In this paper, we perform registration of noisy curves. We provide an appropriate model in estimating the rotation and scaling parameters to adjust a set of curves through a M-estimation procedure. We prove the consistency and the asymptotic normality of our estimators. Numerical simulation and a real life aeronautic e…
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10 results for “Aeronautics”
Paper examines how dimension reduction affects aircraft load prediction.
problem Efficiently predicting aircraft loads for derivative models.
method Compared PCA, polynomial fitting, and combined techniques on regression trees.
result AdaBoost with Random Forest offers promising results for load estimation.
Stacked Monte Carlo improves option pricing efficiency.
problem Evaluating option prices in various models.
method A stacking technique that approximates Monte Carlo draws using a specified function.
result Shows efficiency in European and Asian Call options in both constant and stochastic volatility models.
The article develops a model for predicting aircraft motion in terminal airspace.
problem Predicting aircraft motion for collision avoidance and safety analysis.
method Fits a probabilistic generative model to historical position data of aircraft landings and takeoffs.
result The model generates realistic trajectories, provides accurate predictions, and captures statistical properties of aircraft trajectories.
This chapter introduces quaternion machine learning for 3D rotations.
problem Lack of quaternion machine learning for 3D rotations.
method Augmented statistics, widely linear models, quaternion calculus, mean square estimation.
result Foundation for quaternion machine learning.
Entropy-based GP adaptive design improves failure probability estimation.
problem Limited accuracy in failure probability estimation due to model evaluation costs.
method Entropy-based Gaussian process (GP) adaptive design combined with multifidelity importance sampling (MFIS).
result More accurate failure probability estimates and higher confidence.
End-to-end framework optimizes constrained trajectories using data-driven methods.
problem Optimizing trajectories under constraints with limited dynamics knowledge.
method Data-driven approach decomposes trajectories into function basis, uses maximum a posteriori for optimization, and incorporates linear constraints.
result Commanding results in aeronautics and sailing route optimization.
Machine learning improves aircraft performance prediction by analyzing flight data.
problem Limited aircraft performance models based on industry-wide guidelines.
method Use machine learning on flight data to estimate drag and lift coefficients.
result Excellent accuracy in real-life data, supporting aerodynamics principles.
New method clusters and visualizes anomalies in complex systems.
problem Identifying simultaneous extreme values in random vectors.
method Mixture model based on multivariate extreme value theory.
result Assigns posterior probabilities for anomaly types and clusters extreme observations.
Space exploration technology advances exponentially, consistent with Moore's and Wright's laws.
problem Predicting the advancement of space exploration technology.
method Analysis of Moore's and Wright's laws applied to space exploration technology.
result Spacecraft technology advances exponentially, consistent with Moore's and Wright's laws.