Robust and Frequency-Adaptive Measurement of Peak Value
Bibliographic record
Abstract
A new approach for measuring the peak value of the fundamental component of a distorted sinusoidal signal for power system applications is presented. The method is applicable to single-phase as well as three-phase systems. While maintaining structural simplicity, the proposed approach is highly robust with respect to noise and distortion due to disturbances and unbalanced conditions of the system. The method is also highly tolerant of uncertainties in the setting of its internal parameters. The salient feature of the proposed approach is its capability of adapting to the variations in the center frequency of the input signal. The method is suitable for environments that frequency excursions are experienced and conventional discrete Fourier transform (DFT)-based methods do not provide satisfactory results. Speed and accuracy of the response can also be controlled. Structural simplicity and robustness of the proposed scheme make it well suited for digital implementation on software and hardware platforms. Performance of the proposed method is presented based on simulation studies in the MATLAB environment and an experimental setup.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".