General Impedance Representation of Passive Devices Based on Measurement
Bibliographic record
Abstract
Noise propagation from power stages of power converters to their low-voltage control boards depends on multiple complex paths, generally created by parasitic capacitors across isolation barriers. These barriers can be easily crossed by the high frequencies (up to 100 MHz [5]) generated by new semiconductor technologies such as SiC and GaN resulting in compromised signal integrity on the control side. A common approach to overcome this problem is by using filter. However, due to the presence of several complex propagation paths, DM and CM modes are not properly defined at board level, causing difficulties to predict filter's performance. To cope with this issue, the node-to-node impedance function (NIF) is proposed to identify the impedance of all possible propagation paths in the filter. In the considered frequency range (>30 MHz), NIF parameters identification precision is altered by the impedance of shorting paths used in measurement procedure. In this paper, an optimization procedure based on Newton-Raphson algorithm is proposed to remove these errors. This improved version of NIF is named General Impedance Representation (GIR). Thanks to its generality, the GIR can also applicable for all kinds of passive devices. Experimental results are presented to confirm the effectiveness of 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".