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Record W2143924614 · doi:10.1109/compsac.2009.205

Design of MIDA, a Web-Based Diagnostic Application for Hydroelectric Generators

2009· article· en· W2143924614 on OpenAlexaffabout
Luc Vouligny, C. Hudon, Duc Ngoc Nguyen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsComputer scienceSoftware engineeringSystems engineeringWeb applicationHydroelectricityGenerator (circuit theory)Process (computing)Object-oriented programmingComponent (thermodynamics)EngineeringProgramming languageWorld Wide Web

Abstract

fetched live from OpenAlex

Implementing a cost-effective general maintenance program for generators is of utmost importance at hydro-Quebec. This paper presents the design of MIDA (integrated generator diagnostic methodology), a Web-based application for diagnosing hydroelectric generators. MIDA allows hydro-Quebec maintenance personnel to better establish maintenance priorities based on analysis and trending of data from several diagnostic instruments. This Web-based application represents just the software component of the MIDA project, an eightyear, multi-million dollar research and development project that involved 20 people. The project aimed to develop a simple and rational way to combine measured data from different diagnostic tools in order to determine the general state of each generator. Following the introduction, this paper provides an overview of the MIDA application and its architecture. It then presents the development process, including the evolutionary prototyping methodology which is very well suited to this type of research and development project and the programming language used: MDI, an object-oriented knowledge-based prototyping tool.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score0.243

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations4
Published2009
Admission routes2
Has abstractyes

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