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Record W2156694536 · doi:10.1109/ccece.1998.682717

An intelligent support system for results evaluation of electric power system transients analysis

2002· article· en· W2156694536 on OpenAlexaff
A.I. Ibrahim, D. Lindenmeyer, T. Niimura, H.W. Dommel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEmtpFuzzy logicElectric power systemTransient (computer programming)Expert systemComputer scienceControl engineeringKnowledge baseEngineeringPower (physics)Artificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

The Electromagnetic Transients Program (EMTP) is a general purpose computer program for simulating fast transient effects in electric power systems. The application of the EMTP is not easy, because it requires highly specialized expertise. This paper introduces an intelligent system to support EMTP simulation. This intelligent system works in three steps. Initially, the support system selects a base case, from a case data base to be modified to meet the user's requirement. Then, the expert system checks the syntax and validity of the new case data. Finally, the EMTP solution is checked whether it is reasonable. The results evaluation of the EMTP solution is presented in more detail using fuzzy logic. The EMTP expert knowledge is expressed by fuzzy sets in order to check the time domain solution of the EMTP. The simulation results show that the fuzzy logic is suitable to evaluate the results of EMTP.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.004

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 head0.026
GPT teacher head0.261
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations5
Published2002
Admission routes1
Has abstractyes

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