Bibliographic record
Abstract
With the advent of powerful circuit simulation tools such as Spice-based simulators, the simulation of a power converter system has been reduced to the generation of an adequate electric circuit model of the system. However, this approach often leads to large execution times and uncertain results associated with convergence problems. Alternatives are switched-circuit simulators, where the switches are idealized by assuming zero on-resistance, infinite off-resistance and instantaneous switching. Though these simulators overcome the long execution times and convergence problems, both Spice-based and switched-circuit simulators have execution times proportional to the number of power switches. Furthermore, modern control techniques are difficult to implement. A practical and efficient solution that allows use of the discrete state approach is available today in the form of powerful and user friendly high level language compilers, such as C and BASIC. This paper illustrates the clear advantages of combining this approach with BASIC to simulate power converter systems. Moreover, the use of discrete states, instead of ideal switches, to model static power converters reduces execution time and introduces high flexibility in implementing complex PWM pattern generation algorithms, such as space vector or predictive control techniques, regardless of the power and control circuit structure and/or complexity.>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".