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Record W2143824152 · doi:10.1115/detc2007-34286

Workspace Optimization of a Very Large Cable-Driven Parallel Mechanism for a Radiotelescope Application

2007· article· en· W2143824152 on OpenAlexaff
Samuel Bouchard, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsWorkspaceMechanism (biology)Reflector (photography)TorqueAntenna (radio)Computer scienceSet (abstract data type)Tension (geology)SimulationControl theory (sociology)PhysicsTelecommunicationsRobotOpticsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

The 6-DOF cable-driven mechanism under study in this paper is a feed positioning device for the Large Adaptive Reflector (LAR). The LAR is a concept of a very large orientable radio antenna. The study aims at optimizing the geometry of the cable mechanism to maximize the portion of the desired workspace in which the mechanism can remain in static equilibrium under the predicted external forces and torques. A general test to rapidly compute if the set of external forces and torques can be balanced is developed. This test is applicable to mechanisms with an arbitrary number (minimum six) of cables. Architectures with six to nine cables are optimized and compared. The conclusion of this study is that the prescribed task is unlikely to be achievable by this type of mechanism. Some design guidelines to improve the performance of a large cable-driven mechanism kept under tension by an aerostat are also provided.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.211
Teacher spread0.205 · 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

Citations21
Published2007
Admission routes1
Has abstractyes

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