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Record W2165366872 · doi:10.1109/robot.1992.220246

Numerical stability of forward-dynamics algorithms

2003· article· en· W2165366872 on OpenAlexaff
R.E. Ellis, O.M. Ismaeil, I.H. Carmichael

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputationStability (learning theory)Computer scienceTree (set theory)Topology (electrical circuits)Inverse dynamicsNetwork topologyLink (geometry)AlgorithmMathematicsTheoretical computer sciencePhysics

Abstract

fetched live from OpenAlex

Many physical systems, particularly robotic linkages, can be modeled as mechanisms. In order to simulate the dynamical behavior of such systems as the number of links become large, the algorithms must produce efficient and stable computations. The mechanisms studied here include those which have tree-structured topologies, that is, a link may have more than one successor link but there are no loops in the linkage topology. The authors present a formulation of the dynamics based on representing Lagrangian mechanics with spatial, or screw, displacements. This shows that several existing algorithms are equivalent to recursive calculations on an inertial supermatrix, which is a matrix the elements of which are matrices. The result is numerically stable forward and inverse computations of the second-order terms that grow linearly with the number of links, and provides a new insight into the nature of the dynamics of mechanisms.>

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2003
Admission routes1
Has abstractyes

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