The repair method based on machine vision simulation combines deep learning algorithms and image processing techniques, which can automatically identify and repair defects in images. • The results show that the restoration method based on machine vision simulation proposed in this article can effectively improve the quality of images. With the rise of sustainable building design, the traditional design methods are often unable to fully consider the optimal allocation and application of thermal energy resources. This study aims to explore the application of heat resource utilization and sample image restoration technology based on machine vision simulation technology in interior design, and provide practical solutions for optimizing indoor environment. In this paper, machine vision technology is used to monitor and model indoor space in real time, and heat energy flow and distribution under different design schemes are simulated. At the same time, the influence of different designs on thermal energy utilization efficiency is analyzed by using sample image restoration technology. In the study, a set of comprehensive evaluation indexes was established, which comprehensively considered the factors of heat efficiency, indoor comfort and energy consumption. The experimental results show that the simulation technology based on machine vision can accurately predict the indoor heat distribution and identify the best design scheme. At the same time, the sample image restoration technology significantly improves the evaluation accuracy of heat utilization efficiency. Through these methods, the optimized design scheme has higher thermal energy utilization than the traditional design, and significantly improves the indoor comfort. This study shows that the combination of machine vision simulation and sample image restoration technology can effectively improve the utilization efficiency of thermal energy resources in interior design, and provide new ideas and methods for sustainable building design.

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Publication Details

Journal
Thermal Science and Engineering Progress
Published
2024-09-16
DOI
https://doi.org/10.1016/j.tsep.2024.102897
Citations
5
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
1.74
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article

Thermal energy resource utilization and sample image restoration technology based on Machine vision simulation in interior design

Jiyu Wang
5 citations
Thermal Science and Engineering Progress
Industrial Vision Systems and Defect Detection
1.74
article

Thermal energy resource utilization and sample image restoration technology based on Machine vision simulation in interior design

Jiyu Wang
article en
5 citations

Abstract

• This paper proposes a sample image restoration technique based on machine vision simulation. • The repair method based on machine vision simulation combines deep learning algorithms and image processing techniques, which can automatically identify and repair defects in images. • The results show that the restoration method based on machine vision simulation proposed in this article can effectively improve the quality of images. With the rise of sustainable building design, the traditional design methods are often unable to fully consider the optimal allocation and application of thermal energy resources. This study aims to explore the application of heat resource utilization and sample image restoration technology based on machine vision simulation technology in interior design, and provide practical solutions for optimizing indoor environment. In this paper, machine vision technology is used to monitor and model indoor space in real time, and heat energy flow and distribution under different design schemes are simulated. At the same time, the influence of different designs on thermal energy utilization efficiency is analyzed by using sample image restoration technology. In the study, a set of comprehensive evaluation indexes was established, which comprehensively considered the factors of heat efficiency, indoor comfort and energy consumption. The experimental results show that the simulation technology based on machine vision can accurately predict the indoor heat distribution and identify the best design scheme. At the same time, the sample image restoration technology significantly improves the evaluation accuracy of heat utilization efficiency. Through these methods, the optimized design scheme has higher thermal energy utilization than the traditional design, and significantly improves the indoor comfort. This study shows that the combination of machine vision simulation and sample image restoration technology can effectively improve the utilization efficiency of thermal energy resources in interior design, and provide new ideas and methods for sustainable building design.

Thermal Science and Engineering ProgressVol. 55
Henan University of Engineering (CN), Sias University (CN)
Responsible consumption and production
Openalex Percentile: Top 14%
Industrial Vision Systems and Defect Detection
1.74
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