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Compact-design, Modeling and Simulation of the Internal Permanent Magnet of Magnetically Guided Capsule Endoscope

2022· article· en· W4212938185 on OpenAlexaff
Guanqing Zhang, Ruixue Yin, Kemin Wang, Wenjun Zhang

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Bleeding Diagnosis and Treatment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMiniaturizationMagnetPropulsionActuatorKinematicsFinite element methodComputer scienceMechanical engineeringSimulationEngineeringAerospace engineeringPhysicsElectrical engineeringArtificial intelligenceStructural engineering

Abstract

fetched live from OpenAlex

Abstract Miniaturization is a challenge for the magnetically guided capsule endoscope (MGCE), especially considering the multifunction of MGCE. This paper proposes a compact-design of internal permanent magnet (IPM), which contributes to the miniaturization of MGCE. The IPM is a complex of actuator and shell of the MGCE, which frees the occupation of conventional IPM. The IPM is for the first time designed with a streamline shape for good kinematics. A magnetic propulsion model of the proposed IPM is established and a magnetic propulsion simulation based on finite element (FE) and boundary element (BE) analysis for guaranteeing steady locomotion and navigation is conducted. The simulation is validated by comparison of the analytical model. At last, the locomotion modes and navigation of IPM are simulated based on the justified simulation. In conclusion, the compact-design of IPM can guarantee the locomotion and navigation of MGCE, and increase the available inner space of MGCE.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.290
Teacher spread0.235 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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