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Record W4379793344 · doi:10.23977/jeeem.2023.060306

Research and Application of Power Distribution Model Based on Simulated Annealing Method

2023· article· en· W4379793344 on OpenAlexvenueno aff
Yu Cao, Zhiyuan Wang, Xin Wang, X. F. Zhang, Maosong Wang

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2023
Typearticle
Languageen
FieldEngineering
TopicIndustrial Technology and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsSimulated annealingMathematical optimizationPower (physics)Tree traversalControl theory (sociology)MathematicsComputer scienceAlgorithmControl (management)Physics

Abstract

fetched live from OpenAlex

This paper focuses on the power distribution curve and establishes a power distribution model based on the influence of external resistance and the nature of the object. The power supply model was established by the influence of external physical factors on the object using a physical analysis method. Combining the two models, the driver was identified, the initial edge conditions were determined, and a system of differential control equations containing three parameters was established. For the time problem with trajectory, a single-objective optimization model with the minimum time as the objective was established. Firstly, the maximum power provided by the drive is determined; secondly, the constrained objective function is determined as the process limit; secondly, the constraints are simulated and annealed and the power is searched using a circular traversal method.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.272
Teacher spread0.261 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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