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Record W4285151713 · doi:10.1109/icjece.2022.3166240

Design and Implementation of Universal Converter Conception et implémentation d’un convertisseur universel

2022· article· en· W4285151713 on OpenAlexvenueno aff
K. Suresh, E. Parimalasundar

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsnot available
Fundersnot available
KeywordsRectifier (neural networks)Ćuk converterInductorComputer scienceInverterElectrical engineeringBoost converterVoltageCapacitorMATLABElectronic circuitDiodePower (physics)Electronic engineeringTest benchEngineeringPhysics

Abstract

fetched live from OpenAlex

A newly designed universal converter is depicted for all forms of power conversions that include step-up and step-down operations. Universal operations include a rectifier, an inverter, a dc–dc converter, and ac voltage control operations in multi-input and multi-output circuits. The circuit consists of power switches such as six transistors, four thyristors, and two diodes, along with two inductors and two capacitors. The inductor role is important for step-up and step-down voltage levels from the source to the required output. In the existing converter, the number of switches is reduced from 26 to 12 in the proposed converter to obtain efficient operations. The circuit is initially developed in a MATLAB/Simulink platform by using a mathematical model and, finally, feasibility and effectiveness have been tested in the hardware test bench model. This converter is highly recommended for wind-solar-powered self-charging hybrid electric vehicles.

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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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
GenreMethods

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

Citations24
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Electrical and Computer EngineeringSame topicAdvanced DC-DC ConvertersFrench-language works237,207