Phase and amplitude error indices for error quantification in pseudodynamic testing
Bibliographic record
Abstract
SUMMARY Real‐time pseudodynamic (PSD) and hybrid PSD testing methods are displacement controlled experimental techniques that are used to investigate the dynamic behaviour of complex and load rate‐dependent structures. Because the imposed command displacements are not predefined but generated during the test based on measured feedback, these methods are inherently prone to error propagation, which can affect the accuracy and even the stability of the entire experiment. As a result, to have these experimental methods as reliable tools, the accuracy of the test results needs to be assessed by carefully monitoring, and if possible, quantifying the errors involved. In this paper, phase and amplitude error indices (PAEI) are introduced to identify the experimental errors through uncoupled closed‐form equations. Unlike the indicators that have been previously introduced in the literature for error identification purposes, PAEI do not use test setup specific parameters in their formulation, and can quantify the errors independent of the amplitude of the command displacements. As such, PAEI can be used as standard tools for assessing the quality of the experiments performed in different laboratories or under different conditions. Additionally, because they can quantify the error, when implemented online, PAEI have the potential to be incorporated in the control law and thereby improve the actuator control during the tests. The formulation and implementation of PAEI are provided in this paper. The enhanced performance of the proposed indices is demonstrated by processing several different measured and command signals using PAEI and comparing the results with those revealed by the previous indicators. Copyright © 2011 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".