[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120716-en":3,"doc-seo-120716-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120716,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine learning and mixed reality for smart aviation: Applications and challenges","The aviation industry is a rapidly evolving sector that must adopt advanced technologies to remain efficient, safe, and secure. While some airlines invest in machine learning and mixed reality, many regional airlines still depend on inefficient strategies and lack digital applications. This paper reviews state-of-the-art approaches integrating machine learning and mixed reality across aerospace design, manufacturing, testing, and services. It also examines passenger-experience enhancement through autonomous, self-service, and data-visualization systems, and addresses safety, environmental, technological, cost, security, capacity, and regulatory challenges with potential solution directions.","Journal of Air Transport Management 111 (2023) 102437  \nContents lists available at ScienceDirect  \nJournal of Air Transport Management  \njournal [homepage: www.elsevier.com/locate/jairtraman](homepage: www.elsevier.com/locate/jairtraman)  \n| Machine learning and mixed reality for smart aviation: Applications and challenges |  |  |  |\n| --- | --- | --- | --- |\n| Yirui Jiang, Trung Hieu Tran *, Leon Williams\u003Cbr>Centre for Design Engineering, Cranfield University, Bedfordshire, MK43 0AL, UK |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Aerospace engineering\u003Cbr>Artificial intelligence Intelligent aviation Machine learning Mixed reality Passenger experience Smart aviation |  | The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency. |  |\n\n1. Introduction  \nThe aviation industry has undergone a massive transformation as technology advanced and new digital capabilities have been developed. Intelligent solutions can enhance effectiveness, reduce costs, and boost productivity in the industrial sector. Advanced systems integrate a variety of cutting-edge technologies including automation, robotics, artificial intelligence (AI), machine learning, mixed reality, and the Internet of Things (IoT) (Menouar et al., 2017; Siegel et al., 2017; Zhu et al., 2018; Andreoni et al., 2021; Menezes et al., 2022). Digitization has changed the industry paradigm for smart aviation. The recently adopted innovative digital approaches promote efficiency, safety, and security in the operating process, and raise passenger satisfaction by better understanding their needs, preferences, and habits (Abeyratne, 2020; Molchanova, 2020; Xiong and Wang, 2022). Digitalisation has enhanced cooperation and communication among airlines, airports, and other aviation stakeholders (Kuisma, 2018). With machine learning and mixed reality, the aviation industry has the chance to transform aerospace engineering and enhance passenger experience.  \nMachine learning is crucial for digitalisation, interpreting and identifying features, patterns and trends in digital data to gain valuable insights and make informed decisions (Mahdavinejad et al., 2018; Adiet al., 2020; Brunton and Kutz, 2022). Machine learning provides powerful tools for creating efficient, reliable, and safe aircraft designs,  \nmanufacture, and training. Machine learning applications in the digital twin, aerospace design, aerospace production, aerospace verification and validation, and aerospace services has increased automation and streamlined processes in aviation industry (Mackall et al., 2002; Zhu et al., 2012; Allen, 2016; Brunton et al., 2020; Chinchanikar and Shaikh, 2022; Rodrigues et al., 2022; Xiong and Wang, 2022). Transformative machine learning has an effect on the manufacturing, automation, and data analysis in the aerospace industry with the use of digital modelling and simulation (Hey, 2009; Donoho, 2017; Brunton and Kutz, 2019). The data-rich aviation industry is poised to capitalize on the machine learning revolution. Machine learning optimises trans","cbCaicjUIqa6bT6a","https://ap.wps.com/l/cbCaicjUIqa6bT6a","pdf",3347403,1,16,"English","en",105,"# Introduction\n## Machine learning for digitalisation\n## Mixed reality and user experience","[{\"question\":\"How does the paper describe the role of machine learning in smart aviation digitalisation?\",\"answer\":\"It states that machine learning enables analysis of digital data to identify features, patterns, and trends, supporting informed decisions and enabling automation across aircraft design, manufacturing, verification, and services.\"},{\"question\":\"What passenger-experience capabilities does the paper associate with mixed reality technologies?\",\"answer\":\"It highlights autonomous systems, self-service systems, and data-visualization systems that can improve how passengers are understood and served, enhancing overall experience.\"},{\"question\":\"Which challenge areas does the paper consider for implementing smart aviation solutions?\",\"answer\":\"It discusses safety, environmental, technological, cost, security, capacity, and regulatory challenges, and points to potential solutions to improve future quality, reliability, and efficiency.\"}]","Machine learning and mixed reality for smart aviation: Applications and challenges | 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