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Real-Time Wavelet-Based Data Compression in Light of IEC61850 Communication Protocol

2022· article· en· W4308091080 on OpenAlexafffund
Rizwan Ayub, Walid G. Morsi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceWaveletData compressionReduction (mathematics)Context (archaeology)Compression (physics)Compression ratioWavelet transformProtocol (science)Real-time computingArtificial intelligenceEngineeringMathematics

Abstract

fetched live from OpenAlex

This study addresses the problem of data compression in the context of IEC61850 communication protocol used in smart grids. The study presents a new approach based on wavelet compression that uses the predictor importance to identify the wavelet detail level that holds the salient features of the disturbance signal and then applies a hybrid threshold that includes both hard and soft thresholds to the wavelet details. The effectiveness of the proposed approach has been tested and evaluated and the results have shown that the proposed approach was very effective in increasing the compression ratio leading to reduction in the file sizes. Furthermore, the results of testing the proposed approach using a real-time digital simulator in the context of IEC61850 showed that the implementation of the proposed approach not only led to a significant reduction in the number of messages but also leads to a reduction in their sizes while maintaining a high-quality signal reconstruction following the compression process.

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)0.002
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: Methods
Teacher disagreement score0.885
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0030.002
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 head0.049
GPT teacher head0.345
Teacher spread0.296 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2022
Admission routes2
Has abstractyes

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