MétaCan
Menu
Back to cohort
Record W2235113569 · doi:10.1115/1.4031657

Motion Analysis of Manipulators With Uncertainty in Kinematic Parameters

2015· article· en· W2235113569 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

VenueJournal of Mechanisms and Robotics · 2015
Typearticle
Languageen
FieldComputer Science
TopicNumerical Methods and Algorithms
Canadian institutionsQueen's University
Fundersnot available
KeywordsKinematicsParametric statisticsControl theory (sociology)Interval (graph theory)MathematicsMatrix (chemical analysis)Motion (physics)Computer scienceArtificial intelligenceClassical mechanicsPhysicsStatistics

Abstract

fetched live from OpenAlex

In this article, a novel method for characterizing the exact solution for interval linear systems is presented. In the proposed method, the entries of the interval coefficient matrix and interval right-hand side vector are formulated as linear functions of two or three parameters. The parameter groups for two- and three-parameter cases are identified. The exact solution is characterized using the solution sets corresponding to the parameter groups. The parametric method is then employed in the motion analysis of manipulators considering the uncertainty in kinematic parameters. Example manipulators are used to show the implementation of the method and the effect of uncertainty in the motion performance.

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)0.001
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: Methods
Teacher disagreement score0.241
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.274
Teacher spread0.237 · 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