A Practical Approach to the Phase and Amplitude Error Estimation for Pseudodynamic (PSD) Testing
Bibliographic record
Abstract
In PSD testing, measured signals from the experiment are used in command generation on the fly, thus making this method prone to error propagation. One way to assess the accuracy of the real-time (or fast) PSD test results is to assess the accuracy of the tracking of command displacements imposed by the hydraulic actuators. This paper introduces indicators that can be used to uncouple and quantify actuator lag/lead (i.e., phase errors) and undershoot/overshoot (i.e., amplitude errors) errors that occur while imposing the command displacements dynamically in real-time. These closed-form indicators provide monitors that are not experiment-specific and are simple to use. They use uncomplicated mathematical functions, which makes them suitable candidates for online implementation with the potential for incorporation into the control law to improve the actuator control. The procedure and the associated theoretical background are explained for each step and then the properties of the indicators are examined through several predefined command and measured signals with known characteristics. In addition, the performance of the indicators is evaluated by comparing the results with those revealed by the previous indicators.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".