Graph-Based Aircraft Control Classifier Tuning With Validation Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Developing aircraft control systems that are reliable and efficient, especially in scenarios outside normal operating conditions, is challenging due to the complexity and time-consuming nature of traditional development methods, and existing AI techniques may not be fully trusted until proven dependable.
Innovation Solution
A test system that modifies a graph-based trained classifier by generating a custom loss score based on differences between validation and control data, allowing for refinement and increased reliability of the classifier's outputs, thereby enhancing the aircraft control system's performance and trustworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional development methods are used to develop aircraft control systems, then reliability can be achieved through thorough testing and validation, but the development time and complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by generating validation data and defining loss functions before the classifier is deployed. The system pre-generates candidate paths and validates them against graph models, creating a foundation that guides subsequent classifier training and refinement without requiring extensive post-deployment testing
Solution Approach 2:
The patent implements feedback through the custom loss function that compares classifier outputs against pre-generated validation data. The loss score provides continuous feedback during training, allowing the system to iteratively improve classifier reliability by adjusting weights based on performance differences between predicted and valid paths
2Productivity
If AI techniques are used to decrease development time, then productivity increases, but reliability and trustworthiness of the system decrease
Solution Approach 1:
The patent introduces an intermediary validation layer between the AI classifier and the final control decisions. The graph model and pre-generated validation data act as intermediaries that constrain and guide the AI's output, ensuring that even快速-generated classifications meet reliability requirements before being applied to aircraft control
Solution Approach 2:
The patent uses parameter changes by adjusting the loss function weights and thresholds to balance speed and reliability. By modifying the parameters of the loss calculation (such as weighting different types of errors differently), the system can tune the classifier to achieve acceptable reliability levels while maintaining the efficiency benefits of AI-driven development
3Reliability
If the classifier is heavily trained to reduce incorrect outputs, then reliability increases, but the training time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-generating comprehensive validation data and candidate paths before classifier training begins. This pre-computation creates a ready-made benchmark that accelerates the training process, as the classifier can immediately learn from clearly defined correct answers rather than requiring extensive iterative exploration
Solution Approach 2:
The patent implements partial action by focusing training efforts on the most critical error types identified through the loss function. Rather than uniformly training on all possible scenarios, the system concentrates computational resources on correcting the most impactful classification errors, achieving sufficient reliability with less total training time
4Reliability
If the classifier is trained on all possible scenarios to ensure consistency, then reliability improves, but the complexity and computational load increase
Solution Approach 1:
The patent applies segmentation by dividing the scenario space into manageable segments represented as discrete graph nodes and edges. The graph model breaks down complex aircraft control scenarios into structured, hierarchical components that can be validated and trained upon independently, then combined to ensure overall consistency without requiring exhaustive training on every possible scenario combination
Data Source
Figure 1
Figure 2
Figure 3
AI summary
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.