Augmented Reality – Cockpit Display System In Real-Time Flight Simulation Environment
Bibliographic record
Abstract
Over recent years, hands-free Augmented Reality (AR) technologies are widely being used towards equipment manufacturing and maintenance services in various engineering platforms. One of such applications discussed in this report is an Electronic Flight Instrument System (EFIS)/glass cockpit/Cockpit Display System (CDS), in AR, for real-time testing during the early designs of an aircraft’s flight deck. Two prototype designs, both built using the Buildwagon HoloLens Development Platform and tested on Microsoft HoloLens 1 headset using X-Plane 11 flight simulator for flight tests, are presented. For the presented project, several flight data, as well as any other simulation data, were sent to BinariesLid’s Buildwagon Collaboration Server in the form of messages using the Socket.IO library via the Internet. These messages were processed and used to redraw the EFIS on Buildwagon JavaScript Editor. This thesis presents the design methodology and performance benchmarks of the systems involved during the flight test, i.e. HoloLens 1 AR headset, Buildwagon Emulator, and the simulator system itself.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".