18524250. AIRCRAFT CONTROL SYSTEMS simplified abstract (AIRBUS OPERATIONS LIMITED)
Contents
- 1 AIRCRAFT CONTROL SYSTEMS
AIRCRAFT CONTROL SYSTEMS
Organization Name
Inventor(s)
AIRCRAFT CONTROL SYSTEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18524250 titled 'AIRCRAFT CONTROL SYSTEMS
Simplified Explanation
The abstract describes a test system for modifying a graph-based trained classifier that controls an aircraft system based on a graph model. The system generates a custom loss score to modify the classifier based on scenario data, control data, and validation data.
- Test system for modifying a graph-based trained classifier for controlling an aircraft system
- Generates custom loss score based on scenario data, control data, and validation data
- Modifies classifier based on custom loss score to improve performance
Potential Applications
This technology could be applied in the aerospace industry for enhancing the performance of aircraft systems. It could also be used in autonomous vehicles for improving control algorithms.
Problems Solved
1. Enhances the accuracy and efficiency of control systems in aircraft. 2. Allows for real-time modifications to improve system performance based on data analysis.
Benefits
1. Improved safety and reliability in aircraft operations. 2. Enhanced control and decision-making capabilities in autonomous systems.
Potential Commercial Applications
Optimizing control systems in commercial aircraft for better performance and safety.
Possible Prior Art
There may be prior art related to machine learning algorithms used in control systems for aircraft or autonomous vehicles.
What are the potential limitations of this technology in real-world applications?
The technology may face challenges in adapting to complex and dynamic environments, requiring continuous updates and modifications to the classifier.
How does this technology compare to existing methods for modifying trained classifiers in control systems?
This technology offers a more dynamic and data-driven approach to modifying classifiers, allowing for real-time adjustments based on custom loss scores and scenario data, which may lead to improved system performance compared to traditional methods.
Original Abstract Submitted
A test system for modifying a graph-based trained classifier, configured to output control data for controlling the aircraft system according to a graph model representing the aircraft system. The test system is configured to obtain scenario data, control data, and validation data. The test system generates a custom loss score based on differences between the validation data and the control data and modifies the graph-based trained classifier based on the custom loss score, the scenario data, and the control data. A computer-implemented method for modifying the graph-based trained classifier and a storage medium comprising instructions to perform the method are also provided.
