501 citations · 530 across the 14 of their papers we have counts for
14 papers
CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectors
Jiahuan Long, Wen Yao, Tingsong Jiang +1
Adversarial patches are widely used to evaluate the robustness of object detection systems in real-world scenarios. These patches were initially designed to deceive single-modal de…
Improving the Transferability of Adversarial Examples by Feature Augmentation
Donghua Wang, Wen Yao, Tingsong Jiang +3
Despite the success of input transformation-based attacks on boosting adversarial transferability, the performance is unsatisfying due to the ignorance of the discrepancy across mo…
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
Zhiqiang Gong, Xian Zhou, Wen Yao +2
The dissection of hyperspectral images into intrinsic components through hyperspectral intrinsic image decomposition (HIID) enhances the interpretability of hyperspectral data, pro…
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
Yunyang Zhang, Zhiqiang Gong, Weien Zhou +3
Temperature field prediction is of great importance in the thermal design of systems engineering, and building the surrogate model is an effective way for the task. Generally, larg…
Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network
Xu Liu, Wen Yao, Xiaohu Zheng +1
In the complex multi-state system (MSS), reliability analysis is a significant research content, both for equipment design, manufacturing, usage and maintenance. Universal Generati…
Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm
Jialiang Sun, Xiaohu Zheng, Wen Yao +3
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…
Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part II-Dependent systems
Xiaohu Zheng, Wen Yao, Xiaoqian Chen
In using the Bayesian network (BN) to construct the complex multistate system's reliability model as described in Part I, the memory storage requirements of the node probability ta…
Consistency regularization-based Deep Polynomial Chaos Neural Network Method for Reliability Analysis
Xiaohu Zheng, Wen Yao, Yunyang Zhang +1
Polynomial chaos expansion (PCE) is a powerful surrogate model-based reliability analysis method. Generally, a PCE model with a higher expansion order is usually required to obtain…
Semi-supervision semantic segmentation with uncertainty-guided self cross supervision
Yunyang Zhang, Zhiqiang Gong, Xiaohu Zheng +2
As a powerful way of realizing semi-supervised segmentation, the cross supervision method learns cross consistency based on independent ensemble models using abundant unlabeled ima…
Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation
Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong +3
Few-shot segmentation enables the model to recognize unseen classes with few annotated examples. Most existing methods adopt prototype learning architecture, where support prototyp…
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
Xiaohu Zheng, Wen Yao, Zhiqiang Gong +2
Temperature field reconstruction is essential for analyzing satellite heat reliability. As a representative machine learning model, the deep convolutional neural network (DCNN) is…
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
Xiaohu Zheng, Wen Yao, Zhiqiang Gong +3
For the temperature field reconstruction (TFR), a complex image-to-image regression problem, the convolutional neural network (CNN) is a powerful surrogate model due to the convolu…
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification
Wen Yao, Xiaohu Zheng, Jun Zhang +2
The surrogate model-based uncertainty quantification method has drawn much attention in many engineering fields. Polynomial chaos expansion (PCE) and deep learning (DL) are powerfu…
Self-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations
Zixue Xiang, Wei Peng, Xiaohu Zheng +2
There have been several efforts to Physics-informed neural networks (PINNs) in the solution of the incompressible Navier-Stokes fluid. The loss function in PINNs is a weighted sum…