Toward automated instructor pilots in legacy Air Force systems: Physiology-based flight difficulty classification via machine learning.
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| Title: | Toward automated instructor pilots in legacy Air Force systems: Physiology-based flight difficulty classification via machine learning. |
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| Authors: | Caballero, William N.1 (AUTHOR) william.caballero@us.af.mil, Gaw, Nathan2 (AUTHOR) nathan.gaw@afit.edu, Jenkins, Phillip R.2 (AUTHOR) phillip.jenkins@afit.edu, Johnstone, Chancellor1,2 (AUTHOR) chancellor.johnstone@afit.edu |
| Source: | Expert Systems with Applications. Nov2023, Vol. 231, pN.PAG-N.PAG. 1p. |
| Subjects: | United States Air Force Academy, Machine learning, Air forces, Decision support systems, Air pilots, Principal components analysis, Channel estimation, Feature selection |
| Abstract: | The United States Air Force (USAF) is struggling to train enough pilots to meet operational requirements. Technology has advanced rapidly over the last 70 years but USAF pilot training has not. Modern operational requirements demand a change and, for this reason, USAF senior leadership has advocated for innovation. The automation of instructor and evaluator pilots in select bottlenecks (e.g., simulators) is one such measure. However, to implement this vision, numerous technical issues must be mitigated. Accurate classification of flight difficulty is a foundational problem underpinning many of these technical issues, which requires either the acquisition of new systems or the development of new procedures. Therefore, given this need and the costly nature of purchasing new equipment, physiological-based classification of flight difficulty is our focus herein. Leveraging multimodal data from a designed experiment of pilots landing a simulated aircraft, we develop a high-quality machine learning pipeline for classifying flight difficulty, called the Multi-Modal Functional-based Decision Support System (MMF-DSS). MMF-DSS distills a tabular set of features from our multimodal and functional data through the use of functional principal component analysis, summary statistics, and BorutaSHAP. In this manner, information is derived from the time-series data via the generation of hundreds of features, of which a small subset having the most predictive capability is discerned. Four full factorial designs are used to perform hyperparameter tuning on a set of classifiers. In so doing, a superlative technique is identified. Impacts on executive decision making are examined as well as associated policymaking implications. Alternative classifiers are considered for use within our pipeline that trade predictive accuracy for cost efficiency, and recommendations for choosing among these alternatives is provided. • There currently exists a shortage of Air Force pilots. • Instructor and evaluator pilot bottlenecks could be solved through automation. • Multimodal fusion of physiological data-streams provides instructor augmentation. • Machine learning (e.g., AdaBoost) shows success in determining sortie difficulty. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 169876204 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Toward automated instructor pilots in legacy Air Force systems: Physiology-based flight difficulty classification via machine learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Caballero%2C+William+N%2E%22">Caballero, William N.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> william.caballero@us.af.mil</i><br /><searchLink fieldCode="AR" term="%22Gaw%2C+Nathan%22">Gaw, Nathan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> nathan.gaw@afit.edu</i><br /><searchLink fieldCode="AR" term="%22Jenkins%2C+Phillip+R%2E%22">Jenkins, Phillip R.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> phillip.jenkins@afit.edu</i><br /><searchLink fieldCode="AR" term="%22Johnstone%2C+Chancellor%22">Johnstone, Chancellor</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> chancellor.johnstone@afit.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Nov2023, Vol. 231, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22United+States+Air+Force+Academy%22">United States Air Force Academy</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Air+forces%22">Air forces</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Air+pilots%22">Air pilots</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Channel+estimation%22">Channel estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The United States Air Force (USAF) is struggling to train enough pilots to meet operational requirements. Technology has advanced rapidly over the last 70 years but USAF pilot training has not. Modern operational requirements demand a change and, for this reason, USAF senior leadership has advocated for innovation. The automation of instructor and evaluator pilots in select bottlenecks (e.g., simulators) is one such measure. However, to implement this vision, numerous technical issues must be mitigated. Accurate classification of flight difficulty is a foundational problem underpinning many of these technical issues, which requires either the acquisition of new systems or the development of new procedures. Therefore, given this need and the costly nature of purchasing new equipment, physiological-based classification of flight difficulty is our focus herein. Leveraging multimodal data from a designed experiment of pilots landing a simulated aircraft, we develop a high-quality machine learning pipeline for classifying flight difficulty, called the Multi-Modal Functional-based Decision Support System (MMF-DSS). MMF-DSS distills a tabular set of features from our multimodal and functional data through the use of functional principal component analysis, summary statistics, and BorutaSHAP. In this manner, information is derived from the time-series data via the generation of hundreds of features, of which a small subset having the most predictive capability is discerned. Four full factorial designs are used to perform hyperparameter tuning on a set of classifiers. In so doing, a superlative technique is identified. Impacts on executive decision making are examined as well as associated policymaking implications. Alternative classifiers are considered for use within our pipeline that trade predictive accuracy for cost efficiency, and recommendations for choosing among these alternatives is provided. • There currently exists a shortage of Air Force pilots. • Instructor and evaluator pilot bottlenecks could be solved through automation. • Multimodal fusion of physiological data-streams provides instructor augmentation. • Machine learning (e.g., AdaBoost) shows success in determining sortie difficulty. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.eswa.2023.120711 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: United States Air Force Academy Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Air forces Type: general – SubjectFull: Decision support systems Type: general – SubjectFull: Air pilots Type: general – SubjectFull: Principal components analysis Type: general – SubjectFull: Channel estimation Type: general – SubjectFull: Feature selection Type: general Titles: – TitleFull: Toward automated instructor pilots in legacy Air Force systems: Physiology-based flight difficulty classification via machine learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Caballero, William N. – PersonEntity: Name: NameFull: Gaw, Nathan – PersonEntity: Name: NameFull: Jenkins, Phillip R. – PersonEntity: Name: NameFull: Johnstone, Chancellor IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 11 Text: Nov2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09574174 Numbering: – Type: volume Value: 231 Titles: – TitleFull: Expert Systems with Applications Type: main |
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