MétaCan
Menu
Back to cohort
Record W2155327361 · doi:10.1109/cjece.2015.2431312

PSPICE-Based Analyses of the Vibration Energy Harvester System With Multiple Piezoelectric Units

2015· article· en· W2155327361 on OpenAlexvenueno aff
Xiaobin Cui, Mingming Teng, Junhui Hu

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaGovernment of Jiangsu Province
KeywordsCapacitancePiezoelectricityEnergy harvestingEnergy storageVibrationEnergy (signal processing)Electronic engineeringAcousticsMaterials scienceElectrical engineeringEngineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

It has been a challenge to determine the optimal energy-storage capacitance in piezoelectric vibration energy harvesting (VEH) systems for a given working time. In this paper, the PSPICE software is used to determine the optimal energy-storage capacitance in a VEH system with one or more piezoelectric modules, for maximizing the energy harvesting capability, and to investigate the dependence of this optimum energy-storage capacitance on the device and working parameters. The simulation method is verified by experimental results. It is found that the optimal energy-storage capacitance increases with an increase of the clamped capacitance, working frequency, and working time of the piezoelectric modules. It is also found that the optimal energy-storage capacitance is proportional to the number of the piezoelectric modules used in the VEH system if the piezoelectric modules have close parameters.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.736
Threshold uncertainty score0.365

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 head0.022
GPT teacher head0.182
Teacher spread0.160 · 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 designSimulation or modeling
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

Citations10
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Electrical and Computer EngineeringSame topicInnovative Energy Harvesting TechnologiesFrench-language works237,207