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Record W1482091275 · doi:10.1109/ccece.2015.7129355

A flexible laboratory platform for multi-disciplinary electrical engineering courses

2015· article· en· W1482091275 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMechatronicsSystems engineeringMultidisciplinary approachComputer scienceElectric powerElectronicsEngineeringElectrical engineeringSoftware engineeringPower (physics)

Abstract

fetched live from OpenAlex

As power and mechatronics systems become increasingly more complex, graduating engineers are required to have a deeper understanding of various electrical engineering topics such as: electronics, controls, electro-magnetism, electrical power, electric machines, communications, software, data acquisition and signal processing. To train electrical engineers of the future, conversant in these multi-disciplinary and often diverse fields, it is necessary to have a flexible laboratory platform that supports multidisciplinary areas of electrical engineering. This research project focuses on the practical hands-on integration of different multi-disciplinary fields using a unique development platform. Furthermore, the project enabled to validate, test and extend its operating limits in order to improve the quality of the product. In addition, a set of laboratory manuals to complement this platform is being developed. For future research enhancements, the platform is now being utilized for incorporating renewable energy capabilities.

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: none
Teacher disagreement score0.608
Threshold uncertainty score0.955

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 head0.043
GPT teacher head0.294
Teacher spread0.251 · 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

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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