A systematic review of digital transformation technologies in museum exhibition

Computers in Human Behavior, Journal Year: 2024, Volume and Issue: 161, P. 108407 - 108407

Published: Aug. 10, 2024

Museum exhibitions, both temporary and permanent, form an essential link between a society its cultural, historical, artistic heritage sites. Curating artifacts thematic displays in museum exhibitions can promote dialogue, foster cultural appreciation, contribute to preservation. The traditional way of holding heavily reliant on the expertise designers curatorial staff, makes them labor-intensive process, from conceptualization visitor engagement analysis. This review systematically compiles examines how application digital transformation technologies (DTTs) has revolutionized augmented their future potential. DTTs such as artificial intelligence, immersive technologies, additive manufacturing, Internet Things, cloud computing help create engaging designs, improve accessibility inclusivity, enhance educational potential, allow for sophisticated experience data collection analyses, improving exhibit management. However, despite multiple specialized studies roles connections technology scenarios remain underexplored. By addressing this gap, study is expected inform inspire practitioners sectors present new research avenues scholars.

Language: Английский

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Machine learning-aided prediction and customization on mechanical response and wave attenuation of multifunctional kiri/origami metamaterials

Extreme Mechanics Letters, Journal Year: 2024, Volume and Issue: unknown, P. 102276 - 102276

Published: Dec. 1, 2024

Language: Английский

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Spring-based mechanical metamaterials with deep-learning-accelerated design

Materials & Design, Journal Year: 2025, Volume and Issue: unknown, P. 113800 - 113800

Published: March 1, 2025

Language: Английский

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Optimizing Metamaterial Inverse Design with 3D Conditional Diffusion Model and Data Augmentation

Advanced Materials Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

Abstract The inverse design of metamaterials is critical for advancing their practical applications. Although deep learning has transformed this process, challenges remain, particularly with insufficient data and less realistic, diverse generation 3D represented as voxels. To address these limitations, a augmentation technique developed based on topological perturbation introduced conditional diffusion model (3D‐CDM) to optimize metamaterial generation. This original dataset, comprising 200 voxel representations lattices triply periodic minimal surfaces, labeled effective physical properties computed using homogenization methods. dataset expanded 5000 entries the proposed technique. Training 3D‐CDM augmented significantly improved quality accuracy generated designs. successfully produces realistic targeted properties, including volume fraction, Young's modulus, thermal conductivity, outperforming existing voxel‐based generative models in terms fidelity diversity. can be further optimized extended broader range material microstructures.

Language: Английский

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Composable generation strategy framework enabled bidirectional design on topological circuits

Physical review. B./Physical review. B, Journal Year: 2024, Volume and Issue: 110(13)

Published: Oct. 17, 2024

Language: Английский

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