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Record W2801833168 · doi:10.1139/tcsme-2015-0050

ON THE HYBRID-DRIVEN LINKAGE MECHANISM WITH ONE INPUT CYCLE CORRESPONDING TO TWO OUTPUT CYCLES

2015· article· en· W2801833168 on OpenAlexvenueno aff
Ren-Chung Soong

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsControl theory (sociology)Linkage (software)KinematicsLink (geometry)Mechanism (biology)Reciprocating motionTrajectoryFour-bar linkageComputer scienceConstant (computer programming)Angular velocityMathematicsMotion (physics)PhysicsClassical mechanicsArtificial intelligence

Abstract

fetched live from OpenAlex

A hybrid-driven five-bar linkage mechanism with one input cycle corresponding to two output cycles is presented. The proposed linkage mechanism is driven by a constant-speed motor and a linear motor, respectively. The output link can generate two same required output cycles during a single input cycle, while the rotational input link rotates with a constant angular speed, and the linear input link follows a reciprocating motion along a specified linear guide fixed on the rotational input link. The configuration, displacement relationship between the input and output links, and conditions of mobility of this proposed mechanism were studied, and a kinematic analysis was performed. The selection of the instantaneous motion trajectory of the linear input link and an optimal dimensional synthesis are also described. An example is provided to verify the feasibility and effectiveness of this methodology.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.205
Teacher spread0.187 · 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
Published2015
Admission routes1
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicRobotic Mechanisms and DynamicsFrench-language works237,207