English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In aircraft industry, market needs evolve quickly in a high competitivenesscontext. This requires adapting a given aircraft model in minimum timeconsidering for example an increase of range or the number of passengers (cfA330 NEO family). The computation of loads and stress to resize the airframe ison the critical path of this aircraft variant definition: this is a consumingand costly process, one of the reason being the high dimen-sionality and thelarge amount of data. This is why Airbus has invested since a couple of yearsin Big Data approaches (statistic methods up to machine learning) to improvethe speed, the data value extraction and the responsiveness of this process.This paper presents recent advances in this work made in cooperation betweenAirbus, ENAC and Institut de Math{\'e}-matiques de Toulouse in the framework ofa proof of value sprint project. It compares the influence of three dimensionalreduction techniques (PCA, polynomial fitting, combined) on the extrapolationcapabilities of Regression Trees based algorithms for loads prediction. Itshows that AdaBoost with Random Forest offers promising results in average interms of accuracy and computational time to estimate loads on which a PCA isapplied only on the outputs.

A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft