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Found 14 papers, 2 papers with code
FlexiMo: A Flexible Remote Sensing Foundation Model
The rapid expansion of multi-source satellite imagery drives innovation in Earth observation, opening unprecedented opportunities for Remote Sensing Foundation Models to harness diverse data.
SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
Modern deep reinforcement learning (DRL) methods have made significant advances in handling continuous action spaces.
SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing
SeaMo leverages a masked image modeling framework to fully exploit the spatial, spectral, and seasonal dimensions of RS data.
Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage
Results from numerical simulations and experiments indicate that incorporating the mechanical characteristics into the autoencoder allows for up to a 35% improvement in the detection and localization of minor damage over a standard autoencoder.
S2MAE: A Spatial-Spectral Pretraining Foundation Model for Spectral Remote Sensing Data
The model efficiently captures local spectral consistency and spatial invariance using compact cube tokens demonstrating versatility to diverse input characteristics.
SpectralGPT: Spectral Remote Sensing Foundation Model
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment
This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method for a large-scale 3D complex environment.
Rethinking the editing of generative adversarial networks: a method to estimate editing vectors based on dimension reduction
While Generative Adversarial Networks (GANs) have recently found applications in image editing, most previous GAN-based image editing methods require largescale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images.
Low-Carbon Economic Dispatch of Bulk Power Systems Using Nash Bargaining Game
Because the Nash bargaining solution satisfies Pareto effectiveness, we analyze the computational complexity of Pareto frontiers with parametric linear programming and interpret the inefficiency of the method.
Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components
Therefore, to reduce computational cost while preserving accuracy, a deep learning model, Neuro-DynaStress, is proposed to predict the entire sequence of stress distribution based on finite element simulations using a partial differential equation (PDE) solver.
Physics Informed Neural Network for Dynamic Stress Prediction
Therefore, to reduce computational cost while maintaining accuracy, a Physics Informed Neural Network (PINN), PINN-Stress model, is proposed to predict the entire sequence of stress distribution based on Finite Element simulations using a partial differential equation (PDE) solver.
NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems
Structural monitoring for complex built environments often suffers from mismatch between design, laboratory testing, and actual built parameters.
Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization
The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk.
A new communication paradigm: from bit accuracy to semantic fidelity
In this article, we deploy semantics to solve the spectrum and power bottleneck and propose a first understanding and then transmission framework with high semantic fidelity.
Networking and Internet Architecture
