From the Editors of the Special Issue on Current Applications and Innovations of Artificial Intelligence and Machine Learning in Aerospace Article Swipe
Sabah Mohammed
,
Ruay-Shiung Chang
,
Carlos Ramos
,
Tai-hoon Kim
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/maes.2022.3170740
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/maes.2022.3170740
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/maes.2022.3170740
- https://ieeexplore.ieee.org/ielx7/62/9789431/09789432.pdf
- OA Status
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4281689751Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/maes.2022.3170740Digital Object Identifier
- Title
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From the Editors of the Special Issue on Current Applications and Innovations of Artificial Intelligence and Machine Learning in AerospaceWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-01Full publication date if available
- Authors
-
Sabah Mohammed, Ruay-Shiung Chang, Carlos Ramos, Tai-hoon KimList of authors in order
- Landing page
-
https://doi.org/10.1109/maes.2022.3170740Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/62/9789431/09789432.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/62/9789431/09789432.pdfDirect OA link when available
- Concepts
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Aerospace, Avionics, Aviation, Dependability, Computer science, Artificial intelligence, Identification (biology), Air traffic management, Air traffic control, Systems engineering, Engineering, Aeronautics, Software engineering, Biology, Botany, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.flight | 172 |
| abstract_inverted_index.global | 167 |
| abstract_inverted_index.health | 168 |
| abstract_inverted_index.planes | 163 |
| abstract_inverted_index.remote | 78 |
| abstract_inverted_index.safety | 120 |
| abstract_inverted_index.speed, | 116 |
| abstract_inverted_index.threat | 76 |
| abstract_inverted_index.complex | 127 |
| abstract_inverted_index.current | 8 |
| abstract_inverted_index.enhance | 115 |
| abstract_inverted_index.machine | 16, 24 |
| abstract_inverted_index.provide | 55 |
| abstract_inverted_index.respond | 148 |
| abstract_inverted_index.safety, | 47 |
| abstract_inverted_index.safety. | 199 |
| abstract_inverted_index.section | 5 |
| abstract_inverted_index.sectors | 44 |
| abstract_inverted_index.smaller | 162 |
| abstract_inverted_index.special | 4 |
| abstract_inverted_index.systems | 58, 97 |
| abstract_inverted_index.traffic | 70 |
| abstract_inverted_index.various | 37, 56 |
| abstract_inverted_index.Recently | 137 |
| abstract_inverted_index.advanced | 138 |
| abstract_inverted_index.aircraft | 158 |
| abstract_inverted_index.airlines | 153 |
| abstract_inverted_index.articles | 1 |
| abstract_inverted_index.aviation | 143, 185 |
| abstract_inverted_index.customer | 50 |
| abstract_inverted_index.industry | 144 |
| abstract_inverted_index.learning | 17, 25 |
| abstract_inverted_index.loyalty. | 51 |
| abstract_inverted_index.methods. | 111 |
| abstract_inverted_index.provided | 141 |
| abstract_inverted_index.sensing, | 79 |
| abstract_inverted_index.systems. | 87 |
| abstract_inverted_index.vehicles | 84 |
| abstract_inverted_index.aerospace | 33 |
| abstract_inverted_index.analytics | 140 |
| abstract_inverted_index.automated | 57 |
| abstract_inverted_index.aviation, | 40, 61 |
| abstract_inverted_index.concerned | 90 |
| abstract_inverted_index.employing | 107 |
| abstract_inverted_index.important | 30 |
| abstract_inverted_index.military, | 38 |
| abstract_inverted_index.passenger | 74 |
| abstract_inverted_index.training, | 73 |
| abstract_inverted_index.workload, | 118 |
| abstract_inverted_index.Artificial | 20 |
| abstract_inverted_index.aerospace, | 187 |
| abstract_inverted_index.aerospace. | 19 |
| abstract_inverted_index.algorithms | 92, 104 |
| abstract_inverted_index.artificial | 13 |
| abstract_inverted_index.autonomous | 82, 131 |
| abstract_inverted_index.commercial | 39 |
| abstract_inverted_index.conclusion | 190 |
| abstract_inverted_index.innovative | 179 |
| abstract_inverted_index.navigation | 133 |
| abstract_inverted_index.schedules. | 173 |
| abstract_inverted_index.techniques | 94, 106 |
| abstract_inverted_index.contributes | 53 |
| abstract_inverted_index.ecosystems. | 136 |
| abstract_inverted_index.efficiency, | 65, 117 |
| abstract_inverted_index.exploration | 43 |
| abstract_inverted_index.innovations | 11 |
| abstract_inverted_index.integrating | 124 |
| abstract_inverted_index.management, | 71 |
| abstract_inverted_index.overarching | 189 |
| abstract_inverted_index.statistical | 110 |
| abstract_inverted_index.visionbased | 132 |
| abstract_inverted_index.“learn” | 99 |
| abstract_inverted_index.applications | 9, 34 |
| abstract_inverted_index.increasingly | 29 |
| abstract_inverted_index.intelligence | 14, 21 |
| abstract_inverted_index.maintenance, | 67 |
| abstract_inverted_index.restrictions | 169 |
| abstract_inverted_index.“reason” | 101 |
| abstract_inverted_index.computational | 108 |
| abstract_inverted_index.significantly | 114 |
| abstract_inverted_index.technologies, | 128 |
| abstract_inverted_index.dependability, | 48 |
| abstract_inverted_index.implementation | 194 |
| abstract_inverted_index.identification, | 75, 77 |
| cited_by_percentile_year | |
| countries_distinct_count | 4 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.04949659 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |