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Found 11 papers, 3 papers with code
Deep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data
The performance of the latest 1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and 2D-Justo-UNet-Simple) for cloud segmentation and classification is assessed.
Distributed MPC for autonomous ships on inland waterways with collaborative collision avoidance
Furthermore, the proposed algorithm can safely deviate from traffic rules when necessary to increase efficiency in complex scenarios.
Quick unsupervised hyperspectral dimensionality reduction for earth observation: a comparison
Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly.
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction
Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission.
Sea-Land-Cloud Segmentation in Satellite Hyperspectral Imagery by Deep Learning
Satellites are increasingly adopting on-board AI for enhanced autonomy through in-orbit inference.
The Syncline Model -- Analyzing the Impact of Time Synchronization in Sensor Fusion
The accuracy of sensor fusion algorithms are limited by either the intrinsic sensor noise, or by the quality of time synchronization of the sensors.
Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments
Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitudinal and lateral motions.
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning
Its high computational complexity results in high power consumption from the control algorithm, which could account for a significant share of the energy resources in battery-powered embedded systems.
Reinforcement Learning of the Prediction Horizon in Model Predictive Control
Model predictive control (MPC) is a powerful trajectory optimization control technique capable of controlling complex nonlinear systems while respecting system constraints and ensuring safe operation.
Optimization of the Model Predictive Control Update Interval Using Reinforcement Learning
In control applications there is often a compromise that needs to be made with regards to the complexity and performance of the controller and the computational resources that are available.
Accelerating Reinforcement Learning with Suboptimal Guidance
Reinforcement Learning in domains with sparse rewards is a difficult problem, and a large part of the training process is often spent searching the state space in a more or less random fashion for any learning signals.
