Low Power, Non-Isolated, Extremely-High Step-Up, Quasi-Resonant Hybrid dc–dc Converter
Bibliographic record
Abstract
This paper introduces a low-power, non-isolated, step-up quasi-resonant converter and a complementary mixed-signal controller. The introduced converter is capable of generating conversion ratios exceeding 50x, while maintaining high power processing efficiency. At low power levels, ranging from a fraction of a watt to a few watts, conventional ultra-high step-up solutions operate at efficiencies that are not exceeding 45%, mostly limited by switching losses. The introduced solution exhibits efficiencies exceeding 70%. The improvements are achieved by strategically incorporating a N-stage charge pump with a previously introduced fully soft-switching quasi-resonant boost converter. The solution exhibits load-independent soft-switching for all the active switches, at all voltage conversion ratios. The operation and performance of the introduced converter are experimentally verified with a discrete prototype with input voltage 3.6 V, boosting it to high output voltages ranging from 55 V to 250 V, while supplying load currents ranging from 1 mA to 20 mA and achieves peak efficiency of around 85%.
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How this classification was reachedexpand
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 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".