Multi‐resonator arrays for smart wireless power distribution: comparison with experimental assessment
Bibliographic record
Abstract
This study presents the design of efficient wireless power distribution systems based on resonant inductive arrays. The authors show how to use multi‐resonator arrays to charge and power up several electric devices in parallel, with nearly constant transmitted power, and using a single power source. Their single‐source wireless power transmission clusters, for instance, are shown to provide free positioning at better power efficiency than previous solutions. They provide analysis, simulation, and measurement performance of their multi‐resonator arrays, they compare them with other types of inductive arrays employed into different schemes (multi‐coil inductive links, overlapping and non‐overlapping links), and they show the advantage of their strategy over previous solutions. The presented wireless power distribution systems improve power transmission efficiency (PTE) in free positioning by as much as 30%. The measured results show that their multi‐resonator arrays present significant advantages: (i) they allow multiple charging zones from a single power source; (ii) they provide free positioning with strictly uniform power delivered to the load; and (iii) they provide superior efficiency through a built‐in power localization mechanism, which is not available in other solutions. The PTE of the multi‐resonator array in single‐receiver and multi‐receiver configurations outperformed previous solutions by 26% and 12%, respectively.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".