Zobrazeno 1 - 10
of 280
pro vyhledávání: '"Yang, Lijia"'
Publikováno v:
Nonlinear Engineering, Vol 12, Iss 1, Pp 1511-21 (2023)
With the improvement of the accuracy of experimental devices and measuring instruments, cavitation experiments such as cross-media vehicles and propellers have been carried out in small pools. However, the water quality in the laboratory and the engi
Externí odkaz:
https://doaj.org/article/b697d84de7814bf9bfe651167732f305
The flow-based generative model is a deep learning generative model, which obtains the ability to generate data by explicitly learning the data distribution. Theoretically its ability to restore data is stronger than other generative models. However,
Externí odkaz:
http://arxiv.org/abs/2106.07563
To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includ
Externí odkaz:
http://arxiv.org/abs/2106.07564
The Hermite-Gaussian (HG) modes make up a complete and orthonormal basis, which have been extensively used to describe optical fields. Here, we demonstrate, for the first time to our knowledge, deep learning-based modal decomposition (MD) of HG beams
Externí odkaz:
http://arxiv.org/abs/1907.06081
Autor:
Chen, Ying, Qi, Haoyue, Yang, Lijia, Xu, Liang, Wang, Jiaxuan, Guo, Jiazhuo, Zhang, Liang, Tan, Yuanyuan, Pan, Ronghui, Shu, Qingyao, Qian, Qian, Song, Shiyong
Publikováno v:
In Cell Reports 25 July 2023 42(7)
Publikováno v:
Optics Express, 27, 18683-18694 (2019)
We introduce deep learning technique to predict the beam propagation factor M^2 of the laser beams emitting from few-mode fiber for the first time, to the best of our knowledge. The deep convolutional neural network (CNN) is trained with paired data
Externí odkaz:
http://arxiv.org/abs/1904.11983
Publikováno v:
In Tribology International February 2023 180
Akademický článek
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Publikováno v:
Optics Express Vol. 27, Issue 7, pp. 10127-10137 (2019)
We introduce deep learning technique to perform complete mode decomposition for few-mode optical fiber for the first time. Our goal is to learn a fast and accurate mapping from near-field beam profiles to the complete mode coefficients, including bot
Externí odkaz:
http://arxiv.org/abs/1811.00882
Autor:
Zhang, Zhiyi, Mai, Qiongdan, Yang, Lijia, Chen, Yiwei, Chen, Zixu, Lin, Tao, Tan, Shimin, Wu, Zhiying, Cai, Yongjie, Cui, Taimei, Ouyang, Beiyin, Yang, Yi, Zeng, Lingchan, Ge, Zhenhuang, Zhang, Sien, Zeng, Gucheng, Pi, Jiang, Chen, Lingming
Publikováno v:
In International Journal of Medical Microbiology October 2022 312(7)