Identification of periodic signals with uncertain frequency
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a new algorithm to identify periodic signals with uncertain frequency. The approach is based on the behavior of an internal model in an error feedback system. As such, the signal is fed to a fictitious plant with a feedback controller. The feedback controller is based on a traditional controller in parallel with an internal model which identifies and cancels the periodic disturbances. Under ideal circumstances, the phase plot of the states of the internal model form an ellipse. The speed of rotation about this ellipse can be used to calculate the difference between the nominal frequency of the model and the true frequency of the periodic signal. An integral controller or a least-squares estimator can be used to drive this error to zero. Simulations demonstrate the validity of this approach with time-varying frequency, and the algorithm is then applied to some data collected from a spot welder that has been corrupted by a sinusoidal signal whose frequency is between 1 Hz and 5 KHz.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it