An Analog Circuit Technique to Improve a Geophone Frequency Response for Application as Vibration Sensors
Bibliographic record
Abstract
Vibration sensors find use in monitoring and measuring vibrations of buildings, bridges and in seismological sciences. Geophones are one of the commonly used sensors for such applications. However, geophones have a natural frequency response like that of a high-pass filter. In the past, several innovations have been introduced to extend the -3 dB corner frequency of the geophone to capture the natural frequencies of a building around 1 Hz. These involved modifying the physical construction of the geophone, and/or introducing digital signal conditioning which is cost intensive. We investigated several analog circuit techniques such that the overall electrical response of the geophone approximates to a low-pass filter response. In one approach the device is followed by a cascade of an ideal integrator and a lossy integrator. This eliminates the zeros of the device while preserving the natural low-frequency pole of the device. It is desirable to create a low-frequency pole independent of the natural pole of the geophone. In order to achieve this goal we used multi-loop feedback method which affords to a low-pass characteristic where the pole frequency becomes different from the natural pole frequency of the geophone. In the following theoretical foundations for two techniques are presented. Validity of the multi-loop feedback technique has been established by numerical simulations and verified by lab-bench experiments.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".