Extracting Architectural Design Elements of CAD Data Using Deep Learning Algorithms

被引：0

作者：

Wang Z. ^{[1
]}

Wang D. ^{[2
]}

机构：

[1] School of Ceramics, Pingdingshan University, Henan, Pingdingshan

[2] School of Art and Design College, Henan University of Urban Construction, Henan, Pingdingshan

来源：

Computer-Aided Design and Applications | 2024年 / 21卷 / S19期

关键词：

Architectural Design; CAD; Data Mining; Deep Learning; FCM Algorithm;

D O I：

10.14733/cadaps.2024.S19.179-193

中图分类号：

学科分类号：

摘要：

In computer-aided design (CAD) data, architectural design elements usually exist in various forms, such as graphics, lines, and texts. Extracting these design elements efficiently and accurately from massive CAD data and applying them to architectural design practice is a difficult problem in architectural design. In this article, deep learning (DL) and fuzzy C clustering (FCM) algorithms in data mining (DM) are combined to extract architectural design elements from CAD data, predict the required architectural features, and provide support for the intelligent development of architectural design. By comparing the recall and accuracy, it is found that the algorithm can effectively identify the actual architectural features and has a high proportion of real cases in the samples predicted as positive cases. This shows that the algorithm can not only capture the architectural features but also effectively eliminate the interference factors and reduce the occurrence of false positives. This method improves the performance of extracting design elements and provides reliable technical support for intelligent architectural design and promotes cross-domain innovation and development. © 2024, CAD Solutions, LLC. All rights reserved.

引用

页码：179 / 193

页数：14

共 50 条

[31] Extracting Retinal Vascular Networks Using Deep Learning Architecture [J].

Kassim, Yasmin M. ;

Palaniappan, K. .

2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2017, :1170-1174

[32] Overcoming challenges in extracting prescribing habits from veterinary clinics using big data and deep learning [J].

Hur, B. ;

Hardefeldt, L. Y. ;

Verspoor, K. ;

Baldwin, T. ;

Gilkerson, J. R. .

AUSTRALIAN VETERINARY JOURNAL, 2022, 100 (05) :220-222

[33] Shared Data and Algorithms for Deep Learning in Fundamental Physics [J].

Benato L. ;

Buhmann E. ;

Erdmann M. ;

Fackeldey P. ;

Glombitza J. ;

Hartmann N. ;

Kasieczka G. ;

Korcari W. ;

Kuhr T. ;

Steinheimer J. ;

Stöcker H. ;

Plehn T. ;

Zhou K. .

Computing and Software for Big Science, 2022, 6 (1)

[34] APPLICATION OF DEEP-LEARNING ALGORITHMS TO MSTAR DATA [J].

Wang, Haipeng ;

Chen, Sizhe ;

Xu, Feng ;

Jin, Ya-Qiu .

2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2015, :3743-3745

[35] Diabetes detection using deep learning algorithms [J].

Swapna, G. ;

Vinayakumar, R. ;

Soman, K. P. .

ICT EXPRESS, 2018, 4 (04) :243-246

[36] Deep Learning Algorithms for Defect Detection using Phased Array Ultrasonic Testing Data [J].

Barashok, Kseniia ;

Park, Junpil ;

Lee, Jaesun .

JOURNAL OF THE KOREAN SOCIETY FOR NONDESTRUCTIVE TESTING, 2023, 43 (01) :34-43

[37] Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms [J].

Hou, Lixin ;

Zhu, Yuxia ;

Wang, Mengke ;

Wei, Ning ;

Dong, Jiachi ;

Tao, Yaodong ;

Zhou, Jing ;

Zhang, Jian .

PLANTS-BASEL, 2024, 13 (22)

[38] Challenges of Geological Prospecting Big Data Mining and Integration Using Deep Learning Algorithms [J].

Zuo R. ;

Peng Y. ;

Li T. ;

Xiong Y. .

Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Science - Journal of China University of Geosciences, 2021, 46 (01) :350-358

[39] Data Transcription for India's Supreme Court Documents Using Deep Learning Algorithms [J].

Vaissnave, V. ;

Deepalakshmi, P. .

INTERNATIONAL JOURNAL OF ELECTRONIC GOVERNMENT RESEARCH, 2020, 16 (04) :21-41

[40] The microscopic visual forms in architectural art design following deep learning [J].

Yi Guo .

The Journal of Supercomputing, 2022, 78 :559-577

← 1 2 3 4 5 →