Foreign Object Detection of Wireless Power Transfer System Using Sensor Coil
Bibliographic record
Abstract
Wireless Power Transfer (WPT) system are used in many areas due to their advantages such as safety, aesthetics, and convenience. The WPT system are being applied not only smartphones that are common around us, but also to various electronics and medical devices. In particular, in the case of Electric Vehicle (EV), researches are steadily underway to apply the WPT system to solve the problem of battery dependence. Inductive power transfer (IPT) is the most popular WPT method to transfer power using the magnetic field. However, Foreign Object (FO) in WPT system can be heated by strong magnetic field and can lead to fires. Also, it can reduce power transfer efficiency. The risk of fire in a WPT system such as EVs, which require large power, is a major obstacle. Therefore, FO detection method is necessary for the safety and good performance. In this paper, we propose a Foreign Object Detection method using sensor coils. The proposed method is simple and shows the good performance compared with conventional method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".