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Record W4377138840 · doi:10.20965/jaciii.2023.p0490

Optimization Design Method of Spherical Magnetic Field Generation Coil Based on Differential Evolution Algorithm

2023· article· en· W4377138840 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Advanced Computational Intelligence and Intelligent Informatics · 2023
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsUniversity of British Columbia
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsHelmholtz coilElectromagnetic coilMagnetic fieldMagnetometerComputer scienceSearch coilEarth's magnetic fieldCoaxialAlgorithmAcousticsNuclear magnetic resonancePhysicsMagnetic flux

Abstract

fetched live from OpenAlex

In a coil magnetometer, the size and uniformity of the bias magnetic field generated by the Helmholtz coil directly determine the accuracy of the solution of the geomagnetic direction. The design of traditional spherical coils relies heavily on the manual experience or mathematical derivation, making it difficult to obtain optimal parameters or requiring larger spherical coils. To address the problem, first, a coaxial symmetrical spherical coil model that improves space utilization was established. Second, an optimal design method for the spherical magnetic field generation coil based on a differential evolution algorithm was proposed. Third, the optimal bias magnetic field was obtained without increasing the volume of the coil. The verification results showed that the magnetic non-uniformity and magnetic gradient of the bias field generated by the optimized coil were reduced by 63.2% and 82.8%, respectively.

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.

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: Methods
Teacher disagreement score0.370
Threshold uncertainty score0.520

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 head0.024
GPT teacher head0.275
Teacher spread0.252 · 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