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Record W2994629076 · doi:10.5539/mer.v9n2p20

Analysis of a Continuously Variable Transmission in which Four-Bar Linkages Are Arranged in Parallel

2019· article· en· W2994629076 on OpenAlexvenueno aff
Toshihiro YUKAWA, Syoma Kumagai, Taiyo Fujisawa, Yoshiaki OSHIDA, Youichi Takeda, Kazuyuki HANAHARA

Bibliographic record

VenueMechanical Engineering Research · 2019
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsLeverContinuously variable transmissionCrankLinkage (software)Mechanism (biology)Control theory (sociology)Thermal conductionTransmission (telecommunications)ActuatorPower consumptionPower (physics)Computer scienceAutomotive engineeringMechanical engineeringEngineeringMaterials scienceElectrical engineeringControl (management)PhysicsComposite material

Abstract

fetched live from OpenAlex

This paper describes a development of a new structural type of continuously variable transmission (CVT). We here propose a CVT with linkages and irreversible mechanisms which does not positively depend upon frictional conduction force between conduction components. In the proposed CVT, four lever-crank units are connected in parallel mechanically with the cranks at an input shaft, and the output shaft is also connected mechanically via an irreversible mechanism installed at the fulcrum of the lever. In the experiment, we confirm that the continuous control of gear ratios in real time by realizing high-precision control for expansion and contraction of the links using electric linear actuators. As a merit of the developed linkage type of CVT, it reduces power consumption, compared to other conventional CVTs.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

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)0.0020.000

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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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