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Record W2015644055 · doi:10.1115/detc2009-87597

Tensionability of an Arbitrary Two-Link Multibody

2009· article· en· W2015644055 on OpenAlexaff
Siavash Rezazadeh, Saeed Behzadipour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMultibody systemLink (geometry)Work (physics)Dependency (UML)Matrix (chemical analysis)Computer scienceTension (geology)Mechanical systemControl theory (sociology)Topology (electrical circuits)Mechanical engineeringEngineeringPhysicsClassical mechanicsElectrical engineering

Abstract

fetched live from OpenAlex

The main problem in cable-driven mechanisms is their tensionability, i.e. maintaining positive tension in all cables against any external load. Since the advent of these mechanisms, it was known that with one redundant cable, one can guarantee the tensionability of a rigid body driven by cables. However, the problem of tensionability in multibody systems driven by cables has remained almost intact. In a previous work by the authors, some necessary conditions on the number of cables and their possible distributions for tensionability of a general multibody were found. However, unlike the rigid body case, the sufficiency of these conditions for the tensionability of a multibody cannot be easily guaranteed. In this paper, we find the necessary and sufficient conditions for the tensionability of an arbitrary two-link multibody. Our approach investigates the equilibrium of such systems first by assuming that the cables can apply force in both directions (pull and push). Then, unidirectional force characteristic of the cables is incorporated and the associated conditions on the cables and their distributions are found. The heart of the method is based on a matrix called dependency matrix which carries the equilibrium properties of the system in a more compact form. Also, it has relatively clear connections to the geometry of the system which makes it easy to use.

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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.240

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 head0.006
GPT teacher head0.229
Teacher spread0.222 · 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 teacher head, 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

Citations4
Published2009
Admission routes1
Has abstractyes

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