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Wireless Torque Transfer using Rotating Magnetic Field with Multiple Coils

2021· article· en· W3174208422 on OpenAlexaff
Jaewon Rhee, Yujun Shin, Haerim Kim, Jong-Wook Kim, Changmin Lee, Sungryul Huh, Seongho Woo, Seokhyeon Son, Seungyoung Ahn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsFuture Vehicle Technologies (Canada)
FundersDefense Acquisition Program AdministrationNational Research Foundation of Korea
KeywordsTorqueElectromagnetic coilMagnetic fieldWireless power transferSpin-transfer torqueRotating magnetic fieldPhysicsCurrent (fluid)Electrical engineeringMechanicsAcousticsEngineeringMagnetization

Abstract

fetched live from OpenAlex

Various methods of wireless torque transfer have been introduced to the fields of applications and highly utilized. In this paper, a method of wirelessly transmitting torque to a magnetic material by generating a rotating magnetic field using multi-coils is proposed. The rotating magnetic field generated by the current flowing in the proposed coil arrangement can be proved by using Biot-Savart law. Therefore, when a magnetic material is placed between the proposed coils, torque is generated due to the rotating magnetic field. Furthermore, since the torque by the magnetic field is closely related to the current applied to the coils, so that the torque can be changed through current. The proposed method was verified through theoretical analysis and simulation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.195
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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