High‐gain single‐switch single‐input dual‐output DC‐DC converter with reduced switching stress
Bibliographic record
Abstract
Abstract In light‐emitting diode (LED) driver applications, as an alternative of employing two separate conventional DC‐DC converters, single‐input dual‐output converters (SIDO) have excellent performance over traditional topologies. However, SIDO has the limitation of more number switches required, which leads to higher switching losses and deteriorating efficiency. In order to overcome this drawback, this paper presents a novel of a single‐switch single‐input dual‐output DC‐DC converter with a high‐gain ratio. It attains the two boosted outputs with a high‐gain conversion ratio using a single switch by incorporating the switched‐capacitor technique. In addition, the bulkiness of the converter was reduced by utilizing only one inductor and switch. Moreover, it improves efficiency by reduced switching stress and cross‐regulation at output ends. Furthermore, the performance of the converter in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) has been explained in this paper. Finally, a 120‐W hardware setup has been built and tested. The experiment results are presented to validate the merits of the proposed topology.
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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 | 0.001 | 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, 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".