Identification of periodic signals with uncertain frequency
Bibliographic record
Abstract
Presents an algorithm to identify periodic signals with uncertain frequency. The approach is based on the behaviour of a notch filter 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 PI controller in parallel with an internal model which identifies and cancels the periodic disturbances. An additional integral controller then can be used to reduce this error to zero. The output of the notch filter will be the periodic component of the signal, while the input to the fictitious plant will be the non-periodic random component. An improvement to the basic feedback controller is also given to reduce this error by using continuous-time least-squares estimation. A second algorithm based on the Fourier transform (FT) technique is presented and used to confirm the performance of the feedback based algorithm. The frequency of the periodic signal can be found by an optimal algorithm which considers the windowing effect of FTs. Simulations demonstrate the validity of this approach and the algorithm is then applied to some data collected from a spot welder that has been corrupted by a sinusoidal signal whose frequency varies between 30 Hz and 1 KHz.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".