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Record W1593740443 · doi:10.1109/naps.2005.1560560

A V-I slope-based method for flicker source detection

2005· article· en· W1593740443 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
TopicPower Quality and Harmonics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFlickerLuminanceComputer scienceTroubleshootingPower qualityFlicker noiseSchematicImpressionComputer visionArtificial intelligenceVoltageElectronic engineeringEngineeringTelecommunicationsElectrical engineeringBandwidth (computing)Computer graphics (images)

Abstract

fetched live from OpenAlex

Flicker can be defined as the impression of unsteadiness of visual sensation induced by a light stimulus whose luminance or spectral distribution fluctuates with time. The flicker source detection is an important step in the power quality evaluation process as only after the information about the disturbance location is available, the diagnosis and troubleshooting can be accordingly carried out. This paper is concerned about the flicker source detection subject. The problem is described, the method is proposed and shown to be comprehensive enough to diagnose stationary and random flicker. Analytical proof, a simulation and a practical case assembled in laboratory are presented to show the validity of the method. Its principle is based in the relationship between voltage and current rms values, and it is possible to show that the behavior of this relationship permits one to draw conclusions about the flicker source.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score0.268

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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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

Citations31
Published2005
Admission routes1
Has abstractyes

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