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Record W2172089762 · doi:10.1109/ccece.2005.1557253

Aquantitative comparison of different mother wavelets for characterizing transients in power systems

2006· article· en· W2172089762 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
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsManitoba HydroUniversity of Manitoba
Fundersnot available
KeywordsWaveletComputer scienceWavelet transformSignal processingPower (physics)Electronic engineeringElectric power systemTime–frequency analysisPattern recognition (psychology)Artificial intelligenceEngineeringTelecommunicationsDigital signal processingPhysics

Abstract

fetched live from OpenAlex

This paper presents a study of wavelet analysis of transients in power systems. Short-term and non-stationary nature of transients necessitates the use of advanced signal processing techniques in order to detect, characterize and classify them. The paper examines the fundamental properties of a number of mother wavelets, and provides guidelines for the selection of suitable wavelets for the purpose of characterization and extraction of features of transients in power systems. As an indispensable part of such a study, the paper also presents the results of wavelet analysis of typical types of transients obtained using simulation

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: none
Teacher disagreement score0.668
Threshold uncertainty score0.327

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 head0.035
GPT teacher head0.318
Teacher spread0.283 · 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

Citations20
Published2006
Admission routes1
Has abstractyes

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