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Record W2785396966 · doi:10.1109/tasc.2018.2793200

A Five-Phase Doubly Fed Doubly Salient HTS Linear Motor for Vertical Transportation

2018· article· en· W2785396966 on OpenAlexaff
Jianqiang Li, Wenlong Li, Rui Li, Zhong Ming

Bibliographic record

VenueIEEE Transactions on Applied Superconductivity · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
FundersNational Natural Science Foundation of China
KeywordsArmature (electrical engineering)StatorElectromagnetic coilMagnetMaterials scienceLaminationSalientField coilHigh-temperature superconductivitySuperconductivityComputer scienceMechanical engineeringElectrical engineeringLayer (electronics)PhysicsCondensed matter physicsComposite materialEngineering

Abstract

fetched live from OpenAlex

In this paper, a doubly fed doubly salient high temperature superconducting linear motor (DFDS-HTSLM) is proposed for vertical transportation. The proposed DFDS-HTSLM adopts a five-phase doubly salient configuration. The stator consists of iron lamination only with no windings or permanent magnets. The mover houses both the copper armature winding and the HTS field winding in its outer layer and inner layer, respectively. By using the HTS field winding, the air-gap flux can be flexibly adjusted by controlling the DC current flowing in it. In addition, by using five-phase configuration, the proposed motor can realize the fault-tolerance operation easily, hence improving the reliability.

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 score0.002
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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