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Record W2055512332 · doi:10.1109/tmech.2014.2356295

Magnetic Characterization of Actuators for an Unmanned Aerial Vehicle

2014· article· en· W2055512332 on OpenAlexafffund
M. S. Huq, Robert Forrester, Mojtaba Ahmadi, Paul Straznicky

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2014
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCharacterization (materials science)ActuatorAerospace engineeringComputer scienceAeronauticsRemote sensingEngineeringArtificial intelligenceNanotechnologyMaterials scienceGeology

Abstract

fetched live from OpenAlex

A dipole equivalent modeling approach and experimental results, for relatively small magnetic sources such as servomotors, are presented. Two modeling schemes, viz. the dual permanent magnetic dipole (DuPMaD) and the permanent magnetic dipole (PMaD), are proposed. PMaD assumes only one equivalent magnetic dipole within the physical dimension of the source component, whereas DuPMaD employs a second magnetic dipole with a view to characterize the field more accurately. The equivalent dipole moment vectors and their positions are estimated via fitting experimental data collected on three orthogonal planes around the specimen. Results show good agreements between the model outputs and the experimental data, with both the DuPMaD and the PMaD schemes performing closely in terms of modeling accuracy. Such models may be used to estimate and minimize magnetic interference in various applications such as in geomagnetic surveying using unmanned aerial vehicles considered in this paper.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.210
Teacher spread0.201 · 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 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

Citations9
Published2014
Admission routes2
Has abstractyes

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Same venueIEEE/ASME Transactions on MechatronicsSame topicInertial Sensor and NavigationFrench-language works237,207