Search Results for author: Xiaohu Zheng

Found 11 papers, 5 papers with code

HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding

1 code implementation25 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.

Classification Hyperspectral image analysis +2

Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network

no code implementations17 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.

Self-Supervised Learning

Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network

no code implementations15 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.

Computational Efficiency

Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm

no code implementations16 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.

Layout Design Management +1

Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part II-Dependent systems

no code implementations4 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.

Consistency regularization-based Deep Polynomial Chaos Neural Network Method for Reliability Analysis

1 code implementation29 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).

Semi-supervision semantic segmentation with uncertainty-guided self cross supervision

no code implementations10 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.

Segmentation Semantic Segmentation

Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation

1 code implementation8 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.

Segmentation

Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis

no code implementations14 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.

regression

Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction

1 code implementation14 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.

regression

Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification

1 code implementation22 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.

Dimensionality Reduction Uncertainty Quantification

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