Ideal lowpass filter technique to enhance harmonic spectrum estimation for power electronics circuits
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a new and accurate method of harmonic analysis that permits to obtain high precision spectrum over wide range of bandwidth in order to mitigate power quality as well as EMI related problems. The proposed method is based on the estimation of intermediate points between the initial samples given by the available data acquisition system; therefore, the Fourier coefficients are estimated more precisely using the fast Fourier transform. As interpolation technique we chose digital to analog conversion with ideal lowpass filter for the reconstruction of the analog signal. The proposed method is tested on experimental signals and validated by adding known harmonic components to the measured signal. The obtained results are compared to traditional FFT as well as spline interpolation, and linear interpolation techniques. The performance of the proposed method and its superiority over the known techniques is discussed and the possible sources of errors are identified
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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