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Record W2048791094 · doi:10.1115/detc2010-28186

Design and Optimization of a Spatial Hybrid Motion System

2010· article· en· W2048791094 on OpenAlexafffund
P. R. Ouyang, Steven Cargnello

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsActuatorServomotorKinematicsComputer scienceMotion (physics)Control theory (sociology)Motion systemMotion controlMacroMotion estimationHybrid systemControl engineeringEngineeringArtificial intelligenceRobotPhysics

Abstract

fetched live from OpenAlex

In this paper, a spatial hybrid motion system is developed that integrates two types of motions through one compliant mechanism: a macro motion driven by a DC servomotor and a micro motion driven by a PZT actuator. A unique feature of the developed hybrid motion system is the elimination of interaction between the macro motion and micro motion. Three issues are addressed in this study: (1) the design principle and implementation of the hybrid motion system; (2) the kinematic analysis and dynamic analysis; and (3) the optimization design of the hybrid motion system. For the micro motion, the five-bar topology of a mechanical amplifier is used to increase amplifying ratio and improve dynamic performance of the system. Finite element analysis results verify the design principle of the parallel architecture for the hybrid motion system.

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 score0.836
Threshold uncertainty score0.102

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 head0.184
Teacher spread0.176 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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