Pressure mapping system based on guided waves reflection
Bibliographic record
Abstract
In this paper, guided wave interaction is used to develop a pressure mapping system for medical and touch-screen applications. The principle is based on interaction of guided waves in the presence of an added local mass and in the presence of a local pressure. For this purpose, piezoceramics are used for injecting guided waves into a thin structure and to measure the reflected waves due to the presence of an added mass or pressure. SHM imaging algorithms, based on time-of-flight (EUSR) or correlation (Excitelet), are implemented in order to obtain cartography of the reflections and deduce the presence, localization, and intensity of local contact spots. Analytical and numerical models are first derived to assess the critical parameters in order to maximize the reflection of guided waves (first order modes A0 and S0). It is shown that the sensitivity of the guided waves with respect to an added mass and pressure is highly related to the Young’s modulus of the host structure. Validation on a 0.5 mm thick plane aluminum plate prototype is addressed using 4 sensor/actuator pairs. It is observed that imaging of single pressure spot and multiple or extended pressure spots can be achieved using S0 mode around 300 kHz with a resolution of 0.5 mm.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".