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Record W3160662311 · doi:10.1109/tmag.2021.3082018

Compact Wireless Motor Drive Using Orthogonal Bipolar Coils for Coordinated Operation of Robotic Arms

2021· article· en· W3160662311 on OpenAlexaff
Wei Han, K. T. Chau, Zhichao Hua, Hongliang Pang

Bibliographic record

VenueIEEE Transactions on Magnetics · 2021
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of Toronto
FundersResearch Grants Council, University Grants Committee
KeywordsElectromagnetic coilWireless power transferComputer scienceWirelessElectrical engineeringFlexibility (engineering)TransmitterMagnetPower (physics)Topology (electrical circuits)Channel (broadcasting)PhysicsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

This article proposes and implements a compact wireless motor drive using orthogonal bipolar coils for the coordinated operation of robotic arms. The key is to orthogonally integrate two bipolar coils to form a compact magnetic coupler (CMC), while each CMC serves as the transmitting coil and receiving coil, respectively. Instead of adopting the one-to-many coil structure and operating at multiple frequencies, in this article, two bipolar coils in the CMC are mutually decoupled and separately energized to drive two permanent magnet dc (PMDC) motors. As such, the wireless and selective motor drives can be achieved via the shared power-transfer channel at a single resonant frequency. All controls and switches are concentrated at the transmitter so that the receiver is entirely sealable. Accordingly, the coordinated operation of robotic arms can be realized with the high integration and no electrocution. Theoretical calculations and experimental results are given to validate the flexibility and feasibility of the proposed system.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.239
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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

Citations39
Published2021
Admission routes1
Has abstractyes

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