Skip to main content
SpringerLink
Log in

The microscopic visual forms in architectural art design following deep learning

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The purpose is to analyze the architectural art design of microscopic visual forms based on deep learning. First, GAN (Generative Adversarial Networks) of deep learning is applied to the field of architecture, and a multi-adversarial information sharing GAN is proposed through the improvement of GAN, and an architecture generation model of microscopic visual forms based on deep learning is constructed. The model is stimulated and its accuracy, distortion, and stability are analyzed. The results show that the accuracy of the model can reach more than 80% on different datasets. Compared with the other models in the related field, the model built in this study can show the features of the building images with the minimum distortion. Meanwhile, the curve hovers are around 0 in the process of model training, which is balanced. Therefore, the research can significantly improve the accuracy and the effect of feature extraction, and provide an experimental basis for the later architectural design of microscopic visual forms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Jia L, Ma Q, Du C et al (2020) Rapid urbanization in a mountainous landscape: patterns, drivers, and planning implications[J]. Landsc Ecol 35(11):2449–2469

    Article  Google Scholar 

  2. Aggarwal HK, Mani M, Jacob M et al (2019) MoDL: model-based deep learning architecture for inverse problems[J]. IEEE Trans Med Imaging 38(2):394–405

    Article  Google Scholar 

  3. Mutasa S, Chang P, Ruzalshapiro C et al (2018) MABAL: a novel deep-learning architecture for machine-assisted bone age labeling[J]. J Digit Imaging 31(4):513–519

    Article  Google Scholar 

  4. Maggipinto M, Terzi M, Masiero C et al (2018) A computer vision-inspired deep learning architecture for virtual metrology modeling with 2-dimensional data[J]. IEEE Trans Semicond Manuf 31(3):376–384

    Article  Google Scholar 

  5. Raj AP, Vajravelu SK (2019) DDLA: dual deep learning architecture for classification of plant species[J]. IET Image Proc 13(12):2176–2182

    Article  Google Scholar 

  6. Ahmad F, Abbasi A, Li J et al (2020) A deep learning architecture for psychometric natural language processing[J]. ACM Trans Inf Syst 38(1):1–29

    Article  Google Scholar 

  7. Antholzer S, Haltmeier M, Schwab J et al (2019) Deep learning for photoacoustic tomography from sparse data[J]. Inverse Probl Sci Eng 27(7):987–1005

    Article  MathSciNet  Google Scholar 

  8. Gupta S, Deep K (2020) A novel hybrid sine cosine algorithm for global optimization and its application to train multilayer perceptrons. Appl Intell 50(4):993–1026

    Article  Google Scholar 

  9. Hamanah WM, Abido MA, Alhems LM (2020) Optimum sizing of hybrid pv, wind, battery and diesel system using lightning search algorithm[J]. Arab J Sci Eng 45(3):1871–1883

    Article  Google Scholar 

  10. Jumani TA, Mustafa MW, Md Rasid M et al (2018) Optimal voltage and frequency control of an islanded microgrid using grasshopper optimization algorithm[J]. Energies 11(11):3191

    Article  Google Scholar 

  11. Kaboli SHA, Selvaraj J, Rahim NA (2017) Rain-fall optimization algorithm: a population based algorithm for solving constrained optimization problems[J]. J Comput Sci 19:31–42

    Article  Google Scholar 

  12. Alaa A, Alsewari AA, Alamri HS et al (2019) Comprehensive review of the development of the harmony search algorithm and its applications [J]. IEEE Access 7:14233–14245

    Article  Google Scholar 

  13. Abualigah L, Diabat A (2021) Advances in sine cosine algorithm: a comprehensive survey[J]. Artif Intell Rev 54:1–42

    Article  Google Scholar 

  14. Abualigah L (2020) Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications[J]. Neural Comput Appl 33:1–24

    Google Scholar 

  15. Abualigah L, Diabat A, Mirjalili S et al (2021) The arithmetic optimization algorithm[J]. Computer Methods Appl Mech Eng 376:113609

    Article  MathSciNet  Google Scholar 

  16. Lin J, Liu M, Hao J et al (2017) Many-objective harmony search for integrated order planning in steelmaking-continuous casting-hot rolling production of multi-plants[J]. Int J Prod Res 55(14):4003–4020

    Article  Google Scholar 

  17. Abualigah L, Diabat A, Geem ZW (2020) A comprehensive survey of the harmony search algorithm in clustering applications[J]. Appl Sci 10(11):3827

    Article  Google Scholar 

  18. Giuffrida MV, Doerner P, Tsaftaris SA et al (2018) Pheno-deep counter: a unified and versatile deep learning architecture for leaf counting[J]. Plant J 96(4):880–890

