From the Editors of the Special Issue on Current Applications and Innovations of Artificial Intelligence and Machine Learning in Aerospace

被引:0
|
作者
Mohammed, Sabah [1 ]
Chang, Ruay-Shiung [2 ]
Ramos, Carlos [3 ]
Kim, Tai-Hoon [4 ]
机构
[1] Lakehead Univ, Thunder Bay, ON P7B 5E1, Canada
[2] Natl Taipei Univ Business, Taipei 100, Taiwan
[3] Polytech Porto, P-4200465 Porto, Portugal
[4] Univ Tasmania, Hobart, Tas 7005, Australia
关键词
Special issues and sections; Aerospace engineering; Artificial intelligence; Machine learning; Training data; Biological neural networks; Autonomous aerial vehicles; Artificial neural networks; Aerospace safety;
D O I
10.1109/MAES.2022.3170740
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The articles in this special section focus on current applications and innovations of artificial intelligence and machine learning in aerospace. Artificial intelligence (AI) and machine learning (ML) play an increasingly important role in aerospace applications and serve various military, commercial aviation, and space exploration sectors to ensure safety, dependability, and customer loyalty. AI/ML contributes to provide various automated systems used in aviation, such as fuel efficiency, smart maintenance, smart air traffic management, pilot training, passenger identification, threat identification, remote sensing, and fully autonomous aerial vehicles among other systems. AI/ML is concerned with algorithms and techniques that allow systems to "learn" and "reason" based on algorithms and techniques employing computational and statistical methods. It can significantly enhance speed, efficiency, workload, and safety to enable the integrating of more complex technologies, such as autonomous visionbased navigation and data ecosystems. Recently advanced data analytics provided the aviation industry a way to respond to COVID and advise airlines on when to swap aircraft for bigger or smaller planes and how the global health restrictions may change flight schedules. While there are many other innovative use cases of AI/ML in aviation and aerospace, the overarching conclusion is that the implementation must be driven by safety.
引用
收藏
页码:4 / 5
页数:2
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