Pulse Density Modulation Pattern Optimization using Genetic Algorithms
Bibliographic record
Abstract
Pulse density modulation (PDM) can be used to drive resonant power converters and is an alternative to pulse width modulation (PWM). Its main advantage is simplicity, which allows a power device to achieve zero-current (or voltage) switching while performing load power regulation. Reduced switching stress hinders a converter from polluting power lines with electromagnetic noise. This technique is suitable for designing power converters that show a good overall power factor and low total harmonic distortion (THD). PDM can be used to drive resonant (series or parallel) power converters. These converters are frequently used in induction heating applications where they are required to operate at high frequencies and deliver a wide range of output powers. Conveniently, the power factor produced by PDM converters is near unity and THD is low at high-output powers. However, at low-output powers, THD increases and the power factor gets far away from unity. This paper presents a technique that makes it possible to obtain optimal PDM patterns. Simulations are used to show that intelligent PDM pattern generation using genetic algorithms allows for an improved power factor and a reduced THD at low-output powers. A comparison with other PDM pattern generation techniques shows that AG patterns demonstrate a much better performance
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".