IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i15p8439-d603510.html
   My bibliography  Save this article

Intelligent Recognition Method of Decorative Openwork Windows with Sustainable Application for Suzhou Traditional Private Gardens in China

Author

Listed:
  • Rui Zhang

    (School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China)

  • Yuwei Zhao

    (School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China)

  • Jianlei Kong

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
    National Engineering Laboratory for Agri-Product Quality Traceability, Beijing 100048, China)

  • Chen Cheng

    (School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China)

  • Xinyan Liu

    (School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China)

  • Chang Zhang

    (School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China)

Abstract

Decorative openwork windows (DO-Ws) in Suzhou traditional private gardens play a vital role in Chinese traditional garden art. Due to the delicate and elegant patterns, as well as their rich cultural meaning, DO-Ws have quite high protection and utilization value. In this study, we firstly visited 15 extant traditional gardens in Suzhou and took almost 3000 photos to establish the DO-W datasets. Then, we present an effective visual recognition method named CSV-Net to classify different DO-Ws’ patterns in Suzhou traditional gardens. On the basis of the backbone module of the cross stage partial network optimized with the Soft-VLAD architecture, the proposed CSV-Net achieves a preferable representation ability for distinguishing different DO-Ws in practical scenes. The comparative experimental results show that the CSV-Net model achieves a good balance between its performance, robustness and complexity for identifying DO-Ws, also having further potential for sustainable application in traditional gardens. Moreover, the Canglang Pavilion and the Humble Administrator’s Garden were selected as the cases to analyze the relation between identifying DO-W types and their locations in intelligent approaches, which further reveals the design rules of the sustainable culture contained in Chinese traditional gardens. This work ultimately promotes the sustainable application of artificial intelligence technology in the field of garden design and inheritance of the garden art.

Suggested Citation

  • Rui Zhang & Yuwei Zhao & Jianlei Kong & Chen Cheng & Xinyan Liu & Chang Zhang, 2021. "Intelligent Recognition Method of Decorative Openwork Windows with Sustainable Application for Suzhou Traditional Private Gardens in China," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8439-:d:603510
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/15/8439/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/15/8439/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Seng Lin, 2021. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization," Energies, MDPI, vol. 14(6), pages 1-18, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yunda Wang & Qiguan Shu & Ming Chen & Xudounan Chen & Shiro Takeda & Junhua Zhang, 2022. "Selection and Application of Quantitative Indicators of Paths Based on Graph Theory: A Case Study of Traditional Private and Antique Gardens in Beijing," Land, MDPI, vol. 11(12), pages 1-21, December.
    2. Linlin Shan & Long Zhang, 2022. "Application of Intelligent Technology in Facade Style Recognition of Harbin Modern Architecture," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    3. Huishu Chen & Li Yang, 2023. "Analysis of Narrative Space in the Chinese Classical Garden Based on Narratology and Space Syntax—Taking the Humble Administrator’s Garden as an Example," Sustainability, MDPI, vol. 15(16), pages 1-22, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Venkataramana Veeramsetty & Arjun Mohnot & Gaurav Singal & Surender Reddy Salkuti, 2021. "Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models," Energies, MDPI, vol. 14(11), pages 1-21, May.
    2. Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    3. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    4. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    5. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2021. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality," Energies, MDPI, vol. 14(19), pages 1-19, September.
    6. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
    7. Krishna Prakash N. & Jai Govind Singh, 2023. "Electricity price forecasting using hybrid deep learned networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1750-1771, November.
    8. Thibaut Th'eate & Antonio Sutera & Damien Ernst, 2023. "Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis," Papers 2301.11587, arXiv.org.
    9. Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Min Zuo & Qing-Chuan Zhang & Seng Lin, 2021. "Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse," Agriculture, MDPI, vol. 11(8), pages 1-25, August.
    10. Xudong Pang & Xiangchen Lu & Hao Ding & Josep M. Guerrero, 2022. "Construction of Smart Grid Load Forecast Model by Edge Computing," Energies, MDPI, vol. 15(9), pages 1-16, April.
    11. Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
    12. Krzysztof Lalik & Filip Wątorek, 2021. "Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles," Energies, MDPI, vol. 14(22), pages 1-18, November.
    13. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    14. Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-Martínez, 2024. "Electrical Load Forecasting to Plan the Increase in Renewable Energy Sources and Electricity Demand: a CNN-QR-RTCF and Deep Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 186-194, July.
    15. Zhou, Yihong & Zhang, Xiao & Ding, Feng, 2022. "Partially-coupled nonlinear parameter optimization algorithm for a class of multivariate hybrid models," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    16. Xue-Bo Jin & Zhong-Yao Wang & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su & Hui-Jun Ma & Prasun Chakrabarti, 2023. "Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    17. Wen-Jie Liu & Yu-Ting Bai & Xue-Bo Jin & Ting-Li Su & Jian-Lei Kong, 2022. "Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-21, September.
    18. Liang Ran & Yaling Mao & Tiejiang Yuan & Guofeng Li, 2022. "Low-Carbon Transition Pathway Planning of Regional Power Systems with Electricity-Hydrogen Synergy," Energies, MDPI, vol. 15(22), pages 1-17, November.
    19. Xin Xu & Cheng-Cai Yang & Yang Xiao & Jian-Lei Kong, 2023. "A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation," IJERPH, MDPI, vol. 20(6), pages 1-20, March.
    20. Jianlei Kong & Hongxing Wang & Chengcai Yang & Xuebo Jin & Min Zuo & Xin Zhang, 2022. "A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition," Agriculture, MDPI, vol. 12(4), pages 1-30, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8439-:d:603510. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.