Active sound control with smart foams using piezoelectric sensoriactuator
Bibliographic record
Abstract
Smart foam offers a lightweight, efficient noise control solution by combining the complimentary advantages of passive dissipation in the foam material with the actuation authority of the active piezoelectric component, under appropriate control input. This study aims to implement the sensoriactuator operation of the active piezoelectric component to obtain an alternate error signal from its mechanical strain response. This can potentially replace the use of far-field microphone error sensors in active noise control applications, and hence improve the compactness of the system. The piezoelectric sensoriactuator has been implemented with the hybrid analog–digital compensation of the quasi-stable feedthrough capacitance of the actuator using an adaptive algorithm. The mechanical charge response, thus obtained, has been minimized using an adaptive algorithm and its effect on the transmission loss has been studied. Additionally, it has also been utilized in absorption and transmission control problems, using a virtual sensing strategy, with the aim of obtaining the desired control performance by minimizing an estimated virtual error signal. The experimental results are supplemented with finite element simulation of the coupled noise control system, and it provides a significant insight into the physical problem of the realization of the smart foam sensoriactuator.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".