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Record W4247037951 · doi:10.32920/ryerson.14656161.v1

Three phase digital earth leakage detection

2021· preprint· en· W4247037951 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)Electrical engineeringComputer scienceElectric shockEmbedded systemEngineering

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.610
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.012
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 teacher head, not a consensus.

Study designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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