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Record W2913665447 · doi:10.1109/icecs.2018.8617906

Study and Design of MEMS Cross-Shaped Piezoelectric Vibration Energy Harvesters

2018· article· en· W2913665447 on OpenAlexaff
Abdul Hafiz Alameh, Mathieu Gratuze, Alexandre Robichaud, Frédéric Nabki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsEnergy harvestingMicrosystemPiezoelectricityVibrationResonance (particle physics)Proof massMicroelectromechanical systemsRectifier (neural networks)Beam (structure)AcousticsMaterials scienceMechanical resonancePower (physics)CMOSElectrical engineeringPhysicsEngineeringOptoelectronicsComputer scienceOptics

Abstract

fetched live from OpenAlex

In this paper, a study and design of a cross-shaped piezoelectric vibration energy harvester is presented. The resonance frequency of the harvesters dictates the applications where they can be used, and more importantly their effects on the power outputs. The variations of the resonant frequencies of T-shaped and cross-shaped harvesters has been studied as a function of their geometry. Different geometrical dimension ratios have significant impact on the resonance frequency, e.g., beam to mass lengths, and beam to mass widths. Accordingly, two cross-shaped piezoelectric energy harvesters featuring a central mass with four beams have been designed operating at resonance frequencies of about 9.8 and 4.3 kHz respectively. The extra mass added to the later design increases the average strain resulting in an improved performance. A cross-coupled rectifier circuit has been designed and simulated in 0.13 μm CMOS technology to be combined with the harvester to attain an integrated microsystem.

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 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 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 score0.516
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.255
Teacher spread0.225 · 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 teacher head, 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

Citations3
Published2018
Admission routes1
Has abstractyes

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