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Automatización de un generador de luz ultravioleta, controlada con un HMI, de longitud de onda variable

2020· article· es· W3045668688 on OpenAlexvenueno aff
Jorge Luis Yaulema Castañeda, Paulina Fernanda Bolaños Logroño, Héctor Méndez-Gómez, V. Orquera

Bibliographic record

VenueConcienciaDigital · 2020
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhysicsComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

En la presente investigación, el objetivo principal fue realizar un control de longitudes de onda de luz UV, en un prototipo, analizando su óptica, cuyo referencial es el espectrofotómetro Perkin Elmer 781, usado en análisis de descontaminación de aguas surfactantes y de fotodegradación, para lo cual se ha rediseñado el generador, determinando el rango de frecuencias, disminuyendo el impacto ambiental, mediante la implementación de HMI, cuya metodología aplicada, para efectuar el proceso de automatización cuyo enfoque aplicativo se determina a través de la foto de graduación y descontaminación del agua, presentando un rediseño para la generación de longitudes de onda UV dentro de la parte eléctrica y electrónica a través de la interpolación de Newton disminuyendo el error al (0.05%) mínimos cuadrados de (3.32%) optimizando el control de motores con una conexión en HMI, mediante la cual se visualiza la longitud de onda generada facilitando la navegación y el control de los motores otorgando al operario la interacción directa con el equipo obteniendo como resultado a la mejora del sistema original variando los sensores y actuadores para obtener longitudes de onda de (500 nm) a (700 nm) el cual permite desarrollar trabajos en la descontaminación de aguas y efectividad del sistema.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.196
Teacher spread0.178 · 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.

Study designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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