Active structural intensity control using strain sensing
Bibliographic record
Abstract
An investigation of structural intensity control using strain sensing is presented in this paper. As opposed to previous work, the approach taken here does not assume waves propagating predominantly in one direction. Moreover, the instantaneous intensity is completely taken into account in the control algorithm, i.e., all the terms are considered in the real-time control process and, in particular, the evanescent waves are considered in this approach. A finite-difference approach using discrete PVDF strain sensors is used as the sensing scheme. A feedforward filtered-X LMS algorithm is adapted to this energy-based control problem, involving a nonpositive definite quadratic form in general. In this respect, the approach is limited to cases where the geometry is such that the intensity component will have the same sign for the control source and the primary disturbance. Experimental validation of the approach is conducted on a free–free beam covered with viscoelastic material. A comparison is made between classical acceleration control and structural intensity control and the performance of both approaches is presented. These results tend to indicate that using intensity control allows the error sensors to be placed closer to the control source and the primary disturbance, while preserving a good control performance.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".