Zobrazeno 1 - 10
of 701
pro vyhledávání: '"ZHANG Tianyang"'
Publikováno v:
Chengshi guidao jiaotong yanjiu, Vol 27, Iss 7, Pp 158-163 (2024)
Objective The rationality of joint structures is crucial for station safety. Therefore, it is necessary to study the enhancement effect of additional prestressing measures on the flexural performance of prefabricated station structure bolt joints. Me
Externí odkaz:
https://doaj.org/article/54a232cfc3bf48c08b24f4fb5f858e7b
Publikováno v:
能源环境保护, Vol 37, Iss 5, Pp 99-100 (2023)
The sludge produced by urban drinking water treatment plants has a high yield, low organic content, and a high risk of inorganic pollution. Its green and low-carbon treatment, as well as resource utilization, has become urgent issues in achieving the
Externí odkaz:
https://doaj.org/article/da3b952c4857497bba4d094eb6f0a3ab
Autor:
Zhang, Tianyang, Jiang, Zhuoxuan, Bai, Shengguang, Zhang, Tianrui, Lin, Lin, Liu, Yang, Ren, Jiawei
With the ever-increasing demands on Question Answering (QA) systems for IT operations and maintenance, an efficient and supervised fine-tunable framework is necessary to ensure the data security, private deployment and continuous upgrading. Although
Externí odkaz:
http://arxiv.org/abs/2410.15805
Land cover analysis using hyperspectral images (HSI) remains an open problem due to their low spatial resolution and complex spectral information. Recent studies are primarily dedicated to designing Transformer-based architectures for spatial-spectra
Externí odkaz:
http://arxiv.org/abs/2404.18213
Autor:
Zhang, Xiangrong, Zhang, Tianyang, Wang, Guanchun, Zhu, Peng, Tang, Xu, Jia, Xiuping, Jiao, Licheng
Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capa
Externí odkaz:
http://arxiv.org/abs/2309.06751
Autor:
Zhang, Tianyang1 (AUTHOR), Dewancker, Bart Julien1 (AUTHOR), Gao, Weijun1 (AUTHOR), Zhao, Xueyuan2 (AUTHOR), Wei, Xindong3 (AUTHOR) weixindong@jlju.edu.cn, Liu, Zu-An4 (AUTHOR), Chen, Weilun3 (AUTHOR), Zhao, Qinfeng1 (AUTHOR)
Publikováno v:
Scientific Reports. 10/25/2024, Vol. 14 Issue 1, p1-21. 21p.
Autor:
Jia, Xi, Bartlett, Joseph, Chen, Wei, Song, Siyang, Zhang, Tianyang, Cheng, Xinxing, Lu, Wenqi, Qiu, Zhaowen, Duan, Jinming
Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource-intensive and time-consuming.
Externí odkaz:
http://arxiv.org/abs/2211.16342
Due to their extreme long-range modeling capability, vision transformer-based networks have become increasingly popular in deformable image registration. We believe, however, that the receptive field of a 5-layer convolutional U-Net is sufficient to
Externí odkaz:
http://arxiv.org/abs/2208.04939
Existing explanation models generate only text for recommendations but still struggle to produce diverse contents. In this paper, to further enrich explanations, we propose a new task named personalized showcases, in which we provide both textual and
Externí odkaz:
http://arxiv.org/abs/2207.00422
Autor:
Zhang, Tianyang, Zheng, Shaoming, Cheng, Jun, Jia, Xi, Bartlett, Joseph, Cheng, Xinxing, Fu, Huazhu, Qiu, Zhaowen, Liu, Jiang, Duan, Jinming
Generative models have been widely proposed in image recognition to generate more images where the distribution is similar to that of the real ones. It often introduces a discriminator network to differentiate the real data from the generated ones. S
Externí odkaz:
http://arxiv.org/abs/2205.12857