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An Asymmetric Adaptive Approach to Enhance Output Power in Kinetic-Based Microgenerators

2022· article· en· W4311413634 on OpenAlex
Masoud Roudneshin, Kamran Sayrafian, Amir G. Aghdam

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

Venue2022 IEEE Sensors · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsAccelerationKinetic energyEnergy harvestingComputer scienceWaveformElectric potential energyWearable computerParametric statisticsPower (physics)Generator (circuit theory)Energy (signal processing)Control theory (sociology)PhysicsArtificial intelligenceMathematicsClassical mechanicsTelecommunications

Abstract

fetched live from OpenAlex

A Coulomb force parametric generator (CFPG) can be used to harvest energy from the natural human body motion. This micro-harvester is the architecture of choice for integration with small wearable (or implantable) sensors. In this paper, an asymmetric adaptive approach to estimate the electrostatic force in a CFPG using the acceleration waveform is proposed. Simulations using human motion measurements show that the proposed approach achieves considerable gain in the harvested energy compared to the previously studied symmetric adaptive methodologies.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.232
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