New helicopter model identification method based on flight test data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A helicopter model has been identified and validated for flight conditions defined by altitudes, speeds, loadings and centre of gravity positions. To identify the helicopter models, 2-3-1-1 multistep control inputs were performed by the pilot to excite all helicopter modes. Then, each estimated signal has to remain in tolerance margins defined by the Federal Aviation Administration. Three methods were used to observe the system outputs from its states: a fuzzy logic method, a linear method optimised with a neural network algorithm and a classical method. Because of random effects when gathering data, classical method did not give good enough results. The fuzzy logic method was not robust enough so that output plots showed peaks that could be felt by the pilot. Then, because the model could be implemented in a simulator for the pilot training, the pilot feedback is very useful in order to compare the reality with the results of the mathematical model. When the outputs are obtained from the measured state variables, the linear method gave the best results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it