Search Results for author:
Found 46 papers, 19 papers with code
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding.
Universal Perturbation-based Secret Key-Controlled Data Hiding
In this paper, we propose a novel universal perturbation-based secret key-controlled data-hiding method, realizing data hiding with a single universal perturbation and data decoding with the secret key-controlled decoder.
Adversarial Examples in the Physical World: A Survey
However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding.
MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification
Due to the powerful ability in capturing the global information, Transformer has become an alternative architecture of CNNs for hyperspectral image classification.
Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have been demonstrated their powerful ability to extract discriminative features for hyperspectral image classification.
RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World
In this paper, we propose a novel Reflected Light Attack (RFLA), featuring effective and stealthy in both the digital and physical world, which is implemented by placing the color transparent plastic sheet and a paper cut of a specific shape in front of the mirror to create different colored geometries on the target object.
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations
To bridge the gap between AutoML and semantic adversarial attacks, we propose a novel method called multi-objective evolutionary search of variable-length composite semantic perturbations (MES-VCSP).
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation
Finally, we propose a multi-fidelity online surrogate during optimization to further decrease the search cost.
Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World
Infrared imaging systems have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern.
A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applications
Then, we extract the copyright information from the encoded copyrighted image with the devised copyright decoder.
Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator
Therefore, this work proposes a novel multi-fidelity learning method based on the Fourier Neural Operator by jointing abundant low-fidelity data and limited high-fidelity data under transfer learning paradigm.
Uncertainty Guided Ensemble Self-Training for Semi-Supervised Global Field Reconstruction
Recovering a globally accurate complex physics field from limited sensor is critical to the measurement and control in the aerospace engineering.
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator
The MLP embedding is propitious to more sparse input, while the others benefit from spatial information preservation and perform better with the increase of observation data.
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
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.
RBF-MGN:Solving spatiotemporal PDEs with Physics-informed Graph Neural Network
We introduce GNNs into physics-informed learning to better handle irregular domains with unstructured meshes.
Robust Regression with Highly Corrupted Data via Physics Informed Neural Networks
Physics-informed neural networks (PINNs) have been proposed to solve two main classes of problems: data-driven solutions and data-driven discovery of partial differential equations.
Differential Evolution based Dual Adversarial Camouflage: Fooling Human Eyes and Object Detectors
In the first stage, we optimize the global texture to minimize the discrepancy between the rendered object and the scene images, making human eyes difficult to distinguish.
A Survey on Physical Adversarial Attack in Computer Vision
Then, we discuss the existing physical attacks and focus on the technique for improving the robustness of physical attacks under complex physical environmental conditions.
Physics-informed MTA-UNet: Prediction of Thermal Stress and Thermal Deformation of Satellites
The rapid analysis of thermal stress and deformation plays a pivotal role in the thermal control measures and optimization of the structural design of satellites.
A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design
Therefore, we propose a multi-objective memetic algorithm for auto adversarial attack optimization design, which realizes the automatical search for the near-optimal adversarial attack towards defensed models.
Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network
To overcome the respective defects of UGF and BN, a novel reliability analysis method called UGF-BN is proposed for the complex MSS.
Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm
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.
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
Besides, for inverse PDE problems, problem parameters considered as new output layer weights are unified in a framework with forward PDE problems.
RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks
Learning solutions of partial differential equations (PDEs) with Physics-Informed Neural Networks (PINNs) is an attractive alternative approach to traditional solvers due to its flexibility and ease of incorporating observed data.
Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part II-Dependent systems
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
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).
A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain
Second, a physics-driven CNN surrogate with partial differential equation (PDE) residuals as a loss function is utilized for fast meshing (meshing surrogate); then, we present a data-driven surrogate model based on the multi-level reduced-order method, aiming to learn solutions of temperature field in the above regular computational plane (thermal surrogate).
Semi-supervision semantic segmentation with uncertainty-guided self cross supervision
However, the wrong pseudo labeling information generated by cross supervision would confuse the training process and negatively affect the effectiveness of the segmentation model.
Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation
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.
$A^{3}D$: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks
To alleviate these problems, in this paper, we first propose a novel platform called auto adversarial attack and defense ($A^{3}D$), which can help search for robust neural network architectures and efficient adversarial attacks.
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
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.
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
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.
A deep learning method based on patchwise training for reconstructing temperature field
To solve the problem, this work proposes a novel deep learning method based on patchwise training to reconstruct the temperature field of electronic equipment accurately from limited observation.
Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks
In this work, we develop a physics-informed neural network-based temperature field inversion (PINN-TFI) method to solve the TFI-HSS task and a coefficient matrix condition number based position selection of observations (CMCN-PSO) method to select optima positions of noise observations.
Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data
As the construction of data pairs in most engineering problems is time-consuming, data acquisition is becoming the predictive capability bottleneck of most deep surrogate models, which also exists in surrogate for thermal analysis and design.
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors.
A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems
To overcome this problem, this work develops a machine learning modelling benchmark for TFR-HSS task.
A novel meta-learning initialization method for physics-informed neural networks
Inspired by this idea, we propose the new Reptile initialization to sample more tasks from the parameterized PDEs and adapt the penalty term of the loss.
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification
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.
RBUE: A ReLU-Based Uncertainty Estimation Method of Deep Neural Networks
Deep Ensemble is widely considered the state-of-the-art method which can estimate the uncertainty with higher quality, but it is very expensive to train and test.
IDRLnet: A Physics-Informed Neural Network Library
Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs).
Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction
To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly.
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source Layout
Thermal issue is of great importance during layout design of heat source components in systems engineering, especially for high functional-density products.
Accelerating Physics-Informed Neural Network Training with Prior Dictionaries
Physics-Informed Neural Networks (PINNs) can be regarded as general-purpose PDE solvers, but it might be slow to train PINNs on particular problems, and there is no theoretical guarantee of corresponding error bounds.
Off-Road Drivable Area Extraction Using 3D LiDAR Data
We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application.
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning
This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications.
Robotics
