@inproceedings{d53fa2b2da6149a7813565e16c0d4a77,
title = "Applied Research on Supervised Learning in the Judgment of Airfoil Transition",
abstract = "This paper combines the latest machine learning techniques to develop an effective and reliable supervised learning model for transition judgment. Firstly, the variable-interval time average (VITA) method is used to transform the fluctuating pressure signal into a sequence of states in the Markov state space. Then we describe it using Markov chain model, and obtain its feature vectors. Then the hidden Markov model is used to pre-classify the feature vectors labeled using the traditional RMS criteria. And finally a classification model based on probability density distribution is established. The research shows that the model developed in this paper is effective and reliable and possesses a generalization ability. Compared with the traditional RMS criterion, a reasonable 'transition zone' can be obtained using the developed classification model without comparing the signals at multiple locations.",
keywords = "Classification model, Hidden Markov model, Markov chain model, Supervised learning, Transition judgment",
author = "Binbin Wei and Yongwei Gao and Dong Li",
note = "Publisher Copyright: {\textcopyright} 2021 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021. All rights reserved.; 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021 ; Conference date: 06-09-2021 Through 10-09-2021",
year = "2021",
language = "英语",
series = "32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021",
publisher = "International Council of the Aeronautical Sciences",
booktitle = "32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021",
}