Bibliographic record
Abstract
Coherent analysis techniques and design procedures for DC to AC converters ranging from 250 to 500 Watts working at 13.56MHz based on off-the-shelf power VDMOS transistors are presented in this work. This is accomplished by making contributions in three main areas: modeling of Standard Power and RF VDMOS transistor types at High Frequency (HF), identification of HF converter configurations and modeling of HF transformers. For VDMOS modeling, the Large-Signal Impedance model in conjunction with a suitable parameter extraction process is proposed as a simple but sufficient strategy to characterize the transistor optimum operating conditions inside the HF converter. For converter configuration identification, reactance cancellation and resistance adaptation techniques are used to systematically synthesize a single transistor converter with a minimum component count and well defined component values. Finally for HF transformer modeling, a model based on a Multi-conductor Transmission Line representation which parameters are identified by preserving the differential properties of the transmission line cable forming the windings is demonstrated as a viable approach to analyze and design combined single transistor converters to obtain higher output power. Detailed experimental results are presented for 250W converters based on either the ARF446 or the IRFP450A with overall efficiencies of about 83% as well as a 500W push-pull converter based on two ARF446 with an overall efficiency of 78%.
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 | 0.001 |
| 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 | 0.001 |
| 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".