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Cheng Yuan 0003
Person information
- affiliation: Guangzhou University, Earthquake Engineering Research and Test Center, China
Other persons with the same name
- Cheng Yuan — disambiguation page
- Cheng Yuan 0001 — Michigan State University, Department of Computer Science and Engineering, East Lansing, MI, USA
- Cheng Yuan 0002
— Shanghai Jiao Tong University, School of Biomedical Engineering, China
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2020 – today
- 2024
- [j9]Fengyu Zhang, Qingzhao Kong, Cheng Yuan, Peizhen Li:
Automatic generation of architecture drawings from point clouds. Comput. Aided Civ. Infrastructure Eng. 39(22): 3477-3488 (2024) - [j8]Qingsong Xiong
Three-stage unsupervised learning approach fusing novel pseudo-label diffusion and math-physics translating for real-time structural damage detection. Eng. Appl. Artif. Intell. 138: 109438 (2024) - [j7]Chang He, Qingzhao Kong, Keyan Ji, Qingsong Xiong
Deep learning-based analysis of interface performance between brittle engineering materials and composites. Expert Syst. Appl. 255: 124920 (2024) - 2023
- [j6]Zhenyu Peng
Development of acoustic denoising learning network for communication enhancement in construction sites. Adv. Eng. Informatics 56: 101981 (2023) - [j5]Qingzhao Kong, Jiaxuan Gu, Bing Xiong, Cheng Yuan:
Vision-aided three-dimensional damage quantification and finite element model geometric updating for reinforced concrete structures. Comput. Aided Civ. Infrastructure Eng. 38(17): 2378-2390 (2023) - [j4]Qingsong Xiong
A novel deep convolutional image-denoiser network for structural vibration signal denoising. Eng. Appl. Artif. Intell. 117(Part): 105507 (2023) - [j3]Yanzhi Qi
Damage analysis and quantification of RC beams assisted by Damage-T Generative Adversarial Network. Eng. Appl. Artif. Intell. 117(Part): 105536 (2023) - [j2]Qingsong Xiong
GTRF: A general deep learning framework for tuples recognition towards supervised, semi-supervised and unsupervised paradigms. Eng. Appl. Artif. Intell. 124: 106500 (2023) - [j1]Tao Huang, Cheng Yuan, Peizhen Li
Spatial displacement tracking of vibrating structure using multiple feature points assisted binocular visual reconstruction. Eng. Appl. Artif. Intell. 126: 106889 (2023) - [c1]Qingsong Xiong
Vibration Recognition Based on Feature Extraction by Deep Autoencoder. FAIML 2023: 79-84
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