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Flexible Inductive Wireless Power Transfer System with an Onboard Temperature Sensor

2021· article· en· W3182374924 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

Venue2021 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) · 2021
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaximum power transfer theoremWireless power transferElectrical engineeringWirelessWireless sensor networkWearable computerPower (physics)KaptonVoltageEnergy harvestingComputer sciencePower moduleMaterials scienceElectronic engineeringPolyimideEmbedded systemEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Inductive wireless power transfer has been recognized as a feasible solution to expand the lifespan of wearable and implants. Apparently, these devices have to be mechanically flexible. In this paper, we propose a novel flexible inductive wireless power transfer system with an onboard temperature sensor. We use the flexible Kapton (polyimide) film and printed silver traces to create the circuit layout of the system. The fully flexible and bendable wireless power transfer system occupies an area of 1 cm × 4 cm. Experimental results indicate that the system can deliver 3.3 V DC voltage to the load at the operational frequency of 3.5 MHz when the receiving unit is at a distance of 4 mm. Furthermore, we demonstrate the capability of the proposed system to power up the embedded temperature sensor in two mediums, i.e., air and water. It was observed that the temperature sensor has a linear response in both mediums, which clearly confirms the reliability and stability of the scavenged power by our 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.

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 categoriesMeta-epidemiology (narrow)
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 score0.388
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.247
Teacher spread0.219 · 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