General impedance representation of passive devices based on measurement
Bibliographic record
Abstract
The impedance modeling of passive device is mandatory for EMI prediction of power converter on printed board circuit (PCB) level. The black-box node-to-node impedance function (NIF) model, which extracts the connecting impedance matrix from measurement, can be used as the most general representation. However, the most recent development of this model is still based on the assumption of ideal shorting path used in measurement, which is not true since its small inductance results in high impedance in EMI frequency. It interacts with small inductive and high capacitive impedance of popular passive devices used in the power converter, i.e. common choke, LC filter, power supply, resulting in computational errors of the connecting impedance matrix. In this paper, the errors in the model created by shorting path impedance is analyzed and eliminated by employing the Newton-Raphson (NR) iterative method. This work helps to improve the precision of the model, herein called general impedance representation (GIR); and hence, enables it to be applied for all kinds of passive devices without knowledge of the device's specific model. The experimental results are presented to confirm the effectiveness of the proposed GIR.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".