Bibliographic record
Abstract
Developing an active noise control mechanism is an primary factor to improving passenger noise comfort within aircraft cabins. The purpose of this work is to develop intelligent sub-systems for an active noise control system, the three major systems of focus are head tracking system, speaker motion system, and integrated system. The three systems are individually assessed through established validation tests that evaluate the performance of the elements within the systems, the major components considered mainly correspond to the dynamic nature of the passengers and cabin environment. An AI-based head tracking system is tested for robustness through detailed accuracy tests, additionally, various cabin-based elements have been considered. The impact of the speaker motion system under dynamic head movements is developed through a data-driven approach, which creates a Zone Of Quiet map around the passenger's ears. The integrated system is developed through a hybrid model, that develops geometric relationships and an optimal control strategy that ensures a Zone Of Quiet exists around passenger's ears. Lastly, all the systems have been tested on real cabin environment, to attain a realistic understanding of the systems' performance which effectively builds an understanding of the overall active noise control system with the integration of the individual system mechanisms.
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How this classification was reachedexpand
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) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".