Noise Cancellation Technique for Automotive Intake Noise Using A Manifold Bridging Technique
Bibliographic record
Abstract
<div class="htmlview paragraph">Due to considerable efforts of automobile manufacturers to attenuate various noise sources within the passenger compartment, other sources, including induction noise have become more noticeable. The present study investigates the feasibility of using a non-conventional noise cancellation technique to improve the acoustic performance of an automotive induction system by using acoustic energy derived from the exhaust manifold as the dynamic noise source to cancel intake noise.</div> <div class="htmlview paragraph">The validity of this technique was first investigated analytically using a computational engine simulation software program. Using these results, a physical model of the bridge was installed and tested on a motored engine. The realized attenuation of the intake noise was evaluated using conventional FFT analysis techniques as well as psychoacoustic metrics including loudness, sharpness, roughness and fluctuation strength.</div> <div class="htmlview paragraph">While good correlation was found between the numerical and experimental results, additional work is recommended before implementation of a manifold bridge can be considered commercially viable.</div>
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".