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Record W2566698704 · doi:10.1139/tcsme-2012-0005

THE DOF DEGENERATION CHARACTERISTICS OF CLOSED LOOP OVER-CONSTRAINED MECHANISMS

2012· article· en· W2566698704 on OpenAlexvenueno aff
Sheng Guo, Haibo Qu, Yuefa Fang, Congzhe Wang

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMechanism (biology)Degrees of freedom (physics and chemistry)Degeneration (medical)Control theory (sociology)Loop (graph theory)Computer scienceClosed loopTerminologyMathematicsControl (management)Control engineeringEngineeringArtificial intelligencePhysicsMedicine

Abstract

fetched live from OpenAlex

A new teminology, “degenerative degrees of freedom”, to describe mechanisms possessing different degrees of freedom (DOF) while containing the same number of linkages and joints is introduced. A systematic approach is developed for studying this particular type of closed loop mechanism and its degeneration characteristics of DOFs. First, the single closed loop over-constrained mechanism is analyzed and a relationship between the number of over-constraints and the number of joints and DOFs is established. Then, all possible types of independent over-constraints and their combinations are summarized. Further the non-instantaneous condition of the mechanism is analyzed by using an analytical method. The paper delineates three rules that provide the guidelines for the layout of joints, linkages and their assembly. Finally, the degenerative characteristics of all such mechanisms are systematically tabulated along with sketches of some typical ones. To corroborate the literature an example involving two 6R closed loop mechanisms with 1 and 3 DOFs respectively is presented and analyzed, thus validating their degenerative characteristics.

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: Methods · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.522

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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
GenreMethods

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
Published2012
Admission routes1
Has abstractyes

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