Reinforcement Learning Fuzzy Algorithm for Adaptive Cabin Management System With Application for Adaptive Interior Lighting
Bibliographic record
Abstract
<p>The purpose of this thesis was to design a framework for an adaptive cabin management system that is further explored through a study of light intensity. With the goal of passenger comfort, the system developed would adjust lighting to an average setting and further adapt to an individuals preference. A fuzzy inference system was implemented that utilizes DGI, the passengers age, chronotype and the activity on board to calculate a light intensity. A reinforcement system was then developed to tune the fuzzy inference system parameters (mean and output value K) utilizing a lighting override from the passenger. A cabin mock up was then setup to observe the correctness and e▯ectiveness of the system developed. Overall, the system designed tuned accurately and e▯ectively in all case scenarios tested with varying learning rates. This thesis was concluded with discussions of future work that could further improve the implementations within a cabin.</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".