Hybrid Intelligence in Aerospace Science and Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".
Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 1982
Special Issue Editors
Interests: human-in-the-loop machine learning; collaborative hybrid intelligence for aircraft design; fault diagnosis; pattern recognition; machine learning and engineering applications
Special Issue Information
Dear Colleagues,
To promote the communication and development in the research domain of Aerospace Science and Engineering, the “Applied Sciences” launched a Special Issue on “Hybrid Intelligence in Aerospace Science and Engineering”. Hybrid intelligence is defined as the combination of human and machine intelligent, augmenting their capacities and achieving goals that were unreachable by either humans or machines. With the advancement of artificial intelligence technology, intelligent methods and devices have been extensively employed in all facets of aircraft design, manufacturing, and control, etc. However, the accessibility of intelligent systems that are both computationally efficient and reliable in decision-making, while also being independent of expensive equipment, remains an open question. One of the significant ways to track this question is through the human-assisted intelligent system. The main hybrid intelligent research is as follows: how to build adaptive intelligent systems that augment rather than replace human intelligence, leverage their strengths, and compensate for their weaknesses while take into account domain guidelines, system safety, efficiency and human load considerations.
The Special Issue of “Hybrid Intelligence in Aerospace Science and Engineering” is aimed at aerospace-science-related aircraft design and manufacturing, human-machine interaction, human-machine shared control and the evaluation methods of HI systems. The areas of interest include, but are not limited to:
- Collaborative Hybrid Intelligence for aircraft design;
- Collaborative Hybrid Intelligence for aircraft manufacturing;
- Computer aided design;
- Machine learning for fluid mechanics;
- Human-in-the-loop machine learning;
- Human-machine interaction technologies;
- Decision fusion;
- Shared control;
- Continue Learning;
- Explainable Hybrid Intelligence;
- Uncertainty in Hybrid Intelligence
Prof. Dr. Ke Li
Prof. Dr. Dongsheng Wen
Guest Editors
Manuscript Submission Information
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Keywords
- collaborative hybrid intelligence for aircraft design
- collaborative hybrid intelligence for aircraft manufacturing
- human-in-the-loop machine learning
- human-machine interaction technologies
- explainable hybrid intelligence
- uncertainty in hybrid intelligence
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