Bibliographic record
Abstract
A novel flying capacitor (FC)-based dc-dc converter topology is introduced. This aptly named Exponential Flying Capacitor (EFC) topology achieves an exponential, rather than linear, decline in flying capacitor voltages when compared against the conventional multi-level flying capacitor (MLFC) topology. Such an abrupt drop in FC voltages not only significantly reduces the total operational flying capacitive energy storage/volume, but additionally permits the use of a smaller output filter inductance relative to the conventional MLFC when both topologies are paired equally with more than 3 FCs. This paper describes the structure and operation of the EFC topology, as well as further elaborates on the comparisons between the conventional MLFC. Finally, the potential advantages of the EFC are experimentally verified using a 3-FC, 48 V to 5 V / 3.3 V / 1.0 V (1.0 A) discrete converter prototype running at a 12.5 kHz switching frequency. At these stated operating levels, the 3-FC EFC requires an in-ductor that is up to 3 times smaller compared to the conventional 5-level (3-FC) MLFC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".