Dnyanesh G. Rajpathak, Mark Roboff, Hua Yu, Gautam Biswas
{"title":"Letter from the Guest Editors","authors":"Dnyanesh G. Rajpathak, Mark Roboff, Hua Yu, Gautam Biswas","doi":"10.4271/01-15-02-0008","DOIUrl":"https://doi.org/10.4271/01-15-02-0008","url":null,"abstract":"","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46089838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cause and Risk Factors of Maritime-Related Accidents for\u0000 Aircraft","authors":"Ruiliang Yang, Mingchong Lin, Libin Yang, W. Jin","doi":"10.4271/01-15-02-0015","DOIUrl":"https://doi.org/10.4271/01-15-02-0015","url":null,"abstract":"","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49038350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prognostics and Machine Learning to Assess Embedded Delamination\u0000 Tolerance in Composites","authors":"Savitha Narayanan Nambisan, B. Dattaguru","doi":"10.4271/01-15-02-0016","DOIUrl":"https://doi.org/10.4271/01-15-02-0016","url":null,"abstract":"","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46537864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Saied, Hadil Attieh, H. Mazeh, H. Shraim, C. Francis
{"title":"Supervised Learning Classification Applications in Fault Detection\u0000 and Diagnosis: An Overview of Implementations in Unmanned Aerial\u0000 Systems","authors":"M. Saied, Hadil Attieh, H. Mazeh, H. Shraim, C. Francis","doi":"10.4271/01-16-01-0004","DOIUrl":"https://doi.org/10.4271/01-16-01-0004","url":null,"abstract":"","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43347246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Numerical Analysis of a Separable Metal Composite Pressure\u0000 Vessel","authors":"A. Egorov","doi":"10.4271/01-16-01-0005","DOIUrl":"https://doi.org/10.4271/01-16-01-0005","url":null,"abstract":"","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47044979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Kaakai, Konstantin Dmitriev, S. Adibhatla, E. Baskaya, Emanuele Bezzecchi, Ramesh Bharadwaj, Barclay Brown, Giacomo Gentile, C. Gingins, S. Grihon, Christophe Travers
This article presents a new machine learning (ML) development lifecycle which will constitute the core of the new aeronautical standard on ML called AS6983, jointly being developed by working group WG-114/G34 of EUROCAE and SAE. The article also presents a survey of several existing standards and guidelines related to ML in aeronautics, automotive, and industrial domains by comparing and contrasting their scope, purpose, and results. Standards and guidelines reviewed include the European Union Aviation Safety Agency (EASA) Concept Paper, the DEEL (DEpendable and Explainable Learning) white paper “Machine Learning in Certified Systems”, Aerospace Vehicle System Institute (AVSI) Authorization for Expenditure (AFE) 87 report on Machine Learning, Guidance on the Assurance of Machine Learning for use in Autonomous Systems (AMLAS), Laboratoire National de Metrologie et d’Essais (LNE) Certification Standard of Processes for AI, the Underwriters Laboratories (UL) 4600 Safety Standard for Autonomous Vehicles, and the paper on Assuring the Machine Learning Lifecycle. These standards and guidelines are examined from the perspective of the learning assurance objectives they propose, and the means of evaluation and compliance for achieving these learning objectives. The reference used for comparison is the list of learning assurance objectives defined within the framework of AS6983 development. From this comparative analysis, and based on a coverage criterion defined in this article, only three (3) standards and guidelines exceed 50% coverage of the Machine Learning Development Lifecycle (MLDL) learning assurance objectives baseline. The next steps of this work are to update the AS6983 learning assurance objectives and improve the associated means of compliance to approach a coverage score of 100%, and offer a certification-based process to other domains that could benefit from the AS6983 standard.
{"title":"Toward a Machine Learning Development Lifecycle for Product Certification and Approval in Aviation","authors":"F. Kaakai, Konstantin Dmitriev, S. Adibhatla, E. Baskaya, Emanuele Bezzecchi, Ramesh Bharadwaj, Barclay Brown, Giacomo Gentile, C. Gingins, S. Grihon, Christophe Travers","doi":"10.4271/01-15-02-0009","DOIUrl":"https://doi.org/10.4271/01-15-02-0009","url":null,"abstract":"This article presents a new machine learning (ML) development lifecycle which will constitute the core of the new aeronautical standard on ML called AS6983, jointly being developed by working group WG-114/G34 of EUROCAE and SAE. The article also presents a survey of several existing standards and guidelines related to ML in aeronautics, automotive, and industrial domains by comparing and contrasting their scope, purpose, and results. Standards and guidelines reviewed include the European Union Aviation Safety Agency (EASA) Concept Paper, the DEEL (DEpendable and Explainable Learning) white paper “Machine Learning in Certified Systems”, Aerospace Vehicle System Institute (AVSI) Authorization for Expenditure (AFE) 87 report on Machine Learning, Guidance on the Assurance of Machine Learning for use in Autonomous Systems (AMLAS), Laboratoire National de Metrologie et d’Essais (LNE) Certification Standard of Processes for AI, the Underwriters Laboratories (UL) 4600 Safety Standard for Autonomous Vehicles, and the paper on Assuring the Machine Learning Lifecycle. These standards and guidelines are examined from the perspective of the learning assurance objectives they propose, and the means of evaluation and compliance for achieving these learning objectives. The reference used for comparison is the list of learning assurance objectives defined within the framework of AS6983 development. From this comparative analysis, and based on a coverage criterion defined in this article, only three (3) standards and guidelines exceed 50% coverage of the Machine Learning Development Lifecycle (MLDL) learning assurance objectives baseline. The next steps of this work are to update the AS6983 learning assurance objectives and improve the associated means of compliance to approach a coverage score of 100%, and offer a certification-based process to other domains that could benefit from the AS6983 standard.","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":"1 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41758922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Soleyman, Yang Chen, Joshua Fadaie, Fan Hung, D. Khosla, Shawn Moffit, Shane Roach, C. Tullock
{"title":"Predictive Modeling of Aircraft Dynamics Using Neural\u0000 Networks","authors":"S. Soleyman, Yang Chen, Joshua Fadaie, Fan Hung, D. Khosla, Shawn Moffit, Shane Roach, C. Tullock","doi":"10.4271/01-15-02-0010","DOIUrl":"https://doi.org/10.4271/01-15-02-0010","url":null,"abstract":"","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44994444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi-part Analysis and Techniques for Air Traffic Speech\u0000 Recognition","authors":"N. Srinivasan, S. Balasundaram","doi":"10.4271/01-15-02-0014","DOIUrl":"https://doi.org/10.4271/01-15-02-0014","url":null,"abstract":"","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47834966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Ongoing Safety Risk Assessment and Determination of Correction\u0000 Time Limit for Civil Aircraft","authors":"B. Jia, Zhuo Wang, Yi-qian Wang, Xiaowei Wen","doi":"10.4271/01-15-02-0013","DOIUrl":"https://doi.org/10.4271/01-15-02-0013","url":null,"abstract":"","PeriodicalId":44558,"journal":{"name":"SAE International Journal of Aerospace","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49415857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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