no code implementations • 30 Mar 2024 • Chengyuan Li, Tianyu Zhang, Xusheng Du, Ye Zhang, Haoran Xie
This paper explores the extensive applications of generative AI technologies in architectural design, a trend that has benefited from the rapid development of deep generative models.
no code implementations • 2 May 2023 • Chen Li, Yang Cao, Ye Zhu, Debo Cheng, Chengyuan Li, Yasuhiko Morimoto
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy.
no code implementations • 28 Oct 2022 • Chengyuan Li, Zhifang Qiu, Yugao Ma, Meifu Li
In summary, this work for the first time applies the novel composite deep learning model TFT to the prognosis of key parameters after a reactor accident, and makes a positive contribution to the establishment of a more intelligent and staff-light maintenance method for reactor systems.
no code implementations • 30 Aug 2022 • Chengyuan Li, Zhifang Qiu, Zhangrui Yan, Meifu Li
With the mass construction of Gen III nuclear reactors, it is a popular trend to use deep learning (DL) techniques for fast and effective diagnosis of possible accidents.
no code implementations • 28 Aug 2022 • Chengyuan Li, Meifu Li, Zhifang Qiu
Thus, the encoder part of the framework is able to automatically infer valid representations from partially missing and noisy monitoring data that reflect the complete and noise-free original data, and the representation vectors can be used for downstream tasks for accident diagnosis or else.
no code implementations • 3 Aug 2022 • Chengyuan Li, Meifu Li, Zhifang Qiu
The results show that the TRES-CNN based diagnostic model successfully predicts the position and size of breaks in LOCA via selected 15 parameters of HPR1000, with 25% of time consumption while training the model compared the process using total 38 parameters.
no code implementations • 4 Sep 2021 • Chenjie Wang, Chengyuan Li, Bin Luo, Wei Wang, Jun Liu
Then we extend SOLOV2 to capture temporal information in video to learn motion information, and propose a moving object instance segmentation network with RiWFPN called RiWNet.
no code implementations • 18 Dec 2020 • Chengyuan Li, Jun Liu, Hailong Hong, Wenju Mao, Chenjie Wang, Chudi Hu, Xin Su, Bin Luo
On the basis of this, a novel octave convolution-based semantic attention feature pyramid network (OcSaFPN) is proposed to get higher accuracy in object detection with noise.
no code implementations • 26 Jul 2020 • Chenjie Wang, Chengyuan Li, Bin Luo
Most scenes in practical applications are dynamic scenes containing moving objects, so segmenting accurately moving objects is crucial for many computer vision applications.
no code implementations • 10 Mar 2020 • Chenjie Wang, Bin Luo, Yun Zhang, Qing Zhao, Lu Yin, Wei Wang, Xin Su, Yajun Wang, Chengyuan Li
The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects.