Space-time reconstruction of a moving acoustic loading on a membrane by coupling the force analysis technique and full-field non-contact vibration measurements
Bibliographic record
Abstract
Identifying acoustical and mechanical loadings on structures is a common problem in acoustics and vibration analysis and stationary loadings are mostly considered on plate-like structures. This work describes a proof of concept for reconstructing the trajectory of an acoustic source moving in front of a membrane. Compared with works focusing on precisely identifying a loading’s amplitude at a given location, the objective is to reconstruct the loading’s trajectory–Qualitative loading identification is sought rather than quantitative. The force analysis technique is used to recover a space-time varying loading on a structure, starting from time-resolved full-field non-contact vibration measurements conducted on a circular membrane. At the same time, a compact and tonal sound source is used to draw freehand shapes in front of the membrane. The loading trajectory, therefore, contains information that was “acoustically written”. Simple hand gestures that correspond to the drawing of a Greek letter (Σ), a capital letter (P), two shapes (♡, ⋆), and a 3-letter word (net) are recovered using the proposed procedure. The effect of various parameters on the reconstructed information is studied. Perspectives in terms of possible research areas and applications are finally discussed. These perspectives include, for example, the use of membranes to help reconstruct complex and space-time-varying loadings or even applications in musical acoustics on membranophones.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".