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Record W4312800313 · doi:10.23977/jemm.2022.070405

Design Optimization of an Electric Positioning Mechanism

2022· article· en· W4312800313 on OpenAlexvenueno aff
Xiaowen Zhao, Tongfei Li, Xiaogang Fang, Peipei Liang, Zhikuan Yang, Jiguang Liu, Youwen Yang

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2022
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsMechanism (biology)ThrustActuatorFrame (networking)Power (physics)ElectromagnetEngineeringPosition (finance)Mechanical engineeringAutomotive engineeringComputer scienceElectrical engineeringMagnetPhysics

Abstract

fetched live from OpenAlex

The positioning mechanism of conventional transfer vehicles commonly employs a hydraulic or pneumatic driving mode, which requires control valves, actuators and pipe fittings, causing problems such as complex composition, high cost and inconvenient hauling of pipes and accessories when installed on transfer vehicles. In order to avoid the above difficulties, an electrical localization mechanism using electromagnets as power units has been designed. First, it explains its working principles and composition. Moreover, the implementation scheme and the structure of the electromagnetic thrust are then detailed, including the frame, the telescopic components and the power equipment. Next, based on the force analysis of the locator, we introduce the calculation of the electromagnetic thrust to the locator and design a reasonable angle between the locator and the pin. Finally, by switching the electromagnet on and off, the mechanism drives the positioning plate to reciprocate, thus enabling multi-position accurate positioning and rapid un-positioning of the transfer vehicle, contributing to the compact, low-cost and environmentally friendly maintenance of the mechanism.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.182
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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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