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Record W2043008477 · doi:10.2514/2.5029

Employing Soft Computing Techniques to Study Stability and Control in Aircraft Design

2003· article· en· W2043008477 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Guidance Control and Dynamics · 2003
Typearticle
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStability (learning theory)Computer scienceAerospace engineeringSoft landingStability derivativesFlight control surfacesControl (management)Soft computingControl engineeringLongitudinal static stabilityControl theory (sociology)AerodynamicsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.232
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it