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Record W2593117883 · doi:10.1049/iet-pel.2016.0616

Design approach for a wireless power transfer system for wristband wearable devices

2017· article· en· W2593117883 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Power Electronics · 2017
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsSurrey Memorial Hospital
Fundersnot available
KeywordsWireless power transferWirelessWearable computerPower (physics)Maximum power transfer theoremInductanceTransmitterElectromagnetic coilElectrical engineeringComputer scienceElectronic engineeringEngineeringEmbedded systemTelecommunicationsChannel (broadcasting)VoltagePhysics

Abstract

fetched live from OpenAlex

In this study, the design approach for wireless power systems in the application of charging wristband wearable devices is studied. As the power transfer efficiency relates to the system parameters, the design approach includes an analytical model for available wireless power in planar circular coils with lateral and angular misalignments, which is typically the case in these applications. The model is then utilised in an optimisation algorithm to design the wireless power system such that maximum available power is transferred through the system. The practicality of the proposed wireless power transfer (WPT) system design is analysed in terms of positioning the power transmitter coil on a bed, while performing the power transfer during a sleeping period of the wearer. Experimental results validate the analytical model of mutual inductance variations and demonstrate the performance of the WPT system for this application.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.227
Teacher spread0.210 · 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