Combined Flight Management System and Flight Data Recorder for General Aviation using Tablet Computers
Bibliographic record
Abstract
The research is to create the necessary algorithms for a low-cost Flight Data Recorder with the possibility of improving functions on existing Flight Management Systems. Within this research, flight data recorders and flight management systems are considered together for low-cost applications as the flight data recorder could record the entire flight internally and subsequently provide the data through a flight management system. The flight management system could also send reports of the aircraft exceeding user-set maximum/minimum parameters. The algorithms can be applied to either a tablet computer or a stand-alone unit. The biggest issue faced with such a project is to determine airspeed without an aircraft modification that requires regulatory approval. This is accomplished by manipulating stability derivatives to computationally approximate airspeed with other known parameters. The other parameters are measured using gyroscopes, accelerometers and GPS. There is currently no low-cost Flight Data Recorder or Flight Management Systems that includes the crucial airspeed parameter. The addition of airspeed on an aftermarket flight management system leads to better performance calculations, indications of stall or near-stall attitudes and a maximum speed warning. The airspeed algorithms are tested experimentally through a purpose built unit for this research, and the unit may be used as a low-cost flight data recorder. With the proposed unit, accident or incident investigation can be significantly simplified or the investigators can use the data to validate theories gained by other means. The flight data recorder could lead to lower insurance costs and the flight management system could reduce other costs for General Aviation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".