1 code implementation • 25 Nov 2023 • Zhiqiang Gong, Xian Zhou, Wen Yao, Xiaohu Zheng, Ping Zhong
To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding.
no code implementations • 17 Jan 2023 • Yunyang Zhang, Zhiqiang Gong, Weien Zhou, Xiaoyu Zhao, Xiaohu Zheng, Wen Yao
Then, a self-supervised learning method for training the physics-driven deep multi-fidelity model (PD-DMFM) is proposed, which fully utilizes the physics characteristics of the engineering systems and reduces the dependence on large amounts of labeled low-fidelity data in the training process.
no code implementations • 15 Jun 2022 • Xu Liu, Wen Yao, Xiaohu Zheng, Yingchun Xu
To overcome the respective defects of UGF and BN, a novel reliability analysis method called UGF-BN is proposed for the complex MSS.
no code implementations • 16 May 2022 • Jialiang Sun, Xiaohu Zheng, Wen Yao, Xiaoya Zhang, Weien Zhou, Xiaoqian Chen
In satellite layout design, heat source layout optimization (HSLO) is an effective technique to decrease the maximum temperature and improve the heat management of the whole system.
no code implementations • 4 Apr 2022 • Xiaohu Zheng, Wen Yao, Xiaoqian Chen
This Part II proposes a novel method for BN reliability modeling and analysis to apply the compression idea to the complex multistate dependent system.
1 code implementation • 29 Mar 2022 • Xiaohu Zheng, Wen Yao, Yunyang Zhang, Xiaoya Zhang
To alleviate this problem, this paper proposes a consistency regularization-based deep polynomial chaos neural network (Deep PCNN) method, including the low-order adaptive PCE model (the auxiliary model) and the high-order polynomial chaos neural network (the main model).
no code implementations • 10 Mar 2022 • Yunyang Zhang, Zhiqiang Gong, Xiaohu Zheng, Xiaoyu Zhao, Wen Yao
However, the wrong pseudo labeling information generated by cross supervision would confuse the training process and negatively affect the effectiveness of the segmentation model.
1 code implementation • 8 Mar 2022 • Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong, Wen Yao, Yunyang Zhang, Xiaohu Zheng
This paper proposes a contrastive enhancement approach using latent prototypes to leverage latent classes and raise the utilization of similarity information between prototype and query features.
no code implementations • 14 Feb 2022 • Xiaohu Zheng, Wen Yao, Zhiqiang Gong, Yunyang Zhang, Xiaoya Zhang
To solve the above problem, this paper proposes an unsupervised method, i. e., the physics-informed deep Monte Carlo quantile regression method, for reconstructing temperature field and quantifying the aleatoric uncertainty caused by data noise.
1 code implementation • 14 Feb 2022 • Xiaohu Zheng, Wen Yao, Zhiqiang Gong, Yunyang Zhang, Xiaoyu Zhao, Tingsong Jiang
However, a lot of labeled data is needed to train CNN, and the common CNN can not quantify the aleatoric uncertainty caused by data noise.
1 code implementation • 22 Jul 2021 • Wen Yao, Xiaohu Zheng, Jun Zhang, Ning Wang, Guijian Tang
Based on the adaptive aPC, a semi-supervised deep adaptive arbitrary polynomial chaos expansion (Deep aPCE) method is proposed to reduce the training data cost and improve the surrogate model accuracy.