Development and Implementation of Cost-effective Flight Simulator Technologies
Bibliographic record
Abstract
Low cost visual, audio, vibration, and control loading cueing technologies were implemented on an initially-obsolete flight simulation training device by Vector Training Systems.This resulted in the Carleton University Redeveloped Vector Simulator (CURVS).X-Plane 10 was used as the new simulation environment and appropriate data acquisition hardware and software was used to control mechanical instruments and pilot controls.An ultra-wide-angle triple-channel HD cylindrical projection system was built to replace the original low resolution (800 x 600 pixels) single-channel system.The screen was constructed out of steel tubing and PVC screen fabric.The images from three projectors were blended together to produce a seamless, asymmetric 220 • field of view offset to the pilot's side, an innovative feature which gives superior situational awareness to a student pilot practising landing manoeuvres.Vibration cueing was implemented with a seat-mounted vibration transducer driven by a custom engine audio recording acquired from a Cessna 172 aircraft.Control loading was also implemented.This feature increases the realism of the pilot experience by allowing the pilot to become accustomed to feeling the resistive forces from the controls during various manoeuvres.Test subjects noted very satisfactory experiences with CURVS.Control loading and ultra-wide-angle projection systems are invaluable elements to flight training, and greatly increase transfer of training between the simulator and an actual aircraft.However, these technologies are typically only available on expensive, high-end simulators.The innovative, cost effective control-loading and projection technologies implemented in this project will help bring these critical features within reach of a much greater body of student pilots, and enable the creation of more realistic, cost effective training devices in general.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".