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Record W2774438824 · doi:10.1109/tmtt.2017.2771444

New Higher Order Method of Moments for Accurate Inductance Extraction in Transmission Lines of Complex Cross Sections

2017· article· en· W2774438824 on OpenAlexafffund
Farhad Sheikh Hosseini Lori, Mohammad Shakander Hosen, Anton Menshov, Mohammad Shafieipour, Vladimir Okhmatovski

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsResearch ManitobaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInductanceMethod of moments (probability theory)DiscretizationImpedance parametersMathematical analysisElectric power transmissionBasis functionTransmission lineMathematicsFinite element methodConductorElectrical impedanceGeometryPhysicsVoltageEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

A new higher order (HO) method of moments is proposed for high accuracy extraction of the resistance and inductance matrices in the multiconductor transmission lines (MTLs) of complex cross sections. The computational framework is based on the numerical solution of a surface-volume-surface electric field integral equation of magnetostatics. In order to achieve an exponentially efficient reduction in the error of the solution, HO geometrical representation of the conductor cross sections is accompanied with the discretization of the unknown field quantities on the conductor boundaries and cross sections with 1-D and 2-D HO polynomial basis functions, respectively. The methodology allows for extraction of the network parameters in broad ranges of frequencies for which resistive and inductive contributions to the impedance matrix vary within a wide dynamic range. Comparison of numerically computed currents to the currents obtained analytically for canonical transmission line configurations is performed. Solutions of the extraction problem for MTLs with complex cross sections are compared against the finite-element method solutions to demonstrate the efficiency of the proposed methodology.

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 score0.736
Threshold uncertainty score0.455

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 head0.028
GPT teacher head0.361
Teacher spread0.334 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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