A New Strategy Optimization Method for Vehicle Active Noise Control Based on the Genetic Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The control strategy design of vehicle active noise control (ANC) relies too much on experiment experience, which costs a lot to gather mass data and the experimental results lack representation. To solve these problems, a new control strategy optimization method based on the genetic algorithm is proposed. First, a vehicle cabin sound field simulation model is built by sound transfer function. Based on the filtered-X Least Mean Squares (FX-LMS) algorithm and the vehicle cabin sound field simulation model, a vehicle ANC simulation model is proposed and verified by a vehicle field test. Furthermore, the genetic algorithm is used as a strategy optimization tool to optimize an ANC control strategy parameter set based on the vehicle ANC simulation model. The optimized results provide a reference for the ANC control strategy design of the vehicle.</div></div>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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 it