Isolated Soft-Switching Flying-Capacitor based Quasi-Resonant Step-Up Converter
Bibliographic record
Abstract
An isolated quasi-resonant step-up converter, and a complementary mixed-signal controller suitable for isolated low-power ultra-high step-up applications, with voltage conversion ratios exceeding 60x and sub-one-watt to a few watts load, are introduced. The converter has the same structure as the previously reported isolated flying capacitor multilevel flyback converter (FCMFC), but operates in a completely different manner. With modification to this typology’s component parameters, and a newly introduced control scheme, load-independent soft-switching is achieved on all the switches on the secondary side and voltage stress across the primary switch has been reduced to the input voltage, for any input-to-output voltage conversion ratio. These improvements are achieved by fully charging and discharging a small flying capacitor on the secondary side, as a resonant element, through a specific technique that ensures proper gating timing for achieving soft-switching. The operation of the converter is experimentally verified using a discrete prototype with input voltage of 3 V and common output voltages of 90 V, 120 V and 180 V, and load currents ranging from 1 mA to 15 mA, demonstrating efficiencies up to 81%.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".