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Record W4320893537 · doi:10.1155/2023/5795266

The Influence of the Load Type on the Center Frequency Tuning of the Variable-Flux Biaxial Energy Harvester

2023· article· en· W4320893537 on OpenAlexaff
Karim El-Rayes, S. R. I. Gabran, William Melek

Bibliographic record

VenueShock and Vibration · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCenter frequencyResistive touchscreenPhysicsElectromagnetic coilElectrical engineeringMagnetCapacitive sensingEnergy (signal processing)StiffnessCenter (category theory)Flux (metallurgy)Power (physics)AcousticsControl theory (sociology)EngineeringStructural engineeringMaterials scienceComputer scienceQuantum mechanicsControl (management)

Abstract

fetched live from OpenAlex

The variable-flux biaxial energy harvester V B H is a novel vibration energy harvester V E H architecture in which a ferromagnetic mass moves against an assembly of a coil and a stationary permanent magnet. The varying flux lines induce potential differences across the coil terminals. Unlike conventional VEHs, the center frequency of the VBH can be tuned by mechanical, electronic, or electromagnetic mechanisms to maximize the generated power. In this research, we demonstrate the impact of two types of loads, purely resistive and complex RC loads, on the harvester center frequency. We also demonstrate a mechanism that utilizes RC load to tune the center frequency of the VBH. In comparison to conventional purely resistive loads, the introduced capacitive load allows control of the total VBH stiffness and, accordingly, modulates the center frequency and eliminates the need for mechanical pretuning of the VBH center frequency.

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)0.002
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.203
Teacher spread0.190 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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