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Record W2801925492 · doi:10.1109/jpets.2018.2825279

Characteristic Parameter-Based Detuned C-Type Filter Design

2018· article· en· W2801925492 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

VenueIEEE Power and Energy Technology Systems Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of AlbertaATCO (Canada)
Fundersnot available
KeywordsFilter (signal processing)CapacitorType (biology)Filter designControl theory (sociology)Computer sciencem-derived filterMathematicsElectronic engineeringEngineeringArtificial intelligenceVoltageElectrical engineering

Abstract

fetched live from OpenAlex

The C-type filter has been widely used in industry to avoid the harmonic resonance caused by the shunt capacitors. This paper provides a deep and unique insight into the C-type filter design. It will be shown that the C-type filter can be simply characterized by two parameters–the tuning frequency and the R-ratio. The tuning frequency is predetermined based on the system need, thus the entire design process comes down to determine the R-ratio. The correlation between the R-ratio with the filter’s cost and performance is then developed and an optimum value of the R-ratio is solved. Compared with the conventional design methods, the proposed method with one underdetermined variable is simpler and more straightforward. Comparative studies on actual capacitor application cases are conducted to demonstrate the usefulness of the proposed design 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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score0.580

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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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