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Machine Learning for Aviation Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning for Aviation Data./
Author:	Meng, Yang.
Description:	1 online resource (93 pages)
Notes:	Source: Masters Abstracts International, Volume: 83-10.
Contained By:	Masters Abstracts International83-10.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29067160click for full text (PQDT)
ISBN:	9798426808706

Machine Learning for Aviation Data.
Meng, Yang.

Machine Learning for Aviation Data. - 1 online resource (93 pages)

Source: Masters Abstracts International, Volume: 83-10.

Thesis (M.S.)--Trent University (Canada), 2022.

Includes bibliographical references

This thesis is part of an industry project which collaborates with an aviation technology company on pilot performance assessment. In this project, we propose utilizing the pilots' training data to develop a model that can recognize the pilots' activity patterns for evaluation. The data will present as a time series, representing a pilot's actions during maneuvers. In this thesis, the main contribution is focusing on a multivariate time series dataset, including preprocessing and transformation. The main difficulties in time series classification is the data sequence of the time dimension. In this thesis, I developed an algorithm which formats time series data into equal length data. Three classification and two transformation methods were used. In total, there are six models for comparison. The initial accuracy was 40%. By optimization through resampling, we increased the accuracy to 60%.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798426808706Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Data MiningIndex Terms--Genre/Form:

542853
Electronic books.

Machine Learning for Aviation Data.
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500 $a Source: Masters Abstracts International, Volume: 83-10.
500 $a Advisor: McConell, Sabine; Hurley, Richard.
502 $a Thesis (M.S.)--Trent University (Canada), 2022.
504 $a Includes bibliographical references
520 $a This thesis is part of an industry project which collaborates with an aviation technology company on pilot performance assessment. In this project, we propose utilizing the pilots' training data to develop a model that can recognize the pilots' activity patterns for evaluation. The data will present as a time series, representing a pilot's actions during maneuvers. In this thesis, the main contribution is focusing on a multivariate time series dataset, including preprocessing and transformation. The main difficulties in time series classification is the data sequence of the time dimension. In this thesis, I developed an algorithm which formats time series data into equal length data. Three classification and two transformation methods were used. In total, there are six models for comparison. The initial accuracy was 40%. By optimization through resampling, we increased the accuracy to 60%.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 523869
650 4 $a Statistics. $3 517247
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Artificial intelligence. $3 516317
653 $a Data Mining
653 $a K-NN
653 $a Machine Learning
653 $a Multivariate Time Series Classification
653 $a Time Series Forest
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710 2 $a Trent University (Canada). $b Applied Modeling and Quantitative Methods. $3 2101153
773 0 $t Masters Abstracts International $g 83-10.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29067160 $z click for full text (PQDT)