Efficient non-Monte Carlo method for statistical analysis of periodically switched linear circuits in frequency domain
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Bibliographic record
Abstract
The author presents an efficient non-Monte Carlo method for the statistical analysis of periodically switched linear (PSL) circuits in the frequency domain. The method uses the first-order second-moment (FOSM) approach to compute the mean and variance of the frequency response of PSL circuits. In addition, it uses a new, efficient, and accurate multi-step numerical Laplace inversion algorithm to calculate the response of PSL circuits. The method has been implemented in a computer program. Numerical results on example circuits demonstrate that the method is computationally efficient. It yields good estimation of the mean and variance of the frequency response of PSL circuits when the coefficient of variance of circuit parameters is low. The method is applicable to general PSL circuits, including switched capacitor and switched current networks, and can be incorporated into existing CAD tools for rapid analysis of the stochastic behaviour of PSL circuits in the frequency domain.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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