Assessment of operating modes of hybrid electromagnetic elements in the inductive-capacitive converters
Bibliographic record
Abstract
Methods of functional integration of electromagnetic elements (EME) allow to improve the technical and economic indicators, accelerate the development process, improve manufacturability and reliability, as well as reduce the cost of secondary power sources (SPS). The authors suggest the use of a hybrid EME as an inductive-capacitive converter (ICC), called a "multifunctional integrated electromagnetic component" (MIEC). The authors consider various MIEC designs, in particular a two-section structure. When designing complex MIEC structures, many unresolved issues and tasks arise. The research and development of MIEC and electrotechnical devices based on them is an urgent task. The frequency and energy characteristics of the ICC are analyzed on the basis of a two-section MIEC in this article. An experimental confirmation of the adequacy of the developed mathematical model for calculating and constructing the frequency characteristics of MIEC and estimating the stabilization properties of ICCs for various design versions of MIEC is carried out. It is obtained that in the manufacture of two identical MIECs with identical electrical parameters placed on separate frames with a common magnetic circuit (with the same number of turns of each electrode, active and inductive resistance of MIEC) this circuit solution of ICC has a higher voltage gain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".