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Record W2978497038 · doi:10.1109/tcsi.2019.2941737

A High Efficiency AC/DC NVC-PSSHI Electrical Interface for Vibration-Based Energy Harvesters

2019· article· en· W2978497038 on OpenAlexafffund
Ahmed O. Badr, Edmond Lou, Ying Y. Tsui, Walied A. Moussa

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2019
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInterface (matter)VibrationMaterials scienceElectrical engineeringAutomotive engineeringAcousticsPhysicsEngineeringComposite material

Abstract

fetched live from OpenAlex

Energy harvesting is the process of capturing the local ambient energy and converting it into useful energy. Piezoelectric energy harvesters (PEHs) are one of the solutions to convert vibration energy to electrical energy. An electrical interface is needed to bridge between the piezoelectric energy harvester and the energy storage element. A high-efficient AC/DC converter is designed for a low vibration power harvester with the millior micro-watt range power. This paper reports a novel self-powered high-efficiency interface to rectify the AC voltage generated by a vibration energy harvester to DC voltage. The reported interface consists of a triggering circuit and a Negative Voltage Converter (NVC) combined with a Parallel Synchronized Switch Harvesting on Inductor (PSSHI) forming the NVC-PSSHI. The analytical model and simulation of the designed NVC-PSSHI interface were derived and performed, respectively. The targeted input voltage, frequency and DC loading conditions were 3 Vpp to 7 Vpp, 100 Hz to 500 Hz and 5 kΩ to 30 kΩ, respectively. Experiments with a PEH were also performed to validate the analytical and simulation results. The maximum efficiency of the designed NVC-PSSHI interface reached 82.1% from the PEH experiment. The NVC-PSSHI interface efficiency was higher than the traditional PSSHI interface by up to 23.4%.

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 categoriesMeta-epidemiology (narrow)
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 score0.822
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
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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.

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

Citations20
Published2019
Admission routes2
Has abstractyes

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