Smart Integrated Optical Rotation Sensor Incorporating a Fly-by-Wire Control System
Bibliographic record
Abstract
In this work, a first prototype smart optical rotary sensor is demonstrated for fly-by-wire applications to detect cockpit inceptors or flight control surface movement. A rotary encoder, optical setup, electronic circuits, and a microcontroller, with built-in analog-to-digital converters, are packed into a single housing with dimensions of 80 mm × 80 mm × 50 mm to propose a smart sensor. To simulate cockpit inceptors or flight control surface displacement, an actuator is rotated by a computer-controlled driver and the output voltages are sent to the computer using a universal serial bus. Signal processing is done in two steps: software filtering followed by optical powers extraction from the measured digitized voltages. Experimental results show that the sensor has the sensitivity of 17.5 mW · W-1/° and an accuracy of 0.5% over the full range of 180°. Moreover, we test the sensor's reliability by examining the sensor response while varying the input power of the light source and we demonstrate that the sensor is highly reliable, a vital requirement for avionic applications. The sensor not only meets the requirements for avionic applications but it is also smart which leads to less maintenance, less electromagnetic interference (EMI) (instead of ~100 twisted wire pairs, four buses are used which are more EMI resistant), and significant weight reduction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".