Characterization of Piezoelectric Accelerometers Beyond the Nominal Frequency Range
Bibliographic record
Abstract
Abstract Ultrasonic testing is preferred widely for the evaluation of material properties. However, ultrasonic signals are highly affected by the characteristics of the piezoelectric transducers employed for the measurements. Since ultrasonic transducers are mostly used to measure the travel time of waves only, their non-flat frequency response does not affect the results. On the other hand, the analyses based on the full-waveform provide more reliable results, as thousands of additional data points in the measured signals are taken into account to determine the material properties (e.g., material damping). To perform such analyses, however, the transfer function of the transducer is required in order to normalize the recorded signals. In this paper, a new calibration approach was proposed to determine the transfer function of the piezo-electric accelerometers that are used beyond their nominal frequency range. A nano-laser vibrometer was utilized to measure the high frequency ultrasonic vibrations generated by the piezoelectric transmitter employed as the excitation source for the accelerometers. The transfer functions of two accelerometers with different nominal frequency ranges were determined for frequencies between 20 and 70 kHz, which were then used to capture the ultrasonic waves on a lightly cemented-sand medium. The original signals and the ones corrected by eliminating the effect of the transfer functions were processed to determine the material damping of the medium. Improvement in the accuracy of the material damping is achieved with the corrected signals compared to the uncorrected ones.
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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 | 0.000 |
| 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".