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Record W2125378754 · doi:10.1109/cdc.2009.5400472

Configuration design and performance analysis of a multidimensional acceleration sensor based on 3RRPRR decoupling parallel mechanism

2009· article· en· W2125378754 on OpenAlexaff
Dan Zhang, Zhen Gao, Bo Song, Yunjian Ge

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsDecoupling (probability)Computer scienceAccelerationStiffnessMechanism (biology)Finite element methodTorqueRedundancy (engineering)SimulationStructural engineeringEngineeringControl engineeringPhysics

Abstract

fetched live from OpenAlex

There has been a growing interest of parallel mechanisms which offer a high degree of precision, stiffness and dexterity to name just a few of their benefits as opposed to their traditional serial counterparts. For the applications of parallel mechanisms in the field of sensor, previous work was focused on investigating the 6 degree-of-freedom force/torque sensors based on common Gough-Stewart platform. In this paper, a novel design of a multidimensional acceleration sensor is proposed based on 3RRPRR fully decoupling parallel elastic mechanism for kinetic information acquisition, specifically for measuring human motions. The translational elements of three perpendicular legs are served as elastic body which is manufactured by aluminum alloy. The detailed design of the structure is first introduced, followed by the mathematics modeling and performance evaluation. Finally, the finite-element analysis of resultant stress, strain and deformations is conducted based upon different input conditions to show the reliability of the proposed structure.

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 score0.497
Threshold uncertainty score0.386

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 head0.017
GPT teacher head0.219
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

Citations7
Published2009
Admission routes1
Has abstractyes

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