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Record W2036632957 · doi:10.1115/1.1814652

Tripod Dynamics and Its Inertia Effect

2005· article· en· W2036632957 on OpenAlexaff
Fengfeng Xi, Ottavio Angelico and, Rosario Sinatra

Bibliographic record

VenueJournal of Mechanical Design · 2005
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTripod (photography)InertiaKinematicsControl theory (sociology)Dynamics (music)System dynamicsComputer scienceEquations of motionMathematicsEngineeringClassical mechanicsMechanical engineeringPhysicsArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

In this paper the tripod dynamics and its inertia effect is studied. The tripod design is becoming popular for the development of parallel kinematic machines (PKMs). The combination of a tripod with a gantry system forms a hybrid machine that offers the advantages from both serial and parallel structures. The tripod dynamics under study includes the mass of the moving platform as well as those of the legs. The natural orthogonal complement method is applied to derive the dynamic equations. The inertia effect of the moving platform and the legs is investigated in terms of two parameters, namely, the ratio of the total leg mass to the mass of the moving platform, and the velocity of the moving platform. The dynamic equations are separated by three terms, inertia, coupling, and gravity. Quantitative studies are carried out by simulation to examine how the two parameters affect the three respective terms. Based on the simulation results, the dynamic equations can be simplified by retaining the dominant terms while neglecting less significant ones. The simplified dynamic equations provide an efficient model for design and control of tripods.

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)0.001
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 score0.789
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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

Citations29
Published2005
Admission routes1
Has abstractyes

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