Identification and cancellation of disturbances having two close sinusoidal components
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers the problem of identifying and cancelling disturbances which contain two sinusoidal components with close frequencies. Previously, Guo and Bodson, 2005, showed that signals composed of two separate but close sinusoids could be represented by a single sinusoid with periodically varying magnitude and phase. Since the magnitude varies periodically, it can be precisely identified and this information can be used to more precisely identify the components of the original signal and more accurately cancel this signal. The base periodic-signal identification algorithm used in this work is the adaptive internal model principle controller rather than the phase locked loop based algorithm used in Guo and Bodson's work. The results are compared with those from Guo and Bodson's algorithm. Other approaches based on the internal model are presented and compared in this paper also.
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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