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Record W2848215114 · doi:10.1139/tcsme-2017-1036

DYNAMIC MODELING OF LONG SPAN NEW MATERIAL CABLE ROBOT

2017· article· en· W2848215114 on OpenAlexvenueno aff
Sobhan Sohrabi, Hamid M. Daniali, Ali Reza Fathi

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsWorkspaceFlexibility (engineering)Span (engineering)Mechanism (biology)TrajectoryComputer scienceControl theory (sociology)Parallel manipulatorPath (computing)RobotCurvatureRobot end effectorMotion planningStructural engineeringControl engineeringEngineeringMathematicsPhysicsArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

Cable driven parallel manipulator (CDPM) is a robotic mechanism which utilize multiple actuated cables to manipulate objects. It offers some advantages over the conventional parallel manipulators, such as higher load to weight ratio and larger workspace. These advantages are more evident if one uses composite materials for the cables of CDPM. This study aims at dynamic analysis and trajectory path planning of long-span CDPM by taking into accounts the effects of mass, curvature, flexibility and viscoelastic behavior of its new material cables. The dynamic analysis of the CDPM shows that its end effector vibrates throughout its desired trajectory which leads to an oscillatory pose error. An optimization algorithm is used here for the error suppression of the path planning 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.

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.204
Teacher spread0.191 · 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
Published2017
Admission routes1
Has abstractyes

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