    Article  Google Scholar 

  19. Wang P, Di J (2018) Deep learning-based object classification through multimode fiber via a CNN-architecture SpeckleNet[J]. Appl Opt 57(28):8258–8263

    Article  Google Scholar 

  20. Trabelsi A, Chaabane M, Benhur A et al (2019) Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities[J]. Bioinformatics 35(14):i269–i277

    Article  Google Scholar 

  21. Chambon S, Thorey V, Arnal PJ et al (2019) DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal[J]. J Neurosci Methods 321:64–78

    Article  Google Scholar 

  22. Luo F, Wang M, Liu Y et al (2019) DeepPhos: prediction of protein phosphorylation sites with deep learning[J]. Bioinformatics 35(16):2766–2773

    Article  Google Scholar 

  23. Khokhlova OS, Nagler AO (2020) The Marfa Kurgan in the stavropol territory: an example of an ancient architectural structure[J]. Archaeol Ethnol Anthropol Eurasia 48(2):38–48

    Article  Google Scholar 

  24. Trivizakis E, Ioannidis GS, Melissianos VD et al (2019) A novel deep learning architecture outperforming ‘off-the-shelf’ transfer learning and feature-based methods in the automated assessment of mammographic breast density[J]. Oncol Rep 42(5):2009–2015

    Google Scholar 

  25. Jindal A, Aujla GS, Kumar N et al (2018) SeDaTiVe: SDN-enabled deep learning architecture for network traffic control in vehicular cyber-physical systems[J]. IEEE Network 32(6):66–73

    Article  Google Scholar 

  26. Fadlullah ZM, Mao B, Tang F et al (2019) Value iteration architecture based deep learning for intelligent routing exploiting heterogeneous computing platforms[J]. IEEE Trans Comput 68(6):939–950

    Article  MathSciNet  Google Scholar 

  27. Zhan Y, Zhang J, Li P et al (2019) Crowdtraining: architecture and incentive mechanism for deep learning training in the internet of things[J]. IEEE Network 33(5):89–95

    Article  Google Scholar 

  28. Reynolds MJ, Gong R, Reyes SEDL et al (2020) Deep learning reveals the link between filament architecture and subunit conformation in bent actin[J]. Biophys J 118(3):124a–125a

    Article  Google Scholar 

  29. Shin D, Lee J, Lee J et al (2018) DNPU: an energy-efficient deep-learning processor with heterogeneous multi-core architecture[J]. IEEE Micro 38(5):85–93

    Article  Google Scholar 

  30. Chen H, Chen A, Xu L et al (2020) A deep learning CNN architecture applied in the smart near-infrared analysis of water pollution for agricultural irrigation resources[J]. Agric Water Manag 240:106303

    Article  Google Scholar 

  31. Zhu J, Zeng H, Huang J et al (2020) Vehicle re-identification using quadruple directional deep learning Features[J]. IEEE Trans Intell Transp Syst 21(1):410–420

    Article  Google Scholar 

  32. Sekhon A, Singh R, Qi Y (2018) DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications[J]. Bioinformatics 34(17):i891–i900

    Article  Google Scholar 

  33. Fan X, Wang F, Wang F et al (2019) When RFID meets deep learning: exploring cognitive intelligence for activity identification[J]. IEEE Wirel Commun 26(3):19–25

    Article  MathSciNet  Google Scholar 

  34. Peterson KT, Sagan V, Sloan JJ (2020) Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing[J]. GIScience Remote Sens 57(4):510–525

    Article  Google Scholar 

  35. Wen X (2020) Using deep learning approach and IoT architecture to build the intelligent music recommendation system. Soft Comput. https://doi.org/10.1007/s00500-020-05364-y

    Article  Google Scholar 

  36. Shen C-W, Min C, Wang C-C (2019) Analyzing the trend of O2O commerce by bilingual text mining on social media. Comput Hum Behav 101:474–483. https://doi.org/10.1016/j.chb.2018.09.031

    Article  Google Scholar 

  37. Liu Y, Chen M (2018) From the aspect of STEM to discuss the effect of ecological art education on knowledge integration and problem-solving capability. Ekoloji 27(106):1705–1711

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Guangdong philosophy and Social Sciences Planning Project (Grant No: GD19YYS08)

Author information

Authors and Affiliations

  1. Institute of Micro Science And Art of SCUT, State Key Laboratory of Subtropical Building Science, South China University Of Technology, Guangzhou, People’s Republic of China

    Yi Guo

Authors
  1. Yi Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Guo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, Y. The microscopic visual forms in architectural art design following deep learning. J Supercomput 78, 559–577 (2022). https://doi.org/10.1007/s11227-021-03888-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03888-0

Keywords

Search

Navigation