Vibration Rectification and Thermal Disturbances in Ultra Precision Inertial Sensors
Bibliographic record
Abstract
Advanced inertial MEMS sensors facilitate achieving superb precision and resolution in measuring translational and rotational displacements, down to femtometers and milli-arcseconds. At present such performance is possible only in measurements of a very short duration, typically below 1 second. As this duration increases, the precision rapidly deteriorates. However, experimental accelerometers indicate the possibility of measurements with sub-micron precision for up to 30 seconds. For longer measurements, e.g., up to 5 minutes, the errors increase. However they still remain below 100 μm. The main cause of errors is a strong amplification of low frequency disturbances and distortions introduced by the sensors. It occurs when acceleration and angular rate are converted to the translational and angular displacement, i.e., during the integration. Thus, the key to maximizing the performance of inertial displacement sensors is a reduction of their low frequency disturbances. In the top tier sensors the key components of the disturbances include (1) the inherent thermodynamic and electrical noise, (2) chaotic mechanical phenomena, and (3) nonlinear distortion. The presented research is concerned with these three areas. It focuses on the identification and correction of errors which deteriorate a stability of the sensors’ bias, in particular on the vibration rectification error (VRE) and temperature variations due to the actuation in servo accelerometers. The investigated accelerometers are high performance sensors, digital and analog, whose total harmonic distortion is in the range from 1% down to a few parts-per-million (i.e., <0.001%). The objective is to develop on-line corrective filters capable of reducing the overall low frequency distortion below 0.00001%.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".