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Record W4242049335 · doi:10.32920/ryerson.14656161

Three phase digital earth leakage detection

2021· preprint· en· W4242049335 on OpenAlexaff
Liaqat Ali

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLeakage (economics)MicrocontrollerReset (finance)Computer scienceElectrical engineeringElectric shockEmbedded systemAutomotive engineeringEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

In any electrical system, protection is the most important requirement to secure both human lives and appliances from any damage. The THREE PHASE EARTH LEAKAGE DETECTION (TDELD), is a design which could be implemented in three phase electrical environment to provide protection to user as well as equipments against any earth leakage fault. Being a microcontroller based solution, it provides ease and luxury at the user end with the help of its auto reset and display features. This research will attempt to improve the existing ELCB design using PIC microcontroller to automatically switch back system to its normal mode when the TDELD tripped during any electric shock or temporary earth leakage while in permanent leakage fault, it provide input control to bring back the system to its normal operation. The results of this research after doing several tests have shown that the average sensitivity value for TDELD against leakage current is better than what could be found in a conventional ELCB.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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