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Record W2068591625 · doi:10.1115/imece2007-42475

Variable Antagonistic Stiffness Element Using Tensegrity Mechanism

2007· article· en· W2068591625 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
TopicStructural Analysis and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTensegrityStiffnessControllabilityStructural engineeringKinematicsVibrationMechanism (biology)Direct stiffness methodComputer scienceMaterials scienceEngineeringStiffness matrixPhysicsMathematicsClassical mechanicsAcoustics

Abstract

fetched live from OpenAlex

Tensegrity mechanisms are self-stressing mechanisms and it is known that the prestress of the elements affect the stiffness of the tensegrity. In this paper stiffness of a spatial tensegrity is studied for the purpose of the noise and vibration control and it is shown that an efficient variable stiffness element can be designed by using tensegrities. The antagonistic force and antagonistic stiffness are explained briefly and the kinematics of the tensegrity is analyzed. Also, the possible motion, the elastic stiffness, load stiffness and antagonistic stiffness formulation for the tensegrity are found symbolically. Some techniques for increasing the magnitude of the antagonistic stiffness are mentioned. The effect of the geometry on the stiffness, stiffness controllability and linearity are shown by examples. Finally, the results of this approach are verified by mechanical simulation of the designed tensegrity.

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: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.573

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)0.0010.000

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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

Citations5
Published2007
Admission routes1
Has abstractyes

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