Artificial Neural Networks for PIO Events Classification Comparing Different Data Collection Procedures

Artificial Neural Networks for PIO Events Classification Comparing Different Data Collection Procedures | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Artificial Neural Networks for PIO Events Classification Comparing Different Data Collection Procedures Adriano Ghigiarelli Bruschi, Daniel Drewiacki, Jorge Henrique Bidinotto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3469609/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This work evaluates the accuracy and reliability of PIO test classification using the PIO Rating Scale and proposes using an automatic tool for this evaluation based on test data to eliminate the subjectivity inherent to the application of rating scales. Two test procedures (Discrete Synthetic Task and Pitch Capture) are executed in a flight simulator, using aircraft dynamic models with different PIO proneness and experienced flight test pilots. The results show a significant effect of subjectivity in pilot rating and various reliability for different test procedures. This data is used to build an Artificial Neural Network (ANN) proposed to classify the executions using the PIO Rating Scale. The ANN presented low computational cost and 97.1% accuracy when using data extracted from the Pitch Capture test procedure. Pilot induced oscillations Artificial Neural Network Flight Tests Flying qualities evaluation Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 Mar, 2024 Reviewers invited by journal 04 Mar, 2024 Editor assigned by journal 28 Oct, 2023 First submitted to journal 22 Oct, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3469609","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276302860,"identity":"58f496cc-abf8-4967-b6d6-58a142ba85bd","order_by":0,"name":"Adriano Ghigiarelli Bruschi","email":"","orcid":"","institution":"USP EESC: Universidade de Sao Paulo Escola de Engenharia de Sao Carlos","correspondingAuthor":false,"prefix":"","firstName":"Adriano","middleName":"Ghigiarelli","lastName":"Bruschi","suffix":""},{"id":276302861,"identity":"08a8cf25-70f0-473d-8b57-b2cbdaafee63","order_by":1,"name":"Daniel Drewiacki","email":"","orcid":"","institution":"Embraer SA","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Drewiacki","suffix":""},{"id":276302862,"identity":"05824179-1b62-4ff8-b6d0-a723560bc645","order_by":2,"name":"Jorge Henrique Bidinotto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACxhk8DAd4DGxg/ANEa0kjQQuDBA8DAw/DYRK0MM/uPXjgTcF5e/4GHuPPBQx38gk7bM65hINzDG4nzjjAYyY9g+GZZQNBLTNyDA7zGNxOMGDgMWMGutCAsC0QLefsgVqMP5Oi5QDjBgYeA2kiteSB/JKcOOMwW5n0DINnhLUYzsg9/OHNHzt7/vbmzZ8LKu4QoaUBxmIGIcIaGBjkkTnMRGgYBaNgFIyCEQgAWLY6PP8zGJ4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5352-2326","institution":"University of Sao Paulo Sao Carlos School of Engineering: Universidade de Sao Paulo Escola de Engenharia de Sao Carlos","correspondingAuthor":true,"prefix":"","firstName":"Jorge","middleName":"Henrique","lastName":"Bidinotto","suffix":""}],"badges":[],"createdAt":"2023-10-20 11:55:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3469609/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3469609/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52083243,"identity":"b973e71a-1ff4-4fa3-9f65-e9a5f2f2c72f","added_by":"auto","created_at":"2024-03-06 11:35:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":791283,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleBMSE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3469609/v1_covered_902f4ee6-00df-4f1a-a40c-2248630caa86.pdf"}],"financialInterests":"","formattedTitle":"Artificial Neural Networks for PIO Events Classification Comparing Different Data Collection Procedures","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-brazilian-society-of-mechanical-sciences-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmse","sideBox":"Learn more about [Journal of the Brazilian Society of Mechanical Sciences and Engineering](http://link.springer.com/journal/40430)","snPcode":"40430","submissionUrl":"https://www.editorialmanager.com/bmse/default2.aspx","title":"Journal of the Brazilian Society of Mechanical Sciences and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pilot induced oscillations, Artificial Neural Network, Flight Tests, Flying qualities evaluation","lastPublishedDoi":"10.21203/rs.3.rs-3469609/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3469609/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This work evaluates the accuracy and reliability of PIO test classification using the PIO Rating Scale and proposes using an automatic tool for this evaluation based on test data to eliminate the subjectivity inherent to the application of rating scales. Two test procedures (Discrete Synthetic Task and Pitch Capture) are executed in a flight simulator, using aircraft dynamic models with different PIO proneness and experienced flight test pilots. The results show a significant effect of subjectivity in pilot rating and various reliability for different test procedures. This data is used to build an Artificial Neural Network (ANN) proposed to classify the executions using the PIO Rating Scale. The ANN presented low computational cost and 97.1% accuracy when using data extracted from the Pitch Capture test procedure.","manuscriptTitle":"Artificial Neural Networks for PIO Events Classification Comparing Different Data Collection Procedures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 11:27:11","doi":"10.21203/rs.3.rs-3469609/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-03-06T01:02:24+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-04T15:12:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-10-28T23:26:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of the Brazilian Society of Mechanical Sciences and Engineering","date":"2023-10-23T02:56:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-brazilian-society-of-mechanical-sciences-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmse","sideBox":"Learn more about [Journal of the Brazilian Society of Mechanical Sciences and Engineering](http://link.springer.com/journal/40430)","snPcode":"40430","submissionUrl":"https://www.editorialmanager.com/bmse/default2.aspx","title":"Journal of the Brazilian Society of Mechanical Sciences and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b78097d3-ae3e-4048-bf4c-824e7c3f0382","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-29T22:17:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 11:27:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3469609","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3469609","identity":"rs-3469609","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}