Matrix Persymmetry Analysis for Misalignment and Foreign Object Detection in Resonant Capacitive Power Transfer
Bibliographic record
Abstract
This paper introduces a persymmetry evaluation of capacitance matrices for resonant capacitive power transfer. Persymmetry analysis of the capacitance matrix indicates imbalances and allows for distinction between misalignments and foreign objects. Misalignment and foreign object detection are achieved with a parameter-based method. Voltages on the transmitter side of a resonant capacitive power transfer link are leveraged for detection. Simulations and supporting measurements were performed with a 13.56MHz resonant capacitive power transfer link incorporating a six-plate structure for electric vehicle charging applications. Metallic and living tissue objects can be detected with the foreign object detection method. Furthermore, lateral misalignment and its direction are detectable for realignment purposes. Simulations show that the foreign object detection range is sufficient to avoid exceeding the basic restrictions for electromagnetic field exposure for kW-range power transmission. The capacitance matrix persymmetry results indicate that both lateral misalignment and foreign object detection are achievable and distinguishable with the parameter-based method. This work introduces practical solutions to detecting imbalances in resonant capacitive power transfer systems, which may improve reliability and safety in applications such as electric vehicle charging and electrified roadways.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".