Active Control of Structure-Borne Road Noise Based on the Separation of Front and Rear Structural Road Noise Related Dynamics
Bibliographic record
Abstract
Axle forces from tire-road interaction can excite different structural resonances of the vehicle hence a high number of sensors is required for observing and separating all the vibrations dynamics that are coherent with the cabin noise. Feed-forward road noise control strategies adopted so far rely mainly on capturing these dynamics and thus the number of sensors constitutes one major limitation of this approach. Therefore there is a necessity for reducing the number of sensors without degrading the performance of an ANC system. In the past coherence function analysis has been found to be a useful tool for optimizing the sensor location. In this case coherence function mapping was performed between an array of vibration sensors and the headrest microphones in order to identify the locations on the structure that are highly correlated with road noise bands in the compartment. A vehicle with an advanced suspension system was used for applying the method and defining some locations as reference signals for feed-forward active road noise control. Three different real-time control experiments were performed with structure-borne road noise simulated by applying broad band random forces to tires through shaker transducers. A single reference feed-forward adaptive controller evaluated the signals from each sensor location with simulated road noise excitation applied to: front wheels only, rear wheels only and whole vehicle. This way it is demonstrated that the control can be focused at specific road noise bands with a low number of sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".