MétaCan
Menu
Back to cohort
Record W4291136122 · doi:10.23977/jaip.2022.050116

Overview of Sensorless Zero-Low Speed Range Control Technology based on High-Frequency Signal Injection for SPMSM

2022· article· en· W4291136122 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Artificial Intelligence Practice · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Algorithms and Applications
Canadian institutionsnot available
FundersChina Postdoctoral Science Foundation
KeywordsRotor (electric)Position (finance)Control theory (sociology)Zero (linguistics)SIGNAL (programming language)Range (aeronautics)Control (management)Process (computing)Computer scienceControl engineeringPermanent magnet synchronous motorEngineeringArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

For the sensorless position control technology of surface permanent magnet synchronous motor (SPMSM) under the operation modes of zero and low speed, this paper introduced several typical control algorithms detecting rotor position of PMSM, and analyzed the working principle and the rotor position estimation deviation control process of these types of algorithms, as well as their advantages and disadvantages in detail. Finally, the applicability of these algorithms in SPMSM at present were further compared and summarized, so as to achieve better control effect.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.318
Teacher spread0.280 · 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