Development and Implementation of Cost-effective Flight Simulator Technologies
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
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.
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 | 0.000 | 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