A Novel Air Traffic Management and Control Methodology using Fault-Tolerant Autoencoder and P2P Blockchain Application on the UAS-S4 Ehécatl
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2190.vid This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm is designed for Aircraft Trajectory Prediction (ATP) based on Autoencoder architecture. The Autoencoder is used due to its excellent fault-tolerant ability when input data provided by the GPS is deficient. After conflict detection, P2P Blockchain is utilized for securely decentralized decision-making. The meta-controller composed of the Autoencoder and P2P blockchain performed the ATMC task very well. The validation studies were done relying on a comprehensive database of trajectories constructed using our UAS-S4 Ehécatl. The ATP accuracy was evaluated for a variety of data failures, and the performance index confirmed the Autoencoder’s excellent efficiency. Aircraft were considered in several local encounter scenarios, and their trajectories were securely managed and controlled using our designed Smart Contract developed on the Ethereum platform. Toward decentralized processing, the edge computing approach improved the P2P Blockchain performance in terms of computational complexity and processing time in real-time operations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".