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Record W4246841468 · doi:10.1109/mwsym.2011.5973517

Longitudinal partitioning based waveform relaxation algorithm for transient analysis of long delay transmission lines

2011· article· en· W4246841468 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

Venue2011 IEEE MTT-S International Microwave Symposium · 2011
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsWestern University
Fundersnot available
KeywordsWaveformLossless compressionCascadeAlgorithmRelaxation (psychology)Computer scienceTransmission lineElectric power transmissionLine (geometry)Transient (computer programming)Transmission (telecommunications)Electronic engineeringTopology (electrical circuits)Data compressionMathematicsTelecommunicationsEngineeringElectrical engineeringGeometry

Abstract

fetched live from OpenAlex

Summary form only given, as follows. In this paper a waveform relaxation algorithm based on longitudinal partitioning is presented. This work models transmission lines as cascade of lumped circuit elements and lossless line segments, where the lossless line segments are modeled using the method of characteristics. This allows the transmission line to be divided into smaller, weakly coupled subcircuits, enabling the algorithm to converge more efficiently than existing relaxation algorithms based on longitudinal partitioning.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.043
GPT teacher head0.274
Teacher spread0.232 · 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