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Record W1980544291 · doi:10.1049/el.2013.3886

Auto‐calibration of Hall effect sensors for home energy consumption monitoring

2014· article· en· W1980544291 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

VenueElectronics Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsCalibrationHall effect sensorEnergy consumptionConsumption (sociology)Remote sensingHall effectEnergy (signal processing)Environmental scienceElectrical engineeringComputer scienceEngineeringGeographySociologyStatisticsMathematics

Abstract

fetched live from OpenAlex

A technique to improve the accuracy of an electrical energy consumption monitoring system is proposed. This system is based on a network of Hall effect wireless sensors attached to the wire at the output of every circuit breaker in the electrical distribution panel. The readings provided by the Hall effect sensors show significant gain errors due to their sensitivity to the distance between the sensor and the monitored wire. To mitigate these gain errors and increase the system accuracy, the addition of a single high‐precision current transformer sensor at the main electrical input is proposed, measuring the total current. This signal is used as the reference signal in a least‐mean square algorithm to compensate for the unknown gains of the Hall effect sensors. Experimental results using three prototype Hall effect sensors show that the proposed solution converges within 1.7 min, reducing the average current measurement error from 2.54 to 0.46 A rms .

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.577

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 head0.005
GPT teacher head0.202
Teacher spread0.198 · 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