An improved real-time rocking motion monitoring system
Bibliographic record
Abstract
Most primary accelerometer calibrations, or absolute calibrations, are currently performed using laser interferometry under the guideline of the international standard (ISO 16063-11, 1999). The laser interferometric method measures the displacement amplitude of a moving surface with an accelerometer mounted. To achieve high accuracy in the measurement of acceleration it requires a poston-like and distortion-free vibration of the accelerometer at calibration frequencies and amplitudes. Unfortunately, vibration shakers usually present hgih levels of rocking motion. Rocking motions is an unwanted phenomenon exhibited by vibration shaker. It is usually manifested in oscillatory back and forth, side to side, or rotatory movements. It causes the measurements of accelerometer sensitivity deviating from each other for up to a few percent. To reduce the effect of rocking motion, measurements may be performed at multiple incidence points equally spaced along the border of the accelerometer on the mounting surface and the corresponding average is then estimated. Recently, mechanical filters have been used to decouple the rocking motion from vibration shaker to acceleromteter. to obtain the best results, the filter should be optimized for a specific type of reference accelerometer. For this purpose, a real-time rocking motion monitoring system was developed at the National Research Council Canada. This paper describes the improvements of the system since it was developed and reported and presents the measurement results of rocking motion for different conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".