Machine learning use cases in Aerospace
Machine learning use cases in Aerospace
Machine learning improves cost and safety in the aircraft sector. Advanced aircraft maintenance is delivering increased air safety, while employee productivity is being reduced. AI also helped advance the aircraft sector by providing useful knowledge that would otherwise have been hard to get using traditional methods.
What is AI and machine learning?
AI, like so many technology buzzwords, can mean different things to different people. For us, an AI system is one that leverages software functions created through a machine learning process rather than through traditional programming. Data, rather than source code, is the critical element. The performance of an AI application is shaped by the data used to train the application.
Without going into detail on machine learning algorithms or approaches, which is beyond the scope of this paper, we can generalize that the power in AI comes from machine learning’s ability to model complex systems and environments far beyond what we can reasonably build in traditional software.
Imagine building a speech recognition system through traditional programming — having a function for every word or a case statement for every pronunciation or accent. It would take a staggering amount of time to cover even 10 percent of the English language. Machine learning models, on the other hand, have made short work of this task, to the point that robust and accurate systems can understand a full vocabulary from hundreds of languages and accents.
Machine learning and artificial intelligence play an increasingly important role in aerospace applications. This is particularly true for automated systems including space robotics and unmanned aerial vehicles, where a variety of technological opportunities have arisen, each requiring novel approaches and algorithms to its address corresponding technological challenges. Other applications include optimization algorithms in structural engineering for the design of fail-safe aerospace structures, as well as solving problems dealing with uncertainties in structural properties, unsteady aerodynamic loading and flow/flight control system parameters, to name a few.
How is AI used in autopilot?
In order to drive on their own, autonomous cars constantly interpret images from their sensors and machine vision cameras, then use that information to make decisions about what to do next. They use AI to understand and anticipate the next movements of cars, pedestrians, and cyclists.
The goal of this Research Topic is to illustrate applications of Machine Learning and Artificial Intelligence methods to problems in aerospace. Novel ML/AI algorithms and/or application of existing approaches to problems involving space robotics, UAV operations, flow and flight control, structural engineering, as well as other fields of aerospace engineering are examined. Military applications are out of scope for this collection.
What is the current use of AI in aviation?
AI tools help aviation industry to increase operational efficiency in customer relationship management with modern technologies, taking a fundamental approach to enhancing the customer feedback, suggestions and AI algorithms to identify real-time customer reaction on social media platforms.
Important Links
Comments
Post a Comment