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Record W1981570976 · doi:10.1088/0022-3727/40/11/004

Motion control of a large gap magnetic suspension system for microrobotic manipulation

2007· article· en· W1981570976 on OpenAlexafffund
David Craig, Mir Behrad Khamesee

Bibliographic record

VenueJournal of Physics D Applied Physics · 2007
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSuspension (topology)Motion (physics)Electromagnetic suspensionMotion controlComputer sciencePhysicsComputer visionArtificial intelligenceMathematicsMagnetRobot

Abstract

fetched live from OpenAlex

Magnetic suspension systems have shown a great deal of promise in the field of microrobotics. This paper discusses the performance of a new large gap magnetic suspension system developed by the researchers. The magnetic drive unit consists of six electromagnets attached to a soft iron pole piece and yoke. Levitation of an 11.19 g microrobot prototype is demonstrated for step, ramp and periodic input trajectories using PID control. The working envelope of the microrobot is 30 × 22 × 20 mm3, with an RMS error on the order of 18 µm in the vertical direction and 8 µm in the horizontal direction. It is demonstrated that the levitated microrobot is able to track the desired trajectory precisely and that the system has potential application for micromanipulation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.700
Threshold uncertainty score0.550

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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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 designBench or experimental
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

Citations21
Published2007
Admission routes2
Has abstractyes

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