Coupling the force analysis technique and full-field vibration measurements for the identification of a time-space-varying sound pressure loading on a membrane
Bibliographic record
Abstract
This work is grounded on the force analysis technique, an identification method that directly uses a structure's equation of motion to formulate an inverse problem, explicitly identifying the force causing the structure's motion. Prior research employing this technique has been predominantly conducted in the frequency domain and was limited to stationary, mechanical excitations using physical sensor arrays such as accelerometers. The objective of this research was mostly quantitative (amplitude, location), while the proposed approach is rather qualitative identification. Indeed and by combining the force analysis technique with full-field and non-contact vibration measurements conducted on a system, here a membrane, this communication describes a proof-of-concept for the identification of a time-space-varying sound pressure loading. A compact and tonal sound source is used to draw freehand shapes against the membrane surface, and the objective is to follow/reconstruct the trajectory followed by this source. Results are provided for different drawn shapes or letters, and the effect of mechanical or calculation parameters on the reconstructed information is studied. Finally, potential research directions are discussed and fed by preliminary measurements on a percussion instrument.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".