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Record W2000062375 · doi:10.1109/tia.2014.2331421

Improved-Power-Quality Bridgeless-Converter-Based Multiple-Output SMPS

2014· article· en· W2000062375 on OpenAlexaff
Shikha Singh, Bhim Singh, G. Bhuvaneswari, Vashist Bist, Ambrish Chandra, Kamal Al‐Haddad

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsPower factorBoost converterConvertersBuck converterSwitched-mode power supplyBuck–boost converterRectifier (neural networks)Electronic engineeringĆuk converterComputer sciencePower (physics)Control theory (sociology)VoltageEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper deals with the design, analysis, simulation, and development of a power-factor-correction (PFC) multiple-output switched-mode power supply (SMPS) using a bridgeless buck–boost converter at the front end. Single-phase ac supply is fed to a pair of back-to-back-connected buck–boost converters to eliminate the diode bridge rectifier, which results in reduction of conduction losses and power quality improvement at the front end. The operation of the bridgeless buck–boost converter in discontinuous conduction mode ensures inherent PFC operation and reduces complexity in control. The performance of the proposed multiple-output SMPS is evaluated under varying input voltages and loads by simulating this circuit in MATLAB/Simulink environment, and the results obtained through simulation are validated experimentally on a developed prototype. Both simulation and experimental results demonstrate the improved performance of the proposed SMPS.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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
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

Citations18
Published2014
Admission routes1
Has abstractyes

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