A Test Setup for Evaluation of Harmonic Distortions in Precision Inertial Sensors
Bibliographic record
Abstract
Steadily improving performance of inertial sensors necessitates significant enhancement of the methods and equipment used for their evaluation. As the nonlinearity of sensors decreases and gets close to that of the exciters, new challenges arise. One of them, addressed in this research, is a superposition of errors caused by the nonlinearity of tested devices with nonlinear distortions of excitation employed for experimental evaluation. This can lead to a cancellation, at least partial, of the effects of both imperfections and underestimation of the actual distortions of the evaluated sensors. We implement and analyze several system architectures and evaluate components of applicable motion generation systems from the viewpoint of satisfying the relevant, often conflicting requirements posed by the evaluation of high performance inertial sensors. Robust mechanical integration of the guidance, actuation, and measurement functions emerges as a key factor for achieving the needed quality of generated test patterns. We find precision air bearing stages, such as ABL1500 series (Aerotech) most suitable for implementing the needed experimental setup. We propose an architecture with two reciprocating stages, implement and evaluate its core components, and illustrate its performance with experimental results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".