Employing Soft Computing Techniques to Study Stability and Control in Aircraft Design
Bibliographic record
Abstract
F-14 Simulation Results The scenario used in the previous work of Fialho et al. was chosen to test the proposedcontrollerdesign.Figure 2 shows the two simulatedmaneuvers. Two pairs of 1-s, 1-in. (2.54-cm) stick inputs to the left and then to the right are applied at 1 and 5 s followed by two pairs of pedal inputs at 11 and 15 s. The comparison of the roll rate response to the stick input with the “ideal” closed-loop response in Fig. 2 shows tracking performance slightly superior to the already very good result shown in Ref. 1. The very small peak sideslip error of 0.06 deg is far better than the 0.8-deg error obtained in Ref. 1. The response to pedal inputs achieved almost perfect tracking of the sideslip angle and a limited residual roll rate responseof only 0.35 deg/s. This is much better than the 1 deg/s roll rate error presented in Ref. 1.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".