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Record W2547521209 · doi:10.1109/ccece.2016.7726662

Designing and implementing an educational intelligent motor control center (IMCC)

2016· article· en· W2547521209 on OpenAlexaff
Samuel L. Merrick, Anthony E. Paquette, Ali Palizban, Kathy Manson, Hassan Saberi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsControl (management)Intelligent controlMotor controlComputer scienceEngineering managementEngineeringControl engineeringSystems engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

High efficiency and low costs are requirements for any industrial plant. Advancements in digital technology and integrated components have made it easier to incorporate device level communications and intelligence into any design. Intelligent motor control is not a new concept; intelligent devices are mature technologies accepted throughout industry with well-documented benefits. This paper outlines a less well-documented area: teaching motor control and protection theories through the interactions of a fully integrated, standards-compliant, intelligent motor control center. As well this paper provides a framework for students to learn about the technical requirements and design specifications for building a state-of-the-art Intelligent Motor Control Center (IMCC).

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 score0.493
Threshold uncertainty score0.569

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)0.0010.000

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 head0.008
GPT teacher head0.238
Teacher spread0.229 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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