Gravity-Assisted, Passive Cancellation of Disturbances for Inertial Sensors
Bibliographic record
Abstract
Continuing enhancements in Microsystem Technologies facilitate the development of inertial sensors — accelerometers and gyroscopes — of unprecedented performance to cost ratio and broaden the frontiers of their application. Of particular interest, because of their immunity to ambient disturbances, are sensors equipped with high resolution Electro-Mechanical ΣΔ converters and with a high speed, digital serial signal transmission. The digital circuitry of these sensors reaches the accuracy of 0.02 parts-per-million (ppm). However, the analogue transducers of measured physical quantities into electrical signals inside of the even best inertial sensors are prone to inherent imperfections of analog systems such as nonlinearity, cross-sensitivity, or noise. The best accuracy of these transducers is about two orders of magnitude worse than that of the electrical circuitry. The overall accuracy can be greatly improved by using corrective filters that cancel the effects of imperfections in the analogue transducers. The effectiveness of these filters hinges upon the accuracy of identifying comprehensive models of the analogue transducers. Ambient disturbances, in particular mechanical vibrations, greatly deteriorate the accuracy of identification. Their impact can be attenuated to some extent by using vibration isolation platforms. The effectiveness of attenuation is usually good at the frequencies above 5–10 Hz, however it is poor at low frequencies. This poor attenuation is a significant disadvantage since the low frequency phenomena in inertial sensors have pronounced impact on their suitability for a broad class of applications (e.g., navigation). The presented research focuses on the design of a passive vibration isolation device in which horizontal movement is coupled to tilt in a way that a component of the gravity perceived by the tested inertial sensor effectively cancels out the horizontal acceleration coming from the ambient vibrations.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".