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Record W2334236368 · doi:10.2514/6.2001-4306

G-cueing system tuning optimization

2001· article· en· W2334236368 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

VenueAIAA Modeling and Simulation Technologies Conference and Exhibit · 2001
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsProcess (computing)Computer scienceActuatorSet (abstract data type)Constraint (computer-aided design)SimulationControl engineeringArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

This paper describes a study carried out on a CAE gcueing system. A g-seat is generally regarded as an economical and practical solution to motion cueing for highly maneuverable aircraft such as military fighter trainers. An inherent difficulty in building a g-seat is to maintain the mechanical profile of the cueing seat close to that in the actual aircraft. This constraint limits the travel of actuators and hence the amount of cueing that can be provided to a pilot. The set up and tuning of a gcueing system is also typically time-consuming. This study proposes a way to optimize the use of available actuator travel for g-cueing; it discusses the goals of g-seat tuning and proposes a strategy to optimize the process and produce an appropriate cueing setting for flight training. The study considers different tunings for pilot training in a level-seven flight training device fitted with an eight-channel visual system. The results from the study show the proposed methods are useful for the tuning of a g-cueing system.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.595
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
GPT teacher head0.222
Teacher spread0.201 · 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