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Record W2097775347 · doi:10.1109/have.2003.1244722

Analysis of a two DOF anthropomorphic arm driven by artificial muscles

2004· article· en· W2097775347 on OpenAlex
Cedric Cocaud, Amor Jnifene

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
TopicDielectric materials and actuators
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsArtificial muscleTorquePneumatic artificial musclesActuatorComputer scienceSMA*Position (finance)Control theory (sociology)Mechanical engineeringEngineeringArtificial intelligencePhysicsControl (management)Algorithm

Abstract

fetched live from OpenAlex

This paper investigates the use of artificial muscles as actuators for robot manipulators. Using some basic equations from the field of kinesiology, a procedure was developed in order to determine the optimal position of a muscle in various musculoskeletal configurations found in the human body. This procedure takes into account the artificial muscle characteristics and finds the origin and insertion point of the muscle by optimizing the maximum range of motion and the output torque produced at the joint. Currently available artificial muscles such as McKibben air muscles, Shape Memory Alloys and electroactive polymers are reviewed. Functional models of the SMA and McKibben muscles were presented and used in the optimization procedure.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.223
Teacher spread0.216 · 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
Published2004
Admission routes1
Has abstractyes

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