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Record W2117315739 · doi:10.1109/ccece.2006.277361

An Improved Signal Detection Algorithm for TWACS based Power Line Signals

2006· article· en· W2117315739 on OpenAlexaff
Jacek Kliber, Wencong Wang, Wilsun Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceReliability (semiconductor)SIGNAL (programming language)Digital signal processingPower (physics)Field (mathematics)Detection theoryElectronic engineeringProcess (computing)VoltageSignal processingAlgorithmEngineeringTelecommunicationsElectrical engineeringComputer hardwareMathematics

Abstract

fetched live from OpenAlex

TWACS (two-way automatic communication system) is a powerful technology to send signals on power lines. The authors have developed an innovative new approach to detecting TWACS signals while applying the technology to check the continuity of a distribution feeder and detect the formation of islands in power distribution systems. This paper presents a new algorithm to detect TWACS signals which increases the reliability and accuracy of the detection process while reducing the cost of signal detection and transmission. It will take advantage of a DSP capability and has been tested using extensive field measured signals. The results show signals as low as 1.2% of the supply voltage can be detected as compared to 3% with the current analog method. Thus the cost and negative impact of the signal on power quality can be reduced. This paper presents detailed research results on the proposed algorithm and extensive field data tests have shown that the proposed algorithm is much more reliable than the traditional method

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.453

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 head0.008
GPT teacher head0.233
Teacher spread0.224 · 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 designBench or experimental
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

Citations7
Published2006
Admission routes1
Has abstractyes

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