Experimental Studies on Active Noise Control Using Smart Structures
Bibliographic record
Abstract
The interior noise induced by vibration of surrounding structures can be attenuated by suppressing the vibration using surface-mounted smart elements. Compared with loudspeakers and microphones, piezoelectric elements are lightweight, compact, and easy to install. These features are very attractive for the noise control in vehicle cabins. Since the sound radiation abilities of different vibration modes are different, the control system has to be appropriately designed in such a way that the vibration components with strong radiation ability are controlled. In this paper, active noise control in a cylindrical shell with a floor partition using piezoelectric actuators (PZT) and Polyvinylidene fluoride (PVDF) error sensors is experimentally investigated. The control systems used in the experiment are designed using the GA-based method proposed in our previous work. To show the effectiveness of the optimal design, the performance of optimal systems is compared with that of non-optimal systems with random configurations. Experimental results are presented and analyzed both in the time and in the frequency domains. Results show that the control performance can be significantly improved through the optimal design using the GA-based method, even when controlled structures are irregular, where conventional design methods do not work well.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".