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Record W3098725329 · doi:10.1115/detc2001/dac-21026

Workspace-Based Design of Parallel Manipulators of Star Topology With a Genetic Algorithm

2001· article· en· W3098725329 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsWorkspaceStar (game theory)Genetic algorithmAlgorithmComputer scienceA* search algorithmTopology (electrical circuits)Optimal designAlgorithm designMathematical optimizationMathematicsArtificial intelligenceRobotCombinatorics

Abstract

fetched live from OpenAlex

Abstract This paper presents the results of the implementation of a genetic algorithm for the design of parallel manipulators of Star topology based on the characteristics of its workspace (W). The algorithm allows to propose new geometries of manipulators that maximize the weighted sum of the volume of W, the percentage of W having a dexterity index greater than a minimum threshold, and a shape ration of W. The algorithm has proven to be effective by proposing a new design that overcomes, by a factor of 3.636, the performances of the original Y Star design, after evaluating the performances of only 2000 alternative designs, which correspond to a traveling of only 2.02 × 10−16% through the search space of this design problem.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.475
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.197
Teacher spread0.184 · 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

Quick stats

Citations7
Published2001
Admission routes1
Has abstractyes

